web analytics


  • Simplify and centralize network security management with Azure Firewall Manager
    by Suren Jamiyanaa on June 15, 2022 at 10:00 am

    We are excited to share that Web Application Firewall (WAF) policy management in Azure Firewall Manager is now generally available.

  • Discover how you can innovate anywhere with Azure Arc
    by Kathleen Mitford on June 15, 2022 at 9:00 am

    Welcome to Azure Hybrid, Multicloud, and Edge Day—please join us for the digital event. Today, we’re sharing how Azure Arc extends Azure platform capabilities to datacenters, edge, and multicloud environments through an impactful, 90-minute lineup of keynotes, breakouts, and technical sessions available live and on demand.

  • Azure powers rapid deployment of private 4G and 5G networks
    by Shriraj Gaglani on June 14, 2022 at 9:00 am

    As the cloud continues to expand into a ubiquitous and highly distributed fabric, a new breed of application is emerging: Modern Connected Applications. We define these new offerings as network-intelligent applications at the edge, powered by 5G, and enabled by programmable interfaces that give developer access to network resources.

  • MLOps Blog Series Part 1: The art of testing machine learning systems using MLOps
    by Takuto Higuchi on June 14, 2022 at 8:00 am

    Testing is an important exercise in the life cycle of developing a machine learning system to assure high-quality operations. In this blog, we will look at testing machine learning systems from a Machine Learning Operations (MLOps) perspective and learn about good case practices and a testing framework that you can use to build robust, scalable, and secure machine learning systems.

  • Supporting openEHR with Azure Health Data Services
    by Matjaz Ladava on June 10, 2022 at 8:00 am

    This blog is part of a series in collaboration with our partners and customers leveraging the newly announced Azure Health Data Services. Azure Health Data Services, a platform as a service (PaaS) offering designed exclusively to support Protected Health Information (PHI) in the cloud, is a new way of working with unified data—providing care teams with a platform to support both transactional and analytical workloads from the same data store and enabling cloud computing to transform how we develop and deliver AI across the healthcare ecosystem.

  • Achieve seamless observability with Dynatrace for Azure
    by Prachi Bora on June 9, 2022 at 10:00 am

    Microsoft Azure enables customers to host their apps on the globally trusted cloud platform and use the services of their choice by closely partnering with popular SaaS offerings. Dynatrace is one such partner that provides deep cloud observability, advanced AIOps, and continuous runtime application security capabilities on Azure.

  • Learn what’s new in Azure Firewall
    by Eliran Azulai on June 9, 2022 at 9:00 am

    We continue to be amazed by the adoption, interest, positive feedback, and the breadth of use cases customers are finding for our service. Today, we are happy to share several key Azure Firewall capabilities as well as an update on recent important releases into general availability and preview.

  • Find the clarity and guidance you need to realize cloud value
    by Evelyn Padrino on June 9, 2022 at 8:00 am

    A modernized cloud workload offers significant benefits—including cost savings, optimized security and management, and opportunities for ongoing innovation. But the process of migrating and modernizing workloads can be challenging.

  • Improve outbound connectivity with Azure Virtual Network NAT
    by Aimee Littleton on June 8, 2022 at 10:00 am

    For many customers, making outbound connections to the internet from their virtual networks is a fundamental requirement of their Azure solution architectures. Luckily, Azure has just the solution for ensuring highly available and secure outbound connectivity to the internet: Virtual Network Network Address Translation. Virtual Network NAT, also known as NAT gateway, is a fully managed and highly resilient service that is easy to scale and specifically designed to handle large-scale and variable workloads.

  • Top 5 reasons to attend Azure Hybrid, Multicloud, and Edge Day
    by Nate Waters on June 8, 2022 at 9:00 am

    Infrastructure and app development is becoming more complex as organizations span a combination of on-premises, cloud, and edge environments.

      • DataEd Webinar: Data Preparation Fundamentals
        by Christiana Nicole on June 16, 2022 at 9:00 pm

          To view just the slides from this presentation, click HERE>> This webinar is sponsored by: About the Webinar Whether you call it data munging, data cleansing, or data wrangling, everyone agrees that data preparation activities account for 80% of analysts’ time, leaving only 20% for analysis. Shifting this work to more specialized talent represents a The post DataEd Webinar: Data Preparation Fundamentals appeared first on DATAVERSITY.

      • DataEd Slides: Data Preparation Fundamentals
        by Christiana Nicole on June 16, 2022 at 9:00 pm

        To view the on-demand recording from this presentation, click HERE>> This webinar is sponsored by: About the Webinar Whether you call it data munging, data cleansing, or data wrangling, everyone agrees that data preparation activities account for 80% of analysts’ time, leaving only 20% for analysis. Shifting this work to more specialized talent represents a major The post DataEd Slides: Data Preparation Fundamentals appeared first on DATAVERSITY.

      • The Future of Database Management Systems
        by Siji Roy on June 16, 2022 at 7:35 am

        As the amount of data generated globally grows exponentially, data-driven businesses are increasingly turning to more innovative database management systems to store, manage, and process all that data. This article provides an overview of database management, how it has evolved – especially during the COVID-19 pandemic – and the future of database management systems.  What Is The post The Future of Database Management Systems appeared first on DATAVERSITY.

      • Putting Theory into Practice: What’s Next for AutoML? 
        by Eswar Nagireddy on June 16, 2022 at 7:25 am

        Automated machine learning (AutoML) is no longer a new topic, but it’s still a growing trend in terms of the impact it’s having on businesses and employees. The main idea of AutoML is that it can help democratize data and data processes, providing people from any level or department of a business with the tools and platforms they need to The post Putting Theory into Practice: What’s Next for AutoML?  appeared first on DATAVERSITY.

      • Data Leadership: The Key to Data Value
        by Amber Lee Dennis on June 15, 2022 at 7:35 am

        Data and business have become inseparable, so if your business isn’t using data effectively, you’re in trouble. Yet using it effectively can be a struggle. Not knowing how to find the right data, not trusting the available data, and not having confidence in the tools and people providing that data can lead people to conclude The post Data Leadership: The Key to Data Value appeared first on DATAVERSITY.

      • How an AI-Driven Approach to Analytics Is Changing the Geospatial Industry
        by Toby Kraft on June 15, 2022 at 7:25 am

        While newer to the geospatial data industry, AI enables professionals to complete their jobs more efficiently and accurately. Originally, analysts required weeks to evaluate and parse massive amounts of data. Surveyors had to visit remote areas in person. Now, however, the use of AI has exponentially decreased how long it takes these professionals to do The post How an AI-Driven Approach to Analytics Is Changing the Geospatial Industry appeared first on DATAVERSITY.

      • Developing a Data Analytics Strategy
        by Paramita (Guha) Ghosh on June 14, 2022 at 7:35 am

        While data-driven digital platforms and the recent pandemic have opened new and novel opportunities for businesses to go beyond geographical borders and compete in truly global business environments, an average business faces tremendous pressure to demonstrate the value of their operations. With the rising importance of data as the new oil of a global business The post Developing a Data Analytics Strategy appeared first on DATAVERSITY.

      • Data Governance at the Edge of the Cloud
        by Robert Baker on June 14, 2022 at 7:25 am

        We are living in turbulent times. Online security has always been an area of concern; however, with recent global events, the world we now live in has become increasingly cloud-centric. With that, I’ve long believed that for most large cloud platform providers offering managed services, such as document editing and storage, email services and calendar The post Data Governance at the Edge of the Cloud appeared first on DATAVERSITY.

      • ADV Webinar: Is Our Information Management Mature?  
        by Christiana Nicole on June 13, 2022 at 9:00 pm

          To view just the slides from this presentation, click HERE>> This webinar is sponsored by: About the Webinar Maturity frameworks have varying levels of information management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The post ADV Webinar: Is Our Information Management Mature?   appeared first on DATAVERSITY.

      • ADV Slides: Is Our Information Management Mature?  
        by Christiana Nicole on June 13, 2022 at 9:00 pm

        To view just the slides from this presentation, click HERE>> This webinar is sponsored by: About the Webinar Maturity frameworks have varying levels of information management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The The post ADV Slides: Is Our Information Management Mature?   appeared first on DATAVERSITY.

      • Best Practices for Managing a Data Fabric
        by Tejasvi Addagada on June 13, 2022 at 7:35 am

        When data is not viable for integration across systems and processes, business users will seldom have the right coverage of data. If people lack knowledge about data and its importance logically, it often becomes a challenge, which leads to less impactful decisions. A data fabric is an architectural capability that can give organizations a “data The post Best Practices for Managing a Data Fabric appeared first on DATAVERSITY.

      • 4 Key Approaches to Using Data Analytics for Social Determinants of Health
        by Amy Brown on June 13, 2022 at 7:25 am

        Every day customers are calling health care call centers, providing visibility into their daily reality – without even being asked. Whether or not you’re listening, customers are sharing details about the social factors impacting their health care decisions within every interaction. These factors, both positive and negative, are called social determinants of health (SDOH) and include: Access to The post 4 Key Approaches to Using Data Analytics for Social Determinants of Health appeared first on DATAVERSITY.

      • AI Governance as Part of the Data Science Lifecycle
        by Chris Luiz on June 10, 2022 at 7:35 am

        AI is everywhere. It is embedded in virtually every new product, from toasters to shoes and beyond. Gone are the days when we found AI only in future-forward software and tech products. AI is being leveraged far beyond the big tech companies. The AI we interact with today is being developed by teams in widely varied companies and The post AI Governance as Part of the Data Science Lifecycle appeared first on DATAVERSITY.

      • Five Ways Multi-Cloud Is Accelerating Medical Science
        by Kim Read on June 10, 2022 at 7:25 am

        Research scientists and health care professionals are taking part in a new era in medicine. More data is being collected and analyzed than ever before in both clinical and research settings. But as pharmaceutical and health care companies begin to apply AI and machine learning to these, they need considerable computing power (“compute”) and storage. Often, they The post Five Ways Multi-Cloud Is Accelerating Medical Science appeared first on DATAVERSITY.

      • Webinar: Rethinking Trust in Data
        by Christiana Nicole on June 9, 2022 at 5:09 pm

          To view just the slides from this presentation, click HERE>> This webinar is sponsored by: About the Webinar The explosive growth of data and the value it creates calls on data professionals to level up their programs to build, demonstrate, and maintain trust. The days of fine print, pre-ticked boxes, and data hoarding are gone The post Webinar: Rethinking Trust in Data appeared first on DATAVERSITY.

            • DBMS Market Transformation 2021: On-Premises DBMS Revenue Grows
              by Merv Adrian on June 16, 2022 at 5:20 am

              On-premises DBMS revenue is alive and well. In previous posts in this series I've discussed the DBMS market's overall extraordinary 22.3% ($14,5B) growth in 2021, Amazon's dominance of nonrelational DBMS revenue overshadowing the $2.3B contribution of nonrelational pureplays, and the impact of open source-based offerings. This time, I'd like to dispel a common misperception. On-premises DBMS revenue is still growing for some vendors, and its growth is good by historical standards. But concerns loom... In 2021, on-premises DBMS revenue grew by $2.2B. Between 2011 and 2016, overall annual market growth was similar to that number. In percentage terms the market grew in mid-single digit percentages early in the decade, as it had for many years before. But between 2016 and 2017, the cloud engine kicked in, pushing overall growth into double digit percentages and starting the extraordinary rise of the past few years. On-premises revenue growth, then, continues along a stable trend, but it may be near a turning point as some vendors begin to decline there. Half of the 2021 growth was attributable to Microsoft. Another quarter was from Oracle, and the rest went to another two dozen vendors. Several grew their on-premises revenue more than $50M: Huawei, Teradata, Rocket, Intersystems and MongoDB. Most of these vendors are generating significant cloud growth now, especially Microsoft, and others are catching up at last. But some DBMS market leaders registered a drop in on-premises DBMS revenue: Cloudera, IBM, Broadcom, Software AG, Actian, HPE, MarkLogic and MicroFocus. And not one of them is among the top ten in Cloud DBMS revenue, which is a broader warning sign for all of them. If they don't get in gear in the cloud, the market will pass them by - if on-premises revenue is dropping and cloud revenue is not keeping up with the market, troubles lay ahead. Overall, the bulk of on-premises growth is relational. Microsoft and Oracle dominate the numbers, and Microsoft has no on-premises nonrelational offering. Among the pureplay nonrelational players the story is mixed: MongoDB and Datastax accounted for nearly $100M of the on-premises revenue growth, while Cloudera, HPE snd MarkLogic combined lost over $65M there. Gartner clients interested in pursuing this analysis can find the total revenue numbers in Market Share: All Software Markets, Worldwide, 2021 and the Cloud numbers in Market Share: Enterprise Public Cloud Services, Worldwide, 2021. I subtracted the latter from the former to do the on-premises analysis. And for a thorough look at trends, my colleague Robin Schumacher's piece Market Share Analysis: Database Management Systems, Worldwide, 2021 is an excellent reference.

            • New Publications: June 2022
              by Jitendra Subramanyam on June 16, 2022 at 4:22 am

              Augment Domain-Expert Decisions With Knowledge Graphs (BDO UK) The majority of decisions are difficult to augment and scale at speed because the decision-making process requires deep domain knowledge and experience. Data and analytics leaders can learn how BDO UK uses knowledge graphs to scale human expertise and monetize complex decision making.   Create a RACI Matrix for Your Data and Analytics Initiatives Data and analytics (D&A) leaders must effectively scope work and delegate responsibilities for efficiently running projects, teams or disciplines. This RACI (responsible, accountable, consulted and informed) chart can be used to define team roles, activities and responsibilities for critical goals.   Data and Analytics Benchmark Findings: How CDAOs Can Achieve Cost Recovery on D&A Investments Survey findings suggest mixed results on what helps CDAOs succeed with D&A investments. Measuring results and size of investment and scope of D&A programs and projects may not make a difference in the success of those investments; size of organization and risk appetite seem to influence results.

            • D&A Peer & Practitioner Research Publication List
              by Jitendra Subramanyam on June 15, 2022 at 12:00 pm

              Here is a list of our publications to date - it is kept current as we release more publications. [Last updated: June 15, 2022]The Team Jitendra Subramanyam  (LinkedIn) Kevin Gabbard (LinkedIn) Akash Krishnan Allison Hebert Dalia Naguib Eugene Walton JC Martel Richa Jha Sanchi Bhat Our Research Coverage Areas Business Value of Data & Analytics Data & Analytics Strategy and Planning Data & Analytics Quality and Ethics Data & Analytics Talent Business Value of Data & Analytics Augment Domain-Expert Decisions With Knowledge Graphs (BDO UK)  NEW!The majority of decisions are difficult to augment and scale at speed because the decision-making process requires deep domain knowledge and experience. Data and analytics leaders can learn how BDO UK uses knowledge graphs to scale human expertise and monetize complex decision making. Data and Analytics Benchmark Findings: How CDAOs Can Achieve Cost Recovery on D&A Investments    NEW!Survey findings suggest mixed results on what helps CDAOs succeed with D&A investments. Measuring results and size of investment and scope of D&A programs and projects may not make a difference in the success of those investments; size of organization and risk appetite seem to influence results. Ignition Guide to Building a Data and Analytics Governance ProgramTraditional approaches to designing data and analytics governance programs don’t deliver the value that modern business outcomes demand. Data and analytics leaders can use this guide to establish a governance program that aligns to business priorities and enables strategic use of data and analytics. Partnering With Startups for Nimble Innovation  Executive leaders must position their organizations to rapidly implement transformative ideas. They can learn how Stora Enso partners with startups to develop innovative AI use cases and products. How Graph Techniques Deliver Business Value Graph techniques are a key component to modern data and analytics capabilities because they span linguistic and numeric domains. Data and analytics leaders must first adopt graph technology and then promote the value it adds in answering increasingly complex business questions. 3 Steps Data and Analytics Leaders Should Take in Responding to Business Crises Data and analytics leaders can struggle to prioritize their response to business crises in a world with rising geopolitical uncertainty. Use this presentation as a starting guide to triage, share and innovate on D&A capabilities to help enterprise leaders achieve their crisis management goals. 4 Case Studies for Developing and Governing Enterprise Data Sharing Pervasive siloing of data and resistance to data sharing limit the value of data and analytics. From these four case studies, data and analytics leaders can learn how to increase access to D&A and develop risk-adjusted business cases to unlock the business value of data and analytics. Infographic: Cross-Industry Benchmark of IT Score for Data and Analytics Gartner’s IT Score for Data and Analytics benchmarks the maturity level and importance of 25 activities across six objectives that are essential for data and analytics leaders worldwide. This infographic shows how the most mature organizations behave differently from all others. Case Study: Attract and Retain Talent With Value-Driven Data Science (Asurion) Asurion has achieved a 93% talent retention rate by recruiting for and engaging on three things that drive data scientists: impact, inclusive community, and creative freedom. Data and analytics leaders can follow Asurion’s approach to improve the management of D&A talent in their own organizations. Case Study: Don’t Create Data and Analytics Stewards; Find Them (Exact Sciences) Many organizations have business SMEs who already perform what data and analytics leaders look for in a D&A steward role. Instead of proposing a new role, Exact Sciences identifies and supports the business SMEs who already do D&A stewardship to create a strong governance community across the enterprise. Tool: Scorecard to Identify Data Stewards in Business Teams Use this scorecard to identify and collaborate with potential data stewards who already exist in business teams to execute governance strategy more effectively. Case Study: Business Must Drive Data Stewardship (Ovintiv) Ovintiv empowers business stakeholders to head and drive its data stewardship. Data and analytics leaders can learn how to use community-generated ideas to develop pragmatic governance requirements that sustain business-aligned adaptive governance and willing compliance.  Data Governance to Establish Enterprisewide Data Sharing (B3)   B3 promotes enterprisewide data sharing by taking a value-first, “risk-adjusted” approach to data governance. D&A leaders can learn how to create a culture of proactive enterprisewide data sharing while balancing risk and return, resulting in cost savings and increased net new revenue. Case Study: Sustainable Data Governance Through Effective Compliance (Royal Bank of Canada) Governance is never finished; to be effective it has to be sustained by business users. Royal Bank of Canada (RBC) shows data and analytics leaders how to align governance activities with business users’ workflows by tying these activities to outcomes that business users already care greatly about. Case Study: Practical Data Literacy (Kraft Heinz)   Enterprisewide adoption of data and analytics is often inhibited by both cultural and technical issues. Data and analytics leaders can learn how Kraft Heinz develops its data literacy program to enable business users to create their own solutions. Ignition Guide to Implementing High-Performing Data Engineering Data and analytics leaders face data integration challenges. This guide can help them modernize their enterprise data engineering practices, streamline integration and increase the consumability of enterprise data. Case Study: Actionable Dashboard Creation (ABB Electrification)   Dashboards are a core way to deliver insights, but often are cluttered with irrelevant metrics, confusing visualizations and poor data quality. The D&A leader at ABB Electrification’s dashboards report strategic insights and exceptions that drive rapid executive action and improve data quality. Case Study: Driving Speed to Value with AI/ML (Kaiser Permanente) Data and analytics leaders must recognize that complex business problems do not need complex data science to generate value. Kaiser Permanente improves speed to value by developing a deep understanding of business workflows and creatively deploying D&A solutions. Case Study: Getting Value From D&A Innovation Failures (Brussels Intercommunal Transit Company):  Brussels Intercommunal Transit Company built a culture of D&A innovation and realized significant savings in maintenance costs with no innovation budget, mandate or dedicated talent. This case study shows data and analytics leaders how to set up projects so that failure is as valuable as success. 3 Case Studies of Data- and Analytics-Driven Business Innovation:  Data and analytics leaders, learn here how three progressive peers have built data- and analytics-driven innovation into their engagements to rapidly generate business value. Case Study: A Culture of Data Literacy and Data-Driven Decision-Making (Froedtert & the Medical College of Wisconsin) Find out how the CDAO at Froedtert addresses the human challenges that prevent business partners from making data-driven decisions through targeted coaching and relationship building. Case Study: Monitoring the Business Value of AI Models in Production (Georgia Pacific) Do your predictive models leak value in production? Do you *know* if they do? Learn how Georgia Pacific monitors their AI models to ensure they generate the most value possible. Case Study: Entity-Event Knowledge Graph for Powering AI Solutions (Montefiore) Do data and analytic silos prevent your organization from rapidly developing AI solutions? Find out how Montefiore uses an event-entity knowledge graph to organize its data and accelerate the development of AI models. Case Study: AI Innovation with Startups (Stora Enso) Learn how Stora Enso partners with early-stage startups to rapidly develop innovative, bespoke solutions to their business problems. Build the Data & Analytics Core and Deliver Value Simultaneously  Don’t fall into the trap of building up your talent and D&A core before delivering value; learn how D&A organizations do both at the same time here! Ignition Guide to Creating, Measuring, and Improving the Performance of a Machine Learning Model Measuring the performance of a machine learning model is sometimes more of an art than a science. Follow these steps to measure and improve your machine learning model! 3 Ways to Monetize Data and Analytics Learn how three progressive organizations overcame common roadblocks to monetize data and analytics! 13 Ways Real-World Companies Deliver Measurable Value with Data and Analytics D&A is a value-generating activity. Ensure your team generates business value by following these 13 strategies sourced from real-world companies! Case Study: Answering Critical Business Questions with Graph Analytics (Jaguar Land Rover) D&A is supposed to solve business problems. All too often, these efforts run aground because business units and data teams lack a shared understanding of data. Learn how JLR uses Graph to connect business and data domains in order to ask and solve difficult business questions. Case Study: KPI-Led Data and Analytics Digital Transformation (St. Luke's) Data and analytics leaders struggle to get business buy-in for transformation efforts. St. Luke’s justified their transformation as crucial to the organization’s ability to calculate cross-functional KPIs at scale. This won them buy-in and enabled rapid response to the COVID-19 crisis. Case Study: Data and Analytics Monetization with Knowledge Graphs and AI (Turku City Data) Learn how knowledge graphs allowed Turku city’s data team to quickly monetize their data and analytics assets. Advanced Data and Analytics; What Do Leading Organizations Do?   Learn the perspectives, practices, and strategies that have made leading D&A organizations so advanced. Case Study: Data Monetization Through Data Product Development (ZF Group) Successful data monetization requires much more than just selling your organization’s data! Learn how ZF Group monetized their data by building and selling powerful data products. Tool: D&A Use Cases to Improve Operational and Financial Performance As your organization transitions from immediate COVID-19 response to adjusting to new economic realities, use this tool to find real-world D&A use cases that improve operational and financial performance. Tool: 6 Ways Data and Analytics Leaders Can Serve Their Organizations During the COVID-19 Crisis How can data and analytics leaders serve their organizations during the COVID-19 crisis? This presentation showcases companies that use D&A to cut costs, optimize business processes, spend efficiently on talent, maximize the value generated from existing spending, and accelerate with new. Tool: Practical Cost Optimization Techniques for Data and Analytics Leaders Responding to COVID-19 Data and analytics leaders are well placed to assist their organizations during the COVID-19 pandemic. This compendium provides critical resources and guides to quickly ramp up cost optimization plays with D&A. 6 Lenses for Discovering D&A Value Generation Opportunities Click here to learn six lenses D&A leaders can use to identify new D&A-based value generation opportunities across their organization. Each lens comes from a real-world practitioner profiled in Gartner research. Tool: A Living Library of Real-World Data and Analytics Use Cases This spreadsheet contains over 300 real-word D&A use cases. Filter use cases by organization size, industry vertical, and solution type. Updated quarterly. Library: Examples of How Data and Analytics is Used Across the Enterprise (Domain Data and Analytics) D&A leaders can use this library to learn how their business partners are using data and analytics. The library collects and categorizes over 150 different case studies, tools, templates, and notes from across Gartner research. Infographic: Six Traps on the Road from Data to Value Gartner research reveals six traps organizations fall into when they try to generate value with data. Avoid them by learning from the case studies referenced here! Case Study: Realizing the Promise of Analytics and BI Platforms (Dow) If your organization is like most, you have an underused BI platform. Dow’s D&A leadership analyzes BI consumption patterns, intervenes strategically, and evangelizes successes. Their approach led to 25% more platform use and a 4-fold gain in revenue. Communicating through Data Visualization Does your team struggle to get business partners to act on the insights you generate? This research helps D&A teams understand how to use visualizations to better communicate their conclusions. Performance Measurement for Data & Analytics Leaders Template (Utah Governor's Office of Management and Budget)  The C-Suite expects D&A leaders to generate value, but many D&A leaders struggle to quantify the performance of their teams. This downloadable and editable template offers D&A leaders an easily implementable approach to performance measurement that follows Utah GOMB’s approach. Performance Measurement for Data & Analytics Leaders Tool (Utah Governor's Office of Management and Budget) Utah GOMB combines multiple performance indicators into a single, easily understood ratio. D&A leaders can follow Utah GOMB’s  approach to quantify the performance of their teams. This downloadable presentation explains Utah GOMB’s approach and complements the template above. 5 Steps to Get Started with Machine Learning Eager to get started with ML but afraid it will be too technically difficult, expensive, or time consuming? Click here to learn the 5 steps D&A teams from Micron, Iron Mountain, and Avon used to get started with ML. How to Reveal the Business Value of Imperfect Data with AI (Avon) Imperfect data is worthless for business intelligence. But it can create business value, if organizations switch from BI to advanced analytics. Find out how Avon did so here. Data and Analytics Value Creation: Key Obstacles and How to Overcome Them Learn what Chief Data & Analytics Officers polled during the 2019 D&A Summits in London and Orlando believe enables their organizations to create business value with data & analytics. Peer-Based Analytics Learning (ABB) Frustrated with low analytics use in your organization? Take a lesson from ABB’s audit function: they use peer-led case studies to give auditors hands on experience in how analytics can improve audits. Machine Learning Literacy for Business Partners (Micron) Do you have data scientists mired in dashboard creation? Or do they develop cool products that don’t meet business needs? Find out how Micron improves communication between data science teams and business partners with a simple ML literacy course. Machine Learning Literacy for Business Partners (Micron) Implementation Tool Download Micron’s internal ML literacy syllabus here. It includes two case studies business partners can use to experience developing an ML solution on their own. Analytics Presentation Engagement Framework (NGA) Too often, analysis falls on deaf ears, and excellent insights fail to drive value. See how analytics leaders at the National Geospatial-Intelligence Agency create analytics presentations that motivate business users to action. Opt-Out Decision Engineering to Increase Analytics Use Business users often have powerful analytics tools available—but they rarely use them. Data and Analytics leaders can use an opt-out technique to shape the behavior of business users by “nudging” them into using of analytic tools. Decision-Focused Data Maps (General Mills) Do people in your organization spend more time looking for the right data than using it to inform decisions? Find out how General Mills developed an easy-to-understand visual that connects crucial business questions to available data sources. Simple, Powerful Machine Learning Pilot (Iron Mountain) Do worries about expertise and expense keep your organization from piloting ML projects? Find out how two FTEs in Iron Mountain’s A/R team developed a Machine Learning pilot off the side of their desks that decreased time to payment by 40%. From Data to Prediction (Iron Mountain): Further Details Find out the specific steps Iron Mountain used to develop their Accounts Receivable late payment prediction pilot here. How to Build Momentum for Machine Learning (ML) Initiatives (Iron Mountain) D&A leaders need to build on their successes with Machine Learning. Find out how Iron Mountain did so here. Data & Analytics Strategy and Planning (Back to the Top) Create a RACI Matrix for Your Data and Analytics Initiatives  NEW!Data and analytics (D&A) leaders must effectively scope work and delegate responsibilities for efficiently running projects, teams or disciplines. This RACI (responsible, accountable, consulted and informed) chart can be used to define team roles, activities and responsibilities for critical goals. Case Study: Demand Management for Self-Service Data and Analytics Tools (SureSparkle*):  SureSparkle* proactively manages the demand for self-service data and analytics tools by targeting high-value users and then building sustainable partnerships with business stakeholders. D&A leaders, learn a new approach to prioritizing and delivering self-service here. Data and Analytics Org model Benchmarks: A Survey of D&A FunctionsHow do D&A functions organize themselves? Based on nearly 90 conversations with D&A leaders, Gartner synthesized nine common organization models. These org models reflect the complex reality in which D&A leaders operate and offer D&A leaders a way to benchmark their own organizations. Three Case Studies of Data and Analytics-Driven Business InnovationD&A’s contribution to business value depends on its innovative application to business problems. Learn how three progressive data and analytics leaders have built innovation into the core activities of their teams to rapidly generate business value. Infographic: IT Score for D&A Benchmarks for Transportation: Gartner’s IT Score for Data & Analytics benchmarks the maturity level and importance of 25 activities across seven objectives that are top of mind for data and analytics leaders worldwide. IT Score for D&A Benchmarks for Manufacturing Learn how your manufacturing organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics! IT Score for D&A Benchmarks for Banking, Finance, and Insurance Learn how your banking, finance, or insurance organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics! IT Score for D&A Benchmarks for Energy and Utilities Learn how your energy or utility organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics! IT Score for D&A Benchmarks for Healthcare Learn how your healthcare organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics! IT Score for D&A Benchmarks for Government Learn how your government organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics! Tool: Data and Analytics Strategy Template This tool can help clients in mid-size enterprises develop and organize their D&A strategy! Ignition Guide to Scoping a Machine Learning Project  Are you eager to use machine learning in your organization but unsure how to begin? This detailed guide gives step-by-step instructions for identifying a business problem that is ready to be solved with machine learning. Infographic: Establish a Repeatable Process to Discover Analytics Insights  Are there features that every analytics project ought to have? Yes! Use this infographic to learn how to standardized analytics projects so they reliably generate business value for your organization. As Demand Soars for Pandemic Analytics, D&A Needs a Business Leader Mindset  Learn how three data and analytics leaders’ business mindset allowed them to responded to the COVID-19 pandemic quickly and generate value for their organizations. Inside View: Attributes of a Good Data & Analytics Strategy This framework illustrates how to create a progressive data strategy by connecting it directly to business outcomes. Tool: Data Literacy Playbook A step-by-step guide for expanding data literacy across an organization drawn from real-world examples Data Pitfalls, Part 1: That Wasn’t Supposed to Happen! Every organization wants to be “data driven.” But few recognize that there are better and worse ways to make decisions with data. Find three pitfalls to avoid in your data-driven decision making here. Case Study: Data-Driven Decision Making Using the Assumption-to-Knowledge Ratio (Bose) Data and analytics leaders need to teach business partners to make good data-driven decisions. Bose developed an assumption-to-knowledge ratio that forces teams to weigh new information against key assumptions. D&A leaders can use this ratio to help their business partners. Pitfalls on the Path from Data to Decision Every organization wants to be “data-driven.” But few distinguish between good and bad ways to use data in business decisions. This publication is the first in a series dedicated to helping D&A leaders avoid data pitfalls: common mistakes business leaders make when using data. How to Select Attributes for a Bottom-Up Data Standard (GWC Implementation Guide)  Internal GWC documents illustrate how they selected attributes for inclusion in their bottom-up data standard. D&A Leaders can use this document to guide  their data integration efforts. A Bottom-Up Data Standard (GWC Implementation Guide)  Internal GWC documents illustrating the final stage of their data integration standard. D&A Leaders can use this document to guide their data integration efforts. Case Study: Bottom-Up Data Integration Standard for Advanced Analytics (GWC) Does fragmented, inaccessible data prevent your organization from taking full advantage of advanced analytics? Find out how GWC created a 500,000 record, water point dataset that integrated data from more than 50 countries in the global south with minimal burden on data providers. Implementation Guide: How to Create a Data Standard from the Bottom Up (GWC) Use this straightforward, six-step guide to recreate GWC’s data integration standard in your own organization. Data Dimension Prioritization Process (Clorox) D&A leaders often assume that more data is better. Clorox thought differently. Learn how they increased sales from targeted ads by limiting, rather than increasing, the variables included in their analysis. Clorox shows how to avoid the “more is always better” trap. Capability-Driven Data Use Expectations (Bunge) Do your business partners bog down your D&A team with endless requests for reports and dashboards? Find out here how Bunge standardized its business partners’ requests to ensure its data specialists received high value requests. Data Analytics Capability Survey Tool (Bunge) Click here to download the survey Bunge used to standardize business partners’ data requests. Continuously Market-Tested Data & Analytics Strategy (UrbanShopping*) Creating value from enterprise data requires organizations to make a blinding array of choices. UrbanShopping’s D&A strategy led them to create a D&A sandbox that enabled the rapid market testing of D&A solutions and drove substantial ROI. (*Pseudonym) IT Score for Data and Analytics Uncertain how to get started in D&A? Use Gartner’s IT Score for Data & Analytics to measure D&A maturity across seven objectives and 25 discrete functional activities! Ignition Guide to Strategic Planning for Data & Analytics As a Data & Analytics leader, how do you develop a world class D&A strategy? Tap into the collective wisdom of hundreds of D&A leaders. Here is Gartner’s step-by-step guide, with tools and templates, to help you establish an actionable Data & Analytics strategy. Analytics Prioritization Principles (Gap Inc.) How can Data and Analytics leaders sense, prioritize, and satisfy the critical data and analytics needs of their business users? Gap Inc. provides a model for engaging with business users to determine their data needs and priorities and develop the analytics they need. Data & Analytics Strategy Workbook D&A leaders often struggle to navigate the complexities of the strategic planning process. This workbook outlines the steps involved and provides hands-on tools and templates to create a strategic plan document. Data & Analytics Strategy Presentation Template This template provides D&A leaders with customizable recommended and optional slides to craft an effective D&A strategy presentation. Data & Analytics Sample Strategy Presentation This sample strategy presentation is an illustrative example of how to tie D&A strategy to business strategy and improve organizational decision making through analytics investments. Planet Architecture: The Role of Data in Platform Strategy (A Conversation with Ian Reynolds and Jitendra Subramanyam) Data is crucial to building the business case for new technology platforms. This episode explores how EAs and D&A leaders can maximize the business value of a platform strategy by effectively using available enterprise data. Data & Analytics Quality and Ethics (Back to the Top) How to Apply Ethical Principles to AI Models NEW!  The Danish Business Authority developed a concrete way to apply ethical guidelines to AI model development and assessment, once deployed. D&A and AI leaders can adopt the DBA’s approach to ensure they develop and use their AI models in an ethically defensible way. Playbook: Building a Modern Data Governance Program NEW! Data and analytics leaders can use this toolkit to modernize their data governance. Topics covered include governance models and processes, stewardship, information health metrics, and data quality. Deploying Effective Data and Analytics Governance: Three Companies That Got It Right   The three companies profiled here learned that D&A governance is more efficient and effective when it occurs as close to business decisions as possible. Three Progressive Approaches to Governing AI Learn how three progressive organizations govern their AI projects by tailoring governance to the stages of the AI development process. Case Study: Ethical AI with an External Board (Axon) Are concerns about potential ethical issues blocking you from implementing AI? Learn from Axon how to set up an external ethics board to keep your work ethically sound! How to Investigate the Operating Characteristics of Any Quantitative Model Any quant model, even deterministic ones, can behave unpredictably. Learn how to systematically investigate the operating characteristics of your business-critical quant models here. Case Study: Data Ethics Decision-Making System (Highmark Health)  Most D&A leaders believe complying with rules ensures ethical use of D&A. It doesn’t. Ethical use of D&A demands reflection on use cases, which enables decisions on their appropriateness. Find out how Highmark Health established a system to do so here. Zen and the Art of Data Quality Improvement Does low quality data prevent value creation in your organization? Reexamine your data quality improvement practices. This research profiles a Zen approach to data quality improvement adopted by several progressive D&A leaders who have created value with imperfect data. Data Governance Playbook  This toolkit collects client templates to aid D&A leaders in developing a data governance model. It includes templates for governance processes, stewardship roles, and information health metrics. Rationales for the Idealist Imperative in Business Are you perplexed by the hype around business ethics? Click here to learn why ethics are crucial for branding and financial performance today. Find concrete recommendations D&A leaders can take to ensure their organizations’ data practices are ethical. Human Controls for AI Dangers (SignatureValue Bank*) Rather than guarding against AI-based attacks, D&A leaders should collaborate with security leaders to guard against the threats internal AI applications cause. Find out how SignatureValue Bank did so here. (*Pseudonym) On Demand Problem Solving Teams (McDonald's) Are you frustrated with onerous data governance practices at your organization? Learn how McDonald's uses rapid response "sand dune teams" to efficiently make data governance decisions. Exclusion-Based Data Sharing Rights (FirstHarbor*) Do data sharing requests at your organization get bogged down because dozens of stakeholders have to approve them? Find out how FirstHarbor quickly determines which stakeholders should be excluded from reviewing data sharing requests. (*Pseudonym) Business-Need Driven Data Governance Objectives (FirstHarbor*) Does your data governance enable value creation or constrain it? Find out how FirstHarbor narrows the scope of data governance, meeting business needs in data collection, use, and sharing while ensuring compliance and productivity. Value-Add Data Minimization (Northrop Grumman) More data is not always better, because data comes with risk. Find out how Northrop Grumman selects data for minimization and sells business partners on value-adding alternatives. Ignition Guide to Building a Data and Analytics Governance Program Don’t assume that traditional data governance will meet the demands of big data and digitization! Use this guide to establish a governance program that aligns with business priorities and divides strategic and tactical responsibilities. Data Quality Score (TE Connectivity) Does your organization struggle to get buy-in for data quality improvements from your business users? Find out how TE Connectivity used an enterprise-public data quality score to hold business users accountable for data quality. Dangerous Data: Can’t Live Without It, Can’t Live With It Many D&A leaders think data is good and more data is better. But some data can pose serious legal, ethical, and brand risks to organizations. Find out why here. “Show Don’t Tell” Data Quality Improvement (Citizens Bank) Business users often struggle to see the relevance of data quality to their work. Citizens Bank creates business demand for increased data quality by contrasting the insights and reports that could be generated from higher quality data to the current state. Data & Analytics Operational, Data Quality, and Data Management Metrics How do Data and Analytics leaders measure success? Find out here: our collection of real-world metrics spanning operations, business value, data quality, and data management maturity. Data & Analytics Talent The Chief Data Scientist Role is Key to Evolving Advanced Analytics and AI  The role of the chief data scientist is increasingly prevalent. Use this research to orient and guide the chief data scientist to strategically support, manage and scale the use and adoption of advanced analytics and AI within the organization. The Current State of Demand for the Chief Data Scientist Role: Q1 2021 Report Click here to learn about talent availability, location, diversity, education and experience levels along with most-common personas and job titles for the trending chief data scientist role. The Current State of Demand for Data and Analytics Roles: Q4 2020 Report Are you looking to keep your data and analytics operation at pace with the market? Use this guide to understand the market demand for D&A talent, including two role profiles. Building a Strong Data Science Team on a Tight Budget Using Data Scientist Adjacent Roles Struggling to find the talent you need for data science? Learn how to use role adjacencies to identify skilled individuals already in your organization and use them to build high performing teams! The Modern Chief Data Officer: 3 Insights from Social Media Discussions (2018-2020) Learn how the of the Chief Data Officer has changed over the past two years from an analysis of social media discussions on the topic. The Modern Chief Data Officer: 3 Insights from Social Media Discussions (2018-2020) Learn how the of the Chief Data Officer has changed over the past two years from an analysis of social media discussions on the topic. Leverage Role Adjacencies to Respond to the Shifting Talent Market Amid Disruption The COVID-19 pandemic has thrown the talent market into turmoil. Learn how to leverage role adjacencies to find the skills and abilities you need. Tool: Data Ethics Interview Guide If you’re committed to ethical data science, you need D&A professionals who can think critically about the ethical issues in their work. Click here to find questions your hiring managers can ask to determine if job candidates have this crucial capability. Toolkit: Critical Market Information for Hiring D&A Talent in the United States This Toolkit presents data from Gartner TalentNeuron revealing the current candidate supply, demand, salary, time to fill, and hiring difficulty of key data and analytics roles in the U.S. labor market. Toolkit: Intelligently Sourcing Internal D&A Talent when Budgets are Tight This toolkit provides essential information to find people in data-adjacent roles inside your organization. For each common D&A role, we provide the most frequent skills and the most adjacent roles. Use this info to look for internal data talent! Use Role Adjacencies to Find and Develop Data Science Talent D&A leaders complain about the difficulty of finding high quality data science talent. How can they do better? By tapping sources the competition doesn’t know about! Click here to learn how to use role adjacencies to find data science talent others ignore. Case Study: Internal Data Science Team Development (Eastman)  Do you wish you could build a data science team but lack the resources to hire expensive external talent? Find out how Eastman built a business-value generating data science team beginning with talent they already had. Data and Analytics Talent Library This library puts all of Gartner’s resources on sourcing, staffing, organizing, and developing high performing D&A teams in one regularly updated place. Capability-Based Data and Analytics Talent (Stats NZ) D&A talent is expensive and hard to find. Learn how Stats NZ attracts the right talent by prioritizing core capabilities rather than technical skills in its recruiting. Find out how the same approach allows them to train staff that are flexible and laterally-mobile. Capability-Based Data and Analytics Talent Implementation Guide: Job Functions (Stats NZ) Use this tool to see how Stats NZ organizes their D&A work into separate specializations so they can effectively recruit the right talent. Capability-Based Data and Analytics Talent Implementation Guide: Core Capabilities (Stats NZ) What capabilities are necessary for a high performing D&A team? Stats NZ divides D&A talent into four core capabilities. Download their definitions and advancement scheme here. Capability-Based Data and Analytics Talent Implementation Guide: Core Capabilities (Stats NZ) What capabilities are necessary for a high performing D&A team? Stats NZ divides D&A talent into four core capabilities. Download their definitions and advancement scheme here. Capability-Based Data and Analytics Talent Implementation Guide: Job Descriptions (Stats NZ) Job ads are your first chance to attract new talent. Yet many D&A job ads are filled with jargon and long lists of technical requirements. Stats NZ writes jargon-free, accessible job ads for their D&A positions. Download examples here. Capability-Based Data and Analytics Talent Implementation Guide: Development Planning (Stats NZ) In a tight D&A talent market, it’s crucial to develop D&A talent internally. Stats NZ uses its for core capabilities to sequence internal D&A talent development. Download their guide to D&A talent development here. Metrics for Fair and Transparent Performance Narratives (Cafcass)  Are your employees frustrated by their performance evaluations? Do they complain about the metrics your organization tracks? Find out how Cafcass created a performance management dashboard that increased employee trust in performance metrics and made  evaluations fair. Cafcass’s Implementation Guide to Metrics for Fair and Transparent Performance Narratives  Learn how Cafcass teaches its employees about the metrics it tracks and ensures that their performance metrics are comparable across employees with different workloads here. Workforcewide Analytics Capability Development (Intel) Click here to learn how Intel's Financial Shared Services Center used internal data science talent to increase data literacy across the organization through peer-led education and a community of practice. Data and Analytics Job Descriptions Library From cutting-edge, emerging roles to those that are now standard in D&A teams, here’s where to find job descriptions sourced from peer organizations. Updated quarterly with contributions from Gartner’s entire D&A research community! Redefining Analysts as Decision Experts (Philips) Find out how Philips grew revenues by more than 18 million by aligning its analysts to support specific decision areas rather than a myriad of stakeholder requests. Creating Business Value with Multidisciplinary Data and Analytics COEs (Omicron) Does your organization use a Data and Analytics Center of Excellence (COE)? Are you thinking of setting one up? Learn how Omicron avoided silos and enabled cross-functional collaboration in their COE for Finance D&A. Creating Business Value with Multidisciplinary Data and Analytics COEs (Omicron) Does your organization use a Data and Analytics Center of Excellence (COE)? Are you thinking of setting one up? Learn how Omicron avoided silos and enabled cross-functional collaboration in their COE for Finance D&A. D&A Organizational Models, Roles, and Responsibilities How do Data and Analytics leaders organize their function? What roles should a data and analytics office have? This deck collects practitioner examples of organizational, staffing, and stewardship models and analytics roles. Analytics Champions Recruitment Guide Learn how companies identify internal evangelists for analytics and use them to increase analytics demand across the enterprise.  

            • Onboarding Your Sellers: Six Steps to Improve Seller Onboarding Effectiveness
              by Doug Bushée on June 15, 2022 at 11:42 am

              Introduction You've likely heard the statistic that it takes 3-4 months for a new sales hire to be fully up and running at their new job. However, on average, most new hires are only assessed for productivity after six months. Waiting six months to start evaluating a new hire seller is too long when you need to meet annual targets. Onboarding an employee is not just about giving them a user manual and letting them learn by failing. It's about developing a program that ensures they feel welcomed into the organization, get off on the right foot, and start contributing as quickly as possible. While every seller is different, there are six key steps to consistently improve the onboarding experience to get new sellers running full speed right out of the gate. Assess the Current State of Onboarding You'll have a much better understanding of where to start if you start by assessing the current state of your onboarding program. A robust assessment means gathering feedback from recent onboarding graduates, new hires currently enrolled in the program, sales managers, and mentors about what's working and not working with your existing program. It also means being a student: taking some of the courses and participating in some synchronous training yourself. During this process, you should also identify an executive sponsor who can help prioritize updating onboarding, among other business initiatives. Determine the Objectives of Your Onboarding Program The next step is to determine the objectives of your onboarding program. These objectives should include business objectives for the onboarding program, such as improving new hire retention or reducing time to performance, and learning objectives, such as building specific competencies needed to succeed in the role. You should also set performance objectives, such as time to completion, knowledge retention, learner experience, and manager perception of the onboarding program. Design Key Activities and Select Key Milestones As you think about the onboarding process, it's crucial to pick key new hire milestones and design learning activities. Key milestones are the steps in the sales process that new sellers need to reach during the onboarding process. For example, they could include milestones such as a first client call, first meeting, demonstration, proposal, and win. You should pick six to eight key milestones that align with your sales cycle and create a learning plan for each. Next, you want to create activities that support learning retention, such as project-based learning (PBL), self-paced learning (SPL), and Instructor-led training (ILT). These approaches, when blended, allow for a more engaging learning environment than traditional classroom-based training. Finally, think through what skills the seller can apply in their job in their first week, first month, and first quarter. Then, build content that supports the learning objectives while aligning with the seller's ability to apply the learning. Develop Support for the New Hires A new hire seller's ability to navigate the internal organization and quickly find answers to questions is as important, if not more important than giving them the skills they need to sell your offering. You can support your new hires by doing the following: Stand up a formalized mentor program that aligns with the onboarding process. Enable a new hire communication channel that allows new hires to interact with other new hires from around your company. Provide sales coaches or, at a minimum, a new hire onboarding program manager that can serve as the first point of contact for typical new hire seller questions. Be sure to have "Where do I go for help?" resources available at the end of every module in your onboarding process so that when people have questions about how things work, they have someplace to go for answers quickly. Deploy the New Onboarding Program to Your New Hires After you've built the onboarding program, it's time to launch and begin offering self-paced and synchronous learning to your new hires. As with any enablement program, change management is essential to the program's success, and effective stakeholder communication is critical as you launch the new program. In cooperation with your executive sponsor, communicate program details and expectations with new hires, managers, and mentors. Where necessary, provide awareness and training on any new apps, tools, or resources that differ from today's onboarding program. Measure Onboarding Effectiveness and Continuously Improve the New Hire Experience Finally, the best way to get continued buy-in from the sales leadership is to show them the value of the onboarding program; decisions about how you measure onboarding effectiveness will play a significant role in demonstrating the program's effectiveness. Here are some standard methods for measuring new hire experience: Surveys: Ask your sellers what they think of their onboarding experience. Use surveys at three points during the process: right after concluding the onboarding program, six months later, and 12 months later. Assessments: Use assessments to gauge learning retention by comparing current answers with that given pre-training. Field feedback: Ask managers and mentors for their thoughts on the new hire's performance. Identify ways to measure impacts, such as time to performance or confirmation of behavior change. Show these metrics in dashboards so everyone can see how well they're doing—and keep improving over time. Conclusion Communication is key to success, and your onboarding program is no exception. Without effective communication, new hires can feel lost and confused about what it means to be successful at your company. In addition, while onboarding doesn't have to be complicated or expensive, it does need to be well-designed and integrated with the rest of the organization. By following these six steps, you can create a new hire experience that helps sellers hit their goals more quickly and strengthens engagement across all functions supporting new hire sellers.

            • Despite Crypto Bashing and Crashing, Blockchain holds Real Value for Businesses
              by Avivah Litan on June 15, 2022 at 6:44 am

              We just published two research notes –  FAQ for NFTs on Blockchains and Web3 Ecosystems  and FAQ for Cryptocurrencies on Blockchains and Web3 Ecosystems documenting the benefits, risks and use cases for NFTs and cryptocurrency. In the first research note, we document how NFTs help organizations manage real world assets, bringing unique value to applications that was heretofore not possible.  Blockchain supports the tokenization of real-world assets, and the use of smart contracts to manage their life cycle. It also supports a trusted immutable system of record, shared across multiple entities. The technology is helping the airline industry with their super complex MRO processes (maintenance repair and overhead) as shown in the two diagrams below for Case Study 1.   This  SITA -sponsored solution, developed with Sky Republic  is already rolling out and should greatly improve MRO. This will also improve passenger experiences with increasing rates of frustrating flight delays and cancellations. Blockchain tech is also helping users manage documents via a Gmail UI, as shown in the last two diagrams under Case Study 2, which depicts a solution developed byShelterzoom. By tokenizing documents and managing them with smart contracts, users can control the documents’ access rights, downloads, printing,  sharing recalls, and entire life cycle.  This same tech can also be used to virtually negotiate contracts, or to tokenize any real world asset and track and manage it through its lifecycle. Case Study 1: Airline MRO Blockchain     Case Study 2: Smart Document Sharing     The value of blockchain technology should not be conflated with the speculative prices of tokens and coins.  I remember covering the first iteration of the Internet during the dot com bubble days – the naysayers and internet bashers were active at that time as well. Pets.com failed and so did other ecommerce experiments, but Web 1.0 and Web 2.0 eventually thrived and changed the world. It has become too cool to criticize cryptocurrency and NFTs these days, but from where I sit, I welcome them as Web3 innovations that are solving big real world problems we have had with the Internet for years, namely Internet cash,  creator-owned and controlled content, and Trust. Our case studies are proving the new business value of blockchain beyond any doubt.

            • B2B Buyers' Behavior Requires Marketing and Sales Alignment
              by Jeffrey L. Cohen on June 15, 2022 at 1:48 am

              Are you a CMO looking to collaborate with your sales counterpart, but just don't know how to approach them? Maybe you're a sales leader who knows it is beneficial to work more closely with marketing, but just don't have the data to back up your desire? Here are some data points uncovered by the latest Gartner B2B Buyer Survey to support your cause. The answers from the over 700 B2B buyers across industries and purchase types help demonstrate the importance of collaboration between these two functions. As more buyers take advantage of digital commerce — 72% of B2B buyers completed a recent significant purchase transaction by ordering or paying online — marketing’s role to support those purchases is critical. And it can't happen in isolation. The sales team is also part of these transactions, whether the sales rep supported the purchase or not. B2B Buyers' Information Sources Buyers get their information from websites. This is not news. But during the purchase process, their leading source of information is from the supplier's website from whom they made their purchase. In addition to the website, buyers got their information from sales subject matter/technical experts and sales reps. Just to recap, in an environment where buyers are leaning more towards online purchases, they are getting their information from the website (created by marketing) and sales. But are marketing and sales saying the same thing? Not always. Not even half the time. More than half of B2B buyers — 53% — received different information from the website and from sales reps. It is time for these functions to get it to together. This can start by working together to determine what to say, but also making sure to say it the same way. B2B Buyers' Purchase Regret One of the negative outcomes of the adoption of digital commerce is that B2B buyers who conducted their purchases online had a high amount of purchase regret. We are talking about a considered purchased that involved at least two buyers. Two-thirds of the purchases cost more than $100,000. It is surprising that there was this much purchase regret from digital commerce. What did that purchase regret look like? 34% agreed that "we should have chosen something different from what we ended up buying." 41% agreed that  "we should have thought about it more before making this purchase." And 50% agreed that "with more information, we could have made a better decision."   These regrets could be prevented by more attention from marketing. Vendors want buyers to be happy with their purchases, and if buyers need friction in their buying process to slow them down, that can be done. Compelling content and digital guided selling tools can help B2B buyers slow down and choose the best solutions. Even stronger encouragement on the website to utilize a sales rep can help. But it is the collaboration between marketing and sales that can improve all of this. Identifying the right audience, know what to say to them and even how to measure success will reduce some of the regret. The more vendors know about their buyers, the more they can provide solutions to meet their challenges. CMOs and sales leaders must align on the information that is available on the website and through the sales rep to improve buyers' decisions.

            • Speculating on Buying Behavior and Inflation Using Enterprise Technology Adoption Profiles
              by Hank Barnes on June 14, 2022 at 12:51 pm

              Rising inflation, the Russian Invasion of Ukraine, and other tensions create a lot of uncertainty in markets.   But uncertainty is nothing new.  We just went through it (and are still going through it to a degree) with COVID-19.   And there will always we changing circumstances in markets that we have to deal with.  Our forecast team is working continually to understand how this impacts what people buy and how much.   That is where behaviors are most likely to change.  But how they buy will probably not change significantly, beyond the potential for increased scrutiny of purchases. All of that being said, I do think this is a good time to remember that you can't really understand what is going on by assuming that all customers will react the same way.  In times of crisis or increased scrutiny, some organizations see an opportunity to gain, or strengthen, competitive advantage knowing that their competitors are likely to be more risk averse.   So, with everything going on, I thought it might be helpful to look at some still relevant Gartner data using our Enterprise Technology Adoption (ETA) profiles.   This is from a study we conducted  about buying efforts undertaken in 2019 and 200.  We looked at the number of buying efforts organizations pursued--and how many of them were effectively canceled --- the decision that was made was not to do anything.  I've probably shared some of this before, but it is worth a new look. Part of what we wanted to do was see how much the pandemic impacted buying efforts.  We were expecting a dramatic difference in the number of efforts pursued in 2020 vs 2019.   We did not see it.  Basically, for every ETA grouping, and overall, there was a slight increase in buying efforts considered in 2020 compared to 2020.  But the more interesting thing would have been cancellations. Surely, we would see a difference there.   Not really. The only two groups that canceled a higher percentage of buying efforts in 2020 compared to 2019 were the two strict planning organizations - Fast Followers and Disciplined Followers.  This does reflect the discipline they bring to the decision process.  For every other group, they canceled the same percentage or less in 2020.     The overall % of cancellations is just more evidence of the New Chasm.  The organizations that are more likely to be effective at buying, namely the Agile Leaders, Fast Followers, and Disciplined Followers cancel a significantly lower percentage of buying efforts than those that are on the other side of the new chasm. Just to add a bit more detail before discussing some speculation on what this might mean for the current, and future situations,  we can look at the percentage of the 2020 efforts that were cancelled due to implications of the COVID-19 pandemic. Here the picture is a bit different.  The more effective buyers were more likely to have canceled buying efforts due to COVID than the others! Implications As uncertainty mounts, the macro trends of markets, using our forecasts, are important to understand.  But for specific opportunities, you can't think macro--you need to think micro-and truly work to understand the situation of your customer.  The questions I would be asking: What is the ETA of the prospect? How important is the project for which your product or service is being considered? How much risk is associated with the project? How does the cost outlay for the customer look over time? and more But above all, don't make blanked assumptions.    You can start with the knowledge that more than 1/2 the market struggles--but many still invests.   Those that are more likely to cancel don't cancel everything--but you will need to connect and understand other issues and dynamics. Just as the more disciplined and effective buying organizations gain advantage from that discipline, now is the time to step up your efforts in building a culture of deep understanding of the customer.  Doing so will give you a competitive advantage.   We'll talk about this more at the virtual Gartner Tech Growth and Innovation Conference.  Hope you join us.    

            • 4 Types of B2B Buying Organizations: Align Your Marketing Accordingly
              by Rick LaFond on June 14, 2022 at 9:01 am

              The only constant in life B2B buying is change. Ninety-four percent of B2B purchases are made amid organizational change, according to the 2021 Gartner B2B Buying Survey. The top organizational changes that contribute to a B2B buyer's need to make a purchase include: digital transformation, a change in operations, and a response to new regulatory requirements. A B2B buyer’s ability to successfully navigate those changes can make or break their ability to buy and implement a new solution. Fortunately, both marketing and sales teams can offer information, guidance and tools to help buyers confidently understand and manage the change(s) contributing to the purchase need. A Gartner survey of 725 B2B buyers revealed four distinct enterprise customer profiles. These profiles have stark differences in how they manage change, as well as how they behave during the buying journey. We’re calling these four profiles “Gartner’s Enterprise Change Readiness Profiles”. Which Profile(s) Represent Your Customers? These four profiles span across industries, purchase categories and company sizes. Fence-Sitters are the most common profile, representing 39% of survey participants. You might be able to instantly recognize which profile best aligns to the majority of your customer base. If you're unsure, share this blog with some members of your sales team. They should be able help you find the right answer. Alternatively, your customer base might consist of a more even mix of two-to-four of these profiles. You might also see some themes across different product and/or customer segments. Tailor Demand Generation To These Profiles Each profile requires a different combination of digital content, guided selling tools and sales support to make high-quality purchases. For example, Adventurers are organizations whose openness to risk and change exceeds their practical readiness to successfully execute change. For customers that fall into this profile, you should offer information and tools that prompt buyers to engage in productive reflection. Help them reexamine their own needs and goals to ensure these customers fully understand what is required to make their change successful. Provide prescriptive advice and practical support for completing the tasks associated with managing the change contributing to their purchase need. To learn more about the characteristics of each of these profiles, as well as to collect guidance on how to best engage with each of these profiles, access this detailed report: Boost B2B Demand Generation Performance Using Gartner’s Enterprise Change Readiness Profiles (Gartner login required).

            • Scenario Planning for Seasonality — The Ultimate Duet Between Demand and Supply
              by Sarah Gilchrist on June 14, 2022 at 9:00 am

              Summer finally feels like it’s arrived in the U.K. The sun is shining, Wimbledon is on the horizon and families up and down the country are sparking up their barbeques. Whilst many British residents associate the summer sunshine with relaxation — and maybe even a well-earned summer holiday — there will be others in the country for which this is the make-or-break selling period for their company’s seasonal products (e.g., alcoholic summer spritzers, barbeques and Father’s Day gifts) Here lies the crux of the seasonal product supply chain. Months of supply chain planning and production have been orchestrated, and now the only question to be answered is “Will the sales be in line with the forecast?” Facing Into the Decoupling Between Demand and Supply For products with highly seasonal demand, supply chain leaders must acknowledge, and proactively face into, having to finalize decisions on supply commitments in the context of incomplete demand intelligence. This challenge is not unique to seasonal products, however, it is amplified by the stockbuild requirements often required to service the huge peak in sales. Key to maximizing the seasonal peak’s profitability is ensuring that commercial teams are engaged up front and in advance of the supply plan being “locked and loaded.” Insights around projected category size and market share, joint business plans with key customers and advertising and promotional plans all provide assumptions to underpin the size of the demand peak. Identifying Risk and Opportunity to the Sales Forecast Through Scenario Planning Companies can leverage commercial insights further by using scenario planning within their S&OP process. Scenario planning allows companies to take proactive, financial-based decisions to trade off the opportunities of seasonally driven sales, often at optimal margins, versus the risk of closing the season with unsold stock. At best this stock might sit on the balance sheet for a year and, at worst, depending on shelf life, may be at risk of write off. To work at its best, scenarios should focus on variables that might cause a positive or negative variance around the demand plan during the sales peak. Such insights and intelligence lie in the heads of the commercial teams in the business. Therefore, it is critical that intelligence and insights come from the demand side of the business, rather than being estimated or assessed in isolation by those further upstream in supply chain. Consider Not Only External Variables, But Also Internal Levers Splitting the factors that might impact sales into external and internal drivers helps provide a structure (see Figure 1). It is also an important distinction to make. A company will have limited ability to influence the external factors: however, the internal levers are within its control. Data and intelligence from sales and marketing teams on understanding, upfront, what internal levers exist to mitigate external headwinds allows different scenarios to be modeled from both a volume and value perspective. For example, commercial colleagues might be able to provide intelligence such as: “An x% uplift in sales or a y% increase in market share could be stimulated by an extra z$ investment in marketing” Upfront information like this is scenario-planning gold dust! These sorts of inputs allow business leaders to make informed, assumption-based and quantified decisions upfront about the financial consequences of selling through seasonal stock builds. Constructing various scenarios that assess the impact of a variety of “what if” scenarios based on both external variables and internal lever responses enables potential outcomes to be assessed before irreversible decisions are made. Five recommended steps to bring the theory to life Start your engagement with stakeholders early — If your seasonal products have an annual cycle, you only have one chance a year to set out your stall. Identify who will sponsor your approach — Ultimately, this needs to be the person in your business that cares how both revenue and costs impact the bottom line and is also invested in the inventory impact on cashflow. Partner with finance — Switching on any internal lever to stimulate sales of prebuilt stock will almost certainly trigger a cost. Collaboration with finance should both facilitate predicting the financial outcomes of different scenarios and also provide an ally in pitching your proactive, scenario-planning approach. Document the lessons — As you practice the strategy, keep a record of what worked and where there were opportunities for improvement, both in terms of data and approach. This ensures that valuable knowledge is incorporated into planning for the next peak. Ensure active decisions are made in a timely manner — By its nature, this supply chain context is time sensitive. If a proactive decision is not made, a passive one will be made, by default, in its place.   Sarah Gilchrist Director Analyst Gartner Supply Chain Sarah.Gilchrist@gartner.com   Listen and subscribe to the Gartner Supply Chain Podcast on Gartner.com, Apple Podcasts, Spotify and Google Podcasts

            • Where Machine Intelligence Excels over Human Intelligence
              by Anthony J. Bradley on June 14, 2022 at 8:57 am

              As I have stated previously, and hopefully clearly, I prefer the term “machine intelligence” over “artificial intelligence.” The former term favors the benefits of machine intelligence. The AI term leads to a comparison with human intelligence with a bent towards how machine intelligence falls short. The comparison to human intelligence is not constructive because, in reality, machines have their own approach to “ intelligence.“ An approach that far exceeds many capabilities of human intelligence. This is why machine intelligence and human intelligence are disparate and compatible, not interchangeable.   Machine intelligence is intelligent in its own way.    With machine intelligence, machines substitute pattern construction for learning, pattern recognition for understanding, and probability analysis for judgment in decision making. These are the core capabilities behind the machine learning subset of AI.    Some artificial intelligence pundits seem to downplay what machines can accomplish because, compared to human intelligence, it is not “true intelligence.” In my opinion, this misses the point. Just because the means are different doesn’t mean the ends aren’t just as, if not more, valuable. Although machines take a different approach to intelligence, this doesn’t mean that it isn’t tremendously beneficial.   Machine intelligence is a tool, just like human intelligence. Just like humans, MI can make bad decisions based on bad data. Like humans, MI can be manipulated and used towards nefarious means. Just like humans, MI needs governance to reach its positive potential. I’m not positioning MI as this pollyannaish force for good. But It is a powerful tool capable of “intelligent” work in its own variant of the term.   Machine intelligence is better than human intelligence in many ways.   The machine intelligence combination of pattern construction, pattern recognition and probability-based decision-making is indeed very powerful. This combination enables data engineers, data scientists and computer programmers to build algorithms that can see. They can create, read, write, listen, respond, decide and act. Machines do these “intelligent” activities differently from humans and with some superhuman capabilities.     You can sum up what machine intelligence does very well with the three Ps of patterns, probabilities and performance.  Machines are far better than humans at identifying patterns in enormous amounts of data.   A big strength of machine learning is finding patterns in large amounts of data. The “big data” concept began gaining general acceptance about a decade ago. But we are now past “big data” into unimaginably enormous data. And the rate of data accumulation is growing exponentially with the combination of social media, the internet of things (IoT) and cloud computing. This ever expanding volume of data makes algorithms indispensable. We now have far more data than humans can absorb with even the best dashboards. There is little value in these enormous data sets without algorithms to help make sense of them.    Finding patterns in large amounts of data is the basis for machine learning. The “learning” aspect is the creation of algorithms that identify select patterns in the data. So, computers don’t learn the way humans do. Instead, they learn the way machines do, through pattern-based algorithm training. For example, using a large number of images of dogs, a computer algorithm can randomly assign and reassign variable values to pixel patterns until the algorithm establishes a formula that represents key pixel pattern characteristics of dog images. This is pattern construction through deep learning neural network technology.   If pattern algorithm creation simulates human learning, pattern recognition simulates human identification. After the “pattern constructed“ algorithm is complete, you can input a new image and the algorithm will predict, using probability mathematics, whether it is or is not a dog based on how well the pixel patterns in the new image match the “dog” pixel patterns in the algorithm.   Algorithms are highly effective at recognizing objects within images and they get better every day. The same goes for identifying patterns in video, sound, light, text, and, of course, structured data. Earlier, I specifically used the phrase “simulates human identification” vs. simulates human understanding. Because although the algorithm may recognize pixel patterns associated with the label “dog,” it doesn’t understand dogs. It doesn’t know that dogs are man’s best friend, that they like to fetch things, that you can train them with treats, etc. It simply recognizes pixel patterns with a strong match to those of dog images. Pattern recognition is the number one value proposition AI delivers to business.   For example, the US Postal service has now deployed an edge AI system using computer vision and analytics to process over 20 terabytes of mail image data per day. Using this image data, AI accomplishes tedious work at great scale. In addition to package tracking, it identifies, deciphers and repairs damaged barcodes. It also checks to see if postage is correct. And the USPS is just beginning to tap into the power of their growing image AI capabilities.  This type of AI application is growing steadily in the supply chain function and industry.   Machine intelligence also can create content from patterns   Machine intelligence doesn’t stop at pattern construction and pattern recognition. With machine intelligence advancements like generative adversarial networks (GANs) the pattern recognition algorithms can be “run backwards” to generate rather than identify content. This is the technology behind “deep fakes.” Machine intelligence can create. This is ground breaking. Over time, as these technologies evolve further, machines will be capable of generating text, images, video and sound content that is indistinguishable from the real thing. Yes, this certainly has numerous scary implications but the positive applications are astounding. Machines can very rapidly generate alternative designs that meet stipulated criteria.    For example, IBM has spearheaded new AI technology that can generate designs for new antibiotics and antivirals. Essentially, researchers apply AI to large data sets to determine patterns of peptide molecule binding relationships. AI identifies how molecules assemble to perform certain functions. Researchers then determine the characteristics they are looking for in an antibiotic. They input these characteristics into the algorithm and it generates alternative molecule designs that meet the criteria. Researchers then test these designs to find the most effective antibiotic.       Another more famous example is AlphaFold where AI is used to generate protein folding patterns. How a protein folds determines its function. Prior to AlphaFold, protein folding was a tedious, long, expensive trial and error effort. Referring to AlphaFold, protein folding expert and CASP co-founder John Moult is quoted as saying, “This is the first time a serious scientific problem has been solved by AI.”      Does it matter that the algorithm doesn’t “understand” the patterns it recognizes or generates? Theoretically, maybe. But practically, machine intelligence can accomplish a large variety of highly valuable work finding, identifying and using patterns.       Machine Intelligence substitutes probability mathematics for human judgment and decision making    Probability math is the foundation of machine learning. With probability based algorithms, machine intelligence can run through huge amounts of data, assess a multitude of potential options and then consistently select those with the highest probability of meeting the desired goal. This MI capability far exceeds those of even the world's smartest mathematicians. The capability delivers a powerful tool in making well understood business decisions at great scale.     With intelligent customer relationship management (iCRM) capabilities, large companies and service providers are using AI to process millions of prospecting email responses and automate decisions on prioritizing them as sales leads.   Some financial institutions are using AI to comb through enormous amounts of customer data to identify good candidates for a new product or service.     It is well known that facebook and Google apply AI against unimaginably large amounts of member activity data to make automated decisions on what content to serve them next. They also apply the same approach to decide what advertisements to feed members while trying to serve both user and advertiser needs.    This is algorithm driven probability based decision making that happens everyday across every industry and every geography.         Machine Intelligence outperforms humans in speed, scale and consistency   The performance of computing machines is well understood. Computers operate at astounding speeds and scale. They can consistently execute tasks with great precision at tremendous scale. Computers don’t get tired, frustrated, angry or rebellious. They do what they are asked with great efficiency. Scale, speed, endurance, consistency and precision have long been core value propositions of computing machines. We have long capitalized on this performance to execute procedural tasks. But now we have graduated from these more transactional tasks to more “cognitive” like tasks. And all the core computing benefits translate well to machine intelligence and its capability to detect issues, predict outcomes and facilitate decisions.    This characteristic of machine intelligence can’t be overstated. Why? Because it takes “intelligence” far beyond the human performance spectrum. When you hear about how AI will replace human jobs, it is almost always one-sided and limited to the human scale world. This may make sense for job losses but not for gains. Machine intelligence takes us far beyond the physical human world and opens up the hyper-human realm for new opportunities. This hyper-human realm operates at spatial, time and spectrum scales that are far too small or far too large for unaided human abilities. For example, the combination of microbiome ML, smart-microscopy and bio micro-robotics is turning human biome engineering (managing the bacteria in our gut) into an industry of its own.    At the other end of the scale spectrum, AI was required to combine data from eight observatories across six geographies to help develop the first ever actual picture of a black hole. I will post more in the future on hyper-human hyper-specialization and the explosion of AI generated jobs.        Business leaders can use the 3Ps to direct machine intelligence at the right business problems   Machine intelligence applies well to business challenges involving monitoring activities at great speed and scale to detect and respond to potential issues. Finding important business patterns in large amounts of customer or market data is also a clear application of machine intelligence. Any business challenge requiring pattern recognition, probability-based decisions and automated actions in a high performance environment is potentially a strong fit for machine learning.

            • Expect buyers to ask for more ‘skin’ in the game from providers in the coming recession.
              by Mark P. McDonald on June 14, 2022 at 7:53 am

              When the current economic and political turbulence turns into a formal recession is anyone’s guess. Enterprise IT spending will remain relatively strong. There are early indications that the nature of tech spending could change, namely that CIOs, Procurement, and IT buyers will ask their providers to put more of their revenue at risk – aka ‘skin’ in the game. The logic behind asking for more provider ‘skin’ in the game. Uncertainty increases as turbulence turn toward recession. Normally enterprises would pull back on spending. However, enterprises can be expected to continue IT investments in digital technologies. Companies in progress with digital transformation will need to complete it – quickly – as being half digital is the worst position a firm could be in. Buyers naturally want more downside protection.  That downside protection calls for providers putting more of their revenue at risk. There are several reasons for this move, in no particular order: Immediate demand for digital transformation is off the charts. Normally, providers in a high demand environment would be charging a premium rather than putting revenue at risk. The pending recession creates an incentive to secure future revenue, particularly among less powerful providers.  This sets the stage of trading future revenue with some downside risk. Buyers want to keep providers focused on them in a high demand environment, particularly buyers at companies farther behind the digital curve. They want providers putting their best efforts toward them. Buyers should be willing to trade paying higher margins on digital transformation work in exchange for that focus. Having provider skin in the game, even at higher margins, helps balance that type of deal. Providers in the past, have used a willingness to put skin in the game as a way of nudging clients off the fence. Not sure you want to do this, then let us share in some of the risk because it’s the right thing to do for your business and hey it shows our commitment. This ‘marketing’ strategy is more likely among the middle to lower end of the provider market as they seek to lock in deals. Buyers, particularly procurement and IT, will see having skin in the game to ‘de-risk’ digital transformation. In a way it does, but only if the provider fails to execute, but seeking providers to be at risk will help justify digital transformation cost and disruption. Skin in the game is not the same as committing to a business outcome. Having skin in the game, is a common recessionary play.  Putting revenue or margin at contractual risk is not the same a business outcome-based strategy. Outcome based arrangements like outcome pricing or outcome contracts base provider payment on provider performance. If the provider performs according to the contract, then they get paid.  That is radically different from customers and providers jointly sharing risk and reward based on realizing the benefits of business outcomes. See Why B2B Tech Companies need to value results over provider effort, for some additional points. What do we mean by an outcome?  More on that latter Related posts: The Rumor and the News, making sense of Turbulent Times Why B2B Tech Companies need to value results over provider effort. Turbulent Times Turning Toward Global Recession ? Digital Retooling > Turbulence and Recession Technology’s Evolving Covenant with Business

            • 3 Takeaways for Adapting Your Leadership Style to Uncertainty and Disruption
              by Derek Frost on June 14, 2022 at 7:14 am

              We're at the mid-point of another tumultuous year, with deepening anxiety about a range of issues: the war in Ukraine, the prospect of famine in vulnerable countries, an escalation of the sickening plague of gun violence in the United States, ongoing Covid mutations making their way around the world... just to name a few of the more acute worries on what has become a lengthy list. Meanwhile, the economy is giving off mixed signals at best, as inflation seems to have become entrenched in our late-pandemic world, supply chains groan under the weight of recent lockdowns in China and other, more systemic, factors, and stock markets sink into bear-market territory.   How are banking leaders meeting the current (fraught) moment? Against this backdrop, I had the chance some weeks ago to speak with a number of senior banking executives about their personal approach to leadership. How are they coping with ongoing uncertainty and disruption, both personally and professionally? And how does that translate into the way they support their teams? Some of these leaders had gathered virtually late last year for a discussion led by my colleague Mary Mesaglio on finding purpose and becoming a better leader. I wanted to catch up with them (happily, in person this time) to get a sense of what has changed in their lives as leaders since then... and what deeper lessons they have drawn from their recent experiences. Our conversation yielded three key takeaways for executives trying to adapt to these turbulent times:   1. Strong, confident leaders are candid, transparent, and unafraid of showing vulnerability. The candor---and vulnerability---of the people with whom I spoke were notable. Leaders, by nature, tend to be hyper-aware of the image they project; as a result, they won't often acknowledge being especially worried or uncertain. The executives I talked with, though, admitted to not having all the answers. Also: --They confessed to bouts of angst, pressure, and exhaustion, in addition to a lack of clarity. --Many are very concerned about the well-being of their teams: one executive mentioned that her bank routinely holds active shooter drills in some locations. --Perhaps one of the more telling comments was from a leader who said he felt a blend of despondence and confidence---a "mixed signals" emotional environment, if there ever was one. The vulnerability that this group felt comfortable sharing appeared to reflect their belief in themselves as leaders and their desire to do the right thing. After all, if you really trust in yourself, you don't obsess about how you might be coming across. You're comfortable being exactly who you are. These executives seemingly understand that the times in which we live pose unique, thorny challenges, rendering many traditional notions of leadership obsolete.   2. Use your openness, emotion, and empathy to foster a resilient, cohesive culture for your team. The second takeaway also had to do with vulnerability, but from a different angle: its role in creating a cohesive and trusting culture. (And culture certainly needs special nurturing in today’s "new-normal" remote or hybrid environments.) One leader, underscoring just how crucial culture is, made the point that only in a crisis do you know for sure if it's there or not. If it does show up, you'll realize you’ve succeeded in creating the right culture. Another executive then helped shed light on why vulnerability matters when it comes to culture. His key leadership lesson over the past two years, he said, was how important it is to be transparent and unafraid of appearing vulnerable. In other words, admit it if you don't have all the answers, and keep communications open about the challenges you face, the decisions you're wrestling with, and what you expect of your team. This can bring multiple dividends: --If you're clear about problems and what's being done to address them, that will help prevent the kinds of rumors and unfounded speculation that can stir up collective anxiety. --If you show emotion and behave transparently, your staff will be more comfortable doing the same. --And that transparency, together with a willingness to reveal vulnerability (part of what makes us human, after all), can, in turn, help an empathetic, open, and cohesive culture grow within your organization.    3. How to sail the unknown? Surround yourself with a diversity of perspectives. Reward honesty. And don't be afraid to change course.  The third takeaway was about flying blind: navigating a world of incomplete, misleading information, a world in which it seems harder than ever to gauge where the winds will blow from next. As my colleague Ben Seesel remarked to these executives, "No business school taught you how to make decisions in this environment!"  So, as a leader, what should you do? --Surround yourself with sharp and informed people. Get a diversity of perspectives in order to weigh risks and chart the course ahead more effectively. --That first point speaks to the need for a culture of "brutal honesty": reward staff for speaking truth to power, for giving you news you may not like. And be sure to create a safe environment for them to do so. Then move quickly to solve whatever problems have been surfaced.  --And speaking of a culture of honesty: don't be afraid to admit your own mistakes. Be willing to change course. Backtracking or turning is not a sign of weakness. (Next time you're at the beach, watch how sailboats tack through strong winds that might otherwise impede their progress, and remember that Odysseus, after a ten-year voyage of many a twist and turn, made it home at last!)   Learn, adapt, and remember our shared humanity. The past few years have been exceptionally hard: an era of deep uncertainty, loss, and change. The executives with whom I spoke have not only been weathering these times, but trying to learn from what they’ve been through and adapt their approach accordingly. The best leaders will endeavor to map the way forward with empathy, openness, and an appreciation of just how important a caring, communicative culture is.   

            • The End of the D&A Center of Excellence?
              by Andrew White on June 12, 2022 at 6:05 am

              News last week: Meta shunts their AI hubs into their business product Units. That’s the message reported in the papers Tuesday.  See Wall Street Journal and Meta shakes up AI unit Amid drive for faster growth. The Trouble with Organizations The problem or trigger is not unknown: a centralized team tends to specialize in skills or capability but it’s often remote from the business roles who need that capability. The functions are centralized and operate as a shared service to business functions or units.  This creates one of the oldest customer-service arguments; does the centralized service meet the needs of the distributed 'customer'?  It’s an organizational gulf that is hard to cross. In the past a customer mentality, not unlike "real" customers outside your organization, has been used to help drive success.  This worked in only a few places.  The application of lean, agile and DevOps has been used recently, with mixed results. Not least because such practices evolved for other challenges. The Rise and Fall and Afterward Over the years we have seen the rise and fall of centralized teams. Rather than rise and fall, it’s more like a seven year itch. Organizational structures tend to vacillate every few years. It would seem that we are in a new cycle where the focus is remote, distributed capabilities rather than fatter, centralized structures. The case reported in the WSJ article suggests that time to value is hard to reduce in centralized teams.  The inability to organize the central resource with the remote "customer" needs is the great challenge. Shifting skills to the edge and away from the center should put the capabilities in more direct control of business or "customer" needs.  The result will be that some capabilities will need to be duplicated across business functions or units.  This will likely increase costs and duplicate investments. As such the key here is not really where and when to centralize or distribute. The real challenge here is coordination. And that is the real battle field. Meta May experience shorter time to value in their next cycle. But their costs will increase. Rather than assume your organizational decisions are resolved by shunting the team skills to the edge, real success over time will be in how and who coordinates all the work. This is where your Chief Data and Analytics Officer (CDAO) role is key. The right personality, the right skills, will orchestrate and progress value delivery better than any one dedicated organizational approach. So don’t fret the Meta change: focus on the hinge or fulcrum that connects all the piece parts. For some related research: Where to Best Organize Data and Analytics.

            • 4 Reasons Why Financial Services Talent Leaves
              by Gladys Yeo on June 8, 2022 at 9:42 am

              “Frontline talent attrition levels are at an all-time high since the pandemic, it’s taking us much longer than usual to find the right fit” “We’ll have to likely use higher compensation packages to retain and attract talent in this market”   The above are some of the concerns that financial services leaders have shared with me during our conversation over the past few months. Hiring and retaining talent in this competitive job market is an ongoing challenge for leaders today. Some of the concerning statistics we’ve seen so far show that twenty-five percent of FS frontline talent reported a high intent to leave and nearly two-thirds would leave their current firm for a growth opportunity. Furthermore, about two-thirds of  senior FS executives surveyed in Gartner’s Financial Services Business Priority Tracker in March 2022 are expecting workforce shortages across lines of business and functions (e.g. business operations, contact centres, frontline staff) over the next 12 months But before you walk or dial into your next executive meeting to discuss your hiring and retention strategy, it’s important to understand “why” your people leave in order to determine where your bets are best placed.  One way to illustrate this is to think of your employee as an individual who is selling their house, and their reasons for selling can fall into 1 out of 4 scenarios.   1st Scenario: “There are cracks in the ceiling, the house needs repairing.” This implies poor managers, lack of employee recognition, or lack of professional growth opportunities. In short, your firm’s work experience is broken and in need of repairs. Frontline employees in FS are highly ambitious individuals who rank career growth and personal development as the two most important motivators. In this scenario, you will need to evaluate the way managers lead in the organisation as well as improve the visibility and opportunities for career development.    2nd Scenario: “There’s nothing wrong with this house, but I need an extra room so this house no longer meets my needs.”  In the last few years, new employee needs have emerged such as a greater demand for work flexibility or a greater interest in Environmental, Social, and Governance related issues.  Employees now overwhelmingly favour a hybrid work model - with 56% of operations employees stating that the ability to work flexibly would impact their decision to stay at their organisation. If employees sense a misalignment between these new personal needs and the overall employee value proposition of the organisation, your leadership will need to take a greater role in the things that matter to them.   3rd Scenario:  “The current house is fine, but the other houses are looking better.” In this scenario, other financial services or non-financial services companies are offering a better employer brand or more competitive compensation for critical talent. Firms with deeper pockets are willing to spend more to acquire the talent needed to drive growth. Some of the most sought-after roles within the FS industry include software developers, customer service representatives, sales agents, and financial analysts.  You will feel the most pain in this scenario. It is incredibly easy for employees to peruse the offerings of other firms with little effort, while leaders are typically faced with only one main strategy: that is to match the perceived better offerings of other companies.  However, with limited resources and budgets, leaders will need to determine the critical talent segments and decide where to selectively outcompete.     4th Scenario:  “I don’t want to live in a house anymore, I want to live on a boat” In this scenario, employees may have completely new lifestyle aspirations. The pandemic has caused many to question the purpose of work. Some may want to take the time off to reconsider their career path. Some are keen to pursue academic interests. And others are leaving the workforce entirely. This scenario is mostly outside of your control and ability to influence as an employer. In this situation, your efforts are better spent on recruiting and backfilling these positions instead of trying to convince your employees to stay. FS leaders should invest in creating a pipeline of talent to backfill these roles quickly.  Conclusion There’s no silver bullet to solving the attrition challenge and this will likely endure for the medium to long term in the industry. In order to adopt a more effective approach to address this issue, leaders will need to ask “why” employees leave. This will dictate the next steps critical to retaining talent.  To learn how other best-in-class organisations retain critical talent, here are a few recommended resources.  A Framework for Assessing Attrition Risk: who wants to Pack up, and Why?  4 Bold Strategies to Disrupt Compensation Competition in the New Talent Landscape  How Financial Services Leaders are Winning on ESG Goals  Blogposts: 5 Critical competencies for the Future of Financial Services How to Build an Engaging Virtual Onboarding Program for Today's Talent Market  Creating a Diverse Workforce in FS starts with Entry Level Hiring  Why the "Struggle for Talent" is a Red Herring  How to Make Hybrid Work Successful in Financial Services. 

            • Digital Retooling > Turbulence and Recession
              by Mark P. McDonald on June 8, 2022 at 6:00 am

              When the current economic and political turbulence turns into a formal recession is anyone’s guess. Enterprise IT spending will remain relatively strong. Enterprises will continue to invest in digital technologies despite a recession Turbulent times are turning toward global recession which increases uncertainty. Normally enterprises would pull back on spending. However, enterprises can be expected to continue IT investments in digital technologies. Gartner and others expect enterprise spending on IT to remain strong.  The logic of continued  spending relates to the following: Enterprises need to retool and put themselves on a digital footing. Enterprises started that retooling during the pandemic and need to complete it. Being half digital is the worst option. It creates a mutation not a hybrid – that is costly, rigid, and ineffective. Spending on digital technologies give leaders better tools to manage in a downturn. Cloud-based solutions increase IT cost elasticity and scalability. This is a plus in the face of variable demand. Digital enabled channels support deeper customer relationships and engagement. The deeper the engagement, the more likely customers are to remain loyal. These channels offer deeper interaction to extend the value of the relationship. Insight, generated by analytics and AI, give the organization a clearer picture of actual demand, operations, costs, quality etc. The more you know the better you can navigate turbulent times, particularly the unique turbulence we current face. The Dilemma Facing Buyers Spending now in the face of a near term recession presents a dilemma for IT buyers, primarily the CIO and Procurement. They need the benefits of digital retooling, but they want the flexibility to manage that spend in case of a severe downturn.  Remember being half-digital is worse than not being digital at all. Business leaders are compressing digital transformation schedules, knowing that the only thing certain is now and the longer they wait the more exposed they are to uncertainty.  This is a factor driving accelerated IT spending, particularly for IT Services. Acceleration comes at a premium in terms of costs and operational disruption. We can expect more traditional IT buyers to want to accelerate as well, but without the cost or disruption. More on that in the next post. Related Posts Turbulent Times Turning Toward Global Recession ? Technology’s Evolving Covenant with Business  

            • Connected Keynotes - Announcing Tricia Wang as the GartnerTGI Guest Keynote
              by Hank Barnes on June 7, 2022 at 10:00 am

              I was super excited last week to announce during a LinkedIn Live session that Tricia Wang will be our guest keynote on day two of the Gartner Tech Growth and Innovation Conference this July. I have long admired her work--check out this TEDtalk for a taste of her POV and her web site where the headline on the speaking page just makes me smile, "Not everything valuable is measurable." [caption id="attachment_3097" align="aligncenter" width="635"] Source: triciawang.com[/caption]   We spent a lot of time choosing a speaker, primarily because we didn't want to just have a great speaker (they are relatively easy to find).  We wanted a great speaker whose message would connect with the conference theme, "Tech Growth Requires a Relentless Customer-Centric Approach."   And Tricia fits that bill perfectly.  Her message to look beyond quantitative data for insights in "thick data" (aka qualitative data) is critical for everyone to understand, particularly as AI continues to take hold.   Data provides clues, but also may lead us down the wrong path. Insights that combine a variety of data is much more powerful. One example that comes to mind for me, of my own making, is the answer to the question "Who is your most valuable customer?"  Most go right to the customer that generates the most revenue.  It is the easy answer--but is it the right answer?   What if that customer  requires an inordinate amount of resources to support?  What if they expect customizations that really don't provide any value to others in the market?  What if the complain vocally about you, even as they stick with you? Contrast that with a customer that generates less revenue, but is a visible and vocal advocate--helping your sales and marketing drive interest and win business.   What if that customer is an active member of the customer advisory board, but doesn't do it for themselves, but to help make the product better for the market? I know which one is more valuable to me.   And that is what enhancing your approach to the search for insights can do. But back to Tricia.  It gets even more interesting.   Whenever you hire a guest keynote, there are always discussions about tailoring the content for the specific conference audience.  No surprise there.  Every great speaker has a mix of examples and stories that they can fluidly assemble together in a compelling narrative. But have you ever seen a guest keynote create a keynote that connects to an internal keynote?   I've never seen it, but I'll admit I'm not a big conference goer.   Well, that is what is happening with TGI.   As we were tell Tricia about our conference and the story we will be telling in the opening keynote about regret and paradoxes, her eyes were lighting up.   There is a natural connection between  our stories that we will be delivering.    If you think about my work with psychographics, the connection is clear.  We determine the profiles with quantitative data, but the spirit of the questions (and many of the other questions in our study) are qualitative.   Going further with ethnography, effective listening, and broadening your mindset about data and information will unlock even more value potential. The more you can get comfortable with squishy ideas, the more prepared you will be to deal with the vagaries of customer behavior.  The more open you are to learning from customer interactions--and empowering those closest to the customer to collect and share those insights--the more truly customer centric you will become. Open your morning and open your mind on day 2 of the conference.  You'll be glad you did. There are lots of great reasons to attend #GartnerTGI.  You just got another one.  

            • Doing Better in the Healthcare Supply Chain Top 25 by Adding an ESG Metric
              by Eric O'Daffer on June 7, 2022 at 9:00 am

              Maya Angelou famously said, “Do the best you can until you know better. Then when you know better, do better.” Her quote applies to a lot of things, but for purposes of this blog it applies to the healthcare supply chain and a change we are making in Gartner’s Healthcare Supply Chain Top 25 to “do better.” We are adding an environmental, social and governance (ESG) quantitative metric to the ranking for the first time. The goal of this blog is to share the details of the change, and what you can do to align and give feedback to help shape our next steps. As a refresher, last year we made the biggest change in the history of the Healthcare Supply Chain Top 25 by moving to an all-U.S. healthcare provider ranking. Previously, we had also included manufacturers, distributors and retail pharmacies. This move reflected the growing supply chain maturity and scale of health systems, along with improved performance of life sciences manufacturers in Gartner’s global ranking across all industries. In making this change, we signaled that we would seek to add an ESG metric to the quantitative portion of our ranking. How do ESG Metrics Improve Our Ranking? Our healthcare ranking is a compilation of quantitative metrics and qualitative peer and analyst opinions. The new ESG metric will be weighted at 5% of the valuation for each health system, and will be based upon membership participation and active engagement in the not-for-profit organization Healthcare Anchor Network (HAN). Founded in 2017, HAN is a national leader in promoting healthcare supply chain strategies that uplift local, diverse and employee-owned businesses and that encourage prime suppliers to create better quality jobs in high-need communities, creating a natural alignment with a need to account for ESG practices in our ranking methodology. HAN has partnered with a leading national nonprofit focused on environmental sustainability, Practice Greenhealth, to produce an Impact Purchasing Commitment, a leadership pledge in which HAN members commit to aligning their purchasing power to buy from, and build capacity of, vendors that are minority- and women-owned, sustainable, employee-owned and local. We recognize the critical importance of ESG practices in supply chain and are proud to partner with HAN to improve our ranking system with this ESG metric. Historically, ESG has taken a backseat to cost savings. Measuring and incorporating ESG practices into supply chain poses challenges. There are tradeoffs and costs to incorporating ESG best practices just like there are tradeoffs and costs to resiliency. We know many health systems are working hard on DEI spend and sustainability. Leaders like Kaiser Permanente were recognized in Gartner’s Power of the Profession Supply Chain Awards in 2022 for their commitment in these spaces. While your CEO is (or should be) focused on these strategies, certain systems may need time to adapt to these changes. Therefore, the ESG metric will initially be weighted at 5%, with an intention to increase this weight to 15% or more over the next few years. Many health systems represent the largest employers in a geography and could be better aligned to ESG. We hope these methodology changes to our ranking will encourage further resources and investment in these areas. The new makeup of the Healthcare Supply Chain Top 25 ranking components is detailed below. We are taking 2.5% from the peer and analyst opinion section and creating room for 5% allocation to the increasing commitment to HAN. Every health system can participate equally in this portion of the ranking — it is participation-based, and every health system has the chance to earn this 5%. Below are the details on HAN levels and scoring to earn 5%. We want to keep this simple with the three tiers of participation as highlighted in Figure 2 and outlined below: Are you a member of HAN (like 72 other health systems are)? Did you submit third-party Tier 1 supplier diversity data for procurement and construction to HAN as part of its annual data collection? Is your health system a signatory of the Impact Purchasing Commitment, meaning you will double your DEI spend in the next five years, achieve at least four sustainability goals and commit to five-year goals for community wealth building? Every health system in the United States can access points associated with this new metric, even in this first year. In September, HAN will be notifying us of which members have met the agreed-upon criteria this year for our ranking, which will be unveiled on Nov. 9, 2022. Please join us in “doing better” by uplifting the critical importance of ESG in our healthcare supply chains. We are excited about this first step on the journey and look forward to collaborating with all of you on the next steps for the 2022 Healthcare Supply Chain Top 25 ranking. If you have questions on our Healthcare Supply Chain Top 25 methodology and/or would like to be a peer voter this year, please contact me directly at eric.odaffer@gartner.com. Eric O’Daffer VP Analyst Gartner Supply Chain eric.odaffer@gartner.com   Listen and subscribe to the Gartner Supply Chain Podcast on Gartner.com, Apple Podcasts, Spotify and Google Podcasts

            • Human's At Risk - Webinar June 8th
              by Barika Pace on June 7, 2022 at 3:25 am

              Why Discuss Humans at Risk? As the connection between the cyber and physical worlds grows more intertwined, the risk to humans and our environment falls into the laps of tech companies. Technology service providers are confronted with questions about sustainability, safety and even navigating geopolitical disruption. Tomorrow, I will be pleased to host a lively panel discussion with three Gartner experts. Discussion Panel This panel discussion with experts will tackle how technology is placing society at risk and address what product leaders must do to safeguard people and the environment. Join Aapo Markkanen, Forest Conner and Wam Voster as we address your questions, such as the following: How do we build a message around technology for the good? Who in our organization is responsible for product safety and security? How do we address environmental, social and governance (ESG) concerns in our product strategy? What societal and geopolitical factors are at play? Join Us Come ready with your questions. I hope to see you tomorrow! Register Today  

            • DBMS Market Transformation 2021: OSDBMS Advances
              by Merv Adrian on June 6, 2022 at 12:33 pm

              A very frequent topic of inquiries to the Gartner DBMS teams can be stated simply as: "should I consider an open source DBMS (OSDBMS)?" Users have asked about open source in 3.6% of the team's inquiries over the past two years. Our usual answer is "yes, if it's commercially supported and meets your requirements after a POC that tests its ability to perform as required." Users are not the only source of this inquiry, though. Investors want to know if OSDBMS are viable commercially, and vendors considering new opportunities often want to think about using open source as the basis for a new offering. For different reasons, they have the same question: "is OSDBMS a commercially significant market I should be interested in?" Is OSDBMS a market juggernaut? Seemingly not. But there is a huge amount of "hidden" money here. Among vendors generating more than $35M in revenue in 2021, 13 primarily offer a commercial product based on an OSDBMS along with a community edition of it: Aerospike, Cloudera, CockroachDB, Couchbase, Databricks, Datastax, EDB, HPE, MariaDB, MongoDB, Neo4j, Pivotal Greenplum, and Redis. Collectively, those who offer this one DBMS represented at least $3.1B in 2021 - 3.9% of the $79.5B market. Over half of that revenue comes from two of the vendors: Cloudera and MongoDB. Neither can realistically be considered aggressive advocates for the open source, community version of their flagship DBMS offerings, but both do offer an open source version. MongoDB Community is the source-available and free to use edition of MongoDB. CDH is Cloudera’s 100% open source platform including Apache HBase (not all Cloudera revenue is attributable to the DBMSs, but Gartner lists it there.) HPE also supports HBase, but recommends its customers use the HPE Ezmeral Data Fabric Database with the Apache HBase APIs. Few other vendors offer products based on these two, though API support is more widespread. Another modest slice of market revenue comes from numerous other small vendors with their own versions of the Big4 favorite open source DBMSs: Cassandra, MySQL, Postgres and Redis, and others with another, less broadly known OSDBMS. We can consider them the pureplays. But there is much more revenue that is hard to quantify. The cloud service providers (CSPs) are DBMS market behemoths and offer their own versions of OSDBMSs. AWS markets RDS for MySQL and Postgres and Elasticache for Redis and Amazon Keyspaces (for Apache Cassandra). Google has Google Cloud SQL MySQL and Postgres, and Memorystore for Redis and Datastax's Astra (based on Cassandra). Microsoft offers Azure Database for MySQL and Postgres, AzureCache for Redis and Azure Managed Instance for Apache Cassandra. Oracle offers a Community Edition of MySQL, but does not disclose the revenue from its commercial version. Oracle has upped the ante with its high-performance Heatwave offering, now in its third release with built-in machine learning; it uses the MySQL API but is not open source. In the IBM portfolio, there is IBM Cloud Database for Datastax, MySQL, Postgres and Redis. IBM Cloud Pak for Data offers OSDBMS options as well. Like the Enterprise versions from commercial vendors, the CSP products often add some special sauce that extends the community version - in their case, it's cloud native features that leverage their storage engines, their control of the stack, and increasingly their ability to share governance and even semantics with their other offerings. And, as I discussed in my earlier post about nonrelational DBMS, for OSDBMS CSP revenue may well exceed all the independent players' revenues combined. CSP revenue for OSDBMS may well exceed all the independent players' revenues combined. Overall, the OSDBMS revenue story, muddy though it is, continues to be one of steady growth, appearing in more of the DBMS landscape every year. Open source is succeeding by itself and as a component of enhanced offerings. It influence and impact will continue to grow in the years ahead as more of the data management stack is disaggregated and slices continue to be replaced by open source offerings.

            • Turbulent Times Turning Toward Global Recession ?
              by Mark P. McDonald on June 6, 2022 at 8:45 am

              Executives are leading in a time of turbulence in its truest sense of the word.  Turbulence in terms of the uneasy or unsteady movement of economic and socio-political forces. Turbulence Factors Navigating turbulence requires acknowledging the multiple and often contradictory factors at play.  These include, in no particular order: Inflation at 40-year highs, initiated by supply chain disruptions, exacerbated by the invasion, and accelerated by recover from the pandemic Rising interest rates, coming off historic lows to curb inflation via traditional monetary policy, as well as the end of quantitative easing. Supply chain disruptions, driven by world events: the pandemic, Russia’s invasion of Ukraine, the realignment of global trade arrangements Food production and supply, as the Ukraine and Russia are major food exporters, this not only drives inflation, but it also increases political and economic instability, people who cannot feed themselves or their families take action. Tight labor markets, as companies look to hire in response to greater demand created by recover from the pandemic, particularly in the services and traditional ‘blue’ collar jobs. Strong U.S. Dollar, supported by a combination of the dollar as a haven in turbulent times as well as rising U.S. interest rates Lower stock market valuations, as rising interest rates and turbulence have shifted investment patterns and predict future economic conditions. Other factors, the shift to more renewable energy, social justice and equity issues, gun violence and rising crime – particularly in the U.S. all contribute to turbulence I am sure I am missing something, apologies.  There is no consistent theme, no clear direction, no easy fix – that is the definition of turbulence. The inability of simple solutions, like raising interest rates and other actions, will most likely turn turbulence into a recession. The stock market has been the best predictor of future recession.  On that basis it looks like we are headed for one, not only in the U.S. but more globally. When, how long, how deep is anyone’s guess. Navigating Turbulence If the past is any predictor of the future, we can expect to see the mood evolve from dire predictions of protracted downturns, toward the lionization of business and political leaders who ‘show the way’, and a turn toward highlighting positive news that are signs of a recovery. What will be the subject of that cycle? How should that cycle influence the actions leaders need to take today and next year? Who knows? Here are few thoughts, conjectures really, that are likely to be wrong in fact but less wrong in their sentiment.  I really welcome your comments and thoughts. The ongoing transition toward an information-based economy. Information and insight are the tools to navigate in turbulence and the basis for taking more powerful actions in a recovery. The information transformation is centered on customers and internal operations via digital technology. That focus will expand to include information created outside of the company changing the balance of power and policy in the future. Access Rules discusses these issues from one perspective. Customers will continue to rule. Businesses will not be able to put customers back in their place as consumers. Consumers buy what businesses sell as demand exceeds supply. Current supply chain issues and shortages are real and significant, but over the longer-term companies with the best customer value can be expected to win. Global trade will change from market-based to partner-based or friend-shoring relationships. It is easy to call form the end of globalization and repatriation of supply chains. Global trade as we knew it will evolve away from open markets toward more exclusive partner/supplier relationships to secure supply chains and stabilize prices and product availability. Resource realities, economic efficiencies, political and other factors support continued trade between nations. Sustainability will remain on the agenda. Sustainability issues transcend individual companies, countries, and societies. The impacts of environmental, social, and other issues are self-evident with significant human, social and political consequences. I might be pollyannish here, but these issues cannot be set aside in the face of near-term turbulence. There are no easy answers here which leads me to my last thought. Political instability will increase, at least over the next 2 – 5 years. This goes beyond political discord and culture wars. Hunger is instability as food insecurity increases due to climate change and Russia’s invasion of the Ukraine. National competition for resources will hopefully not lead to aggression, but it will create instability. Addressing sustainability will require changes in views of national sovereignty and collective action. Global, bi-polar, multi-polar, mono-polar, who knows, but it will be different. Honest dialog is needed There are no simple answers to the turbulence we face. Complex challenges do not resolve themselves in a single, simple answer. Dialogue, understanding, and action are major parts of any answer so: What do you think? How are you considering navigating the current turbulence and high probability of a global recession? What are your considerations for the future?