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How to Create Data-Driven Business?

What if the solution came to fruition through a self-service data analytics platform for employees?

Modern businesses are typically overloaded with large volumes of heterogeneous data from multiple sources.

They invest heavily in technologies and architectures such as data marts, data warehouses, data lakes with sophisticated business intelligence, analytics and predictive systems.

Nevertheless, data and technology do not guarantee better decisions unless an additional component is added to this assortment: the data culture.

“Data-Driven” involves much more than just technology and data. This implies that it takes a special mentality – a leadership attitude. This allows your business to make informed decisions by consistently using data to improve business performance.

The data-driven culture

We see a lot of companies struggling to put in place what is called the “data management culture”.

In many cases, even companies with sophisticated data warehouses failed to integrate key professionals into the data analysis loop, which jeopardized the initiative because of the lack of preparation and lack of adoption by leaders or specific teams.

To drive a data-driven culture, businesses must first establish a stream of clean, accurate, reliable, and active data that reflects all of the company’s core businesses. All of these qualities are essential for employees to trust data, technology and tools – any limitation may be the only point of failure in achieving the data-driven vision.

Assuming the basic principles are there, employees must begin to feel the benefits of being data-driven. It is important for them to understand that by using the data, they can improve their decisions. Their jobs can be more effective and have a significant and measurable impact on the entire company.

Here are several ways to push employees in this direction:

  • Share information and knowledge about data models, tools, reports and ideas through quick start sessions.
  • Systematically share interesting points of view and data results with possible follow-up to ask for interpretations and ideas/proposals on how to make them workable.
  • Share real-life success stories about how data usage has led to smart decisions with real financial gains for the business.
  • Share the company’s vision and data strategy. Ask for ideas, proposals, and requests.
  • Request feedback on the overall data-driven experience, and capture needs and requests for new reports, new data points or new data analysis capabilities, new data models, and new visualizations.
  • Run mini-hackathons with known patterns, hidden in your data (artificial or real) and ask teams to discover them.
  • Organize structured training sessions on data analysis and reporting tools.
  • Make data analysis a must for all business proposals, ideas and business cases.

The self-service shop insights

Each employee must be able to easily understand the company’s performance, competitors, local and global economic environment and market dynamics.

Employees must become accustomed to consuming well-defined (and well-designed) scorecards that show the company’s performance, trends, social cues, and other points of view, as well as the results of business decisions.

As employees become more aware of the data and the complexity of the data increases, a new class of systems is needed: a self-service platform to enable users to exploit, analyze and visualize their own information, submit questions, consume dashboards and predefined reports, dynamically generate reports via simple user interfaces, easily define their simulation scenarios, and get instant answers.

Although typical data warehouses and data lakes target specific categories of users (such as analysts and data scientists), self-service will be open to all authorized employees – no special skills required.

It is an easy-to-use data analysis surface (used by a range of data technologies, analysis engines and reports) where ideas and analyzes are presented in a modular form as Active, reusable widgets, and related report items.

This self-service knowledge store could take the form of a configurable and scalable dashboard system based on a scalable range or predefined active preview widgets. This could eventually become a repository of ideas where new widgets are deployed and become usable by employees through custom dashboards and integration scenarios: employees can easily reuse these widgets and combine them into dashboards personalized and appointed that best meet their business needs.

Personalized experiences

Interactive data preview stories provide a modern means of “wrapping up” and communicating interesting data results and models across the enterprise. Whether generated manually or automatically by the AI, data stories can present a particular aspect of the business, the impact of certain decisions or the results of online experiences on a large scale.

Emphasis is placed on the user experience so that the story is presented in a fluid and interactive way with multiple dimensions and entry points, taking into account the point of view of particular users according to their role, seniority and their department within the organization.

Experience with smart buildings

Smart Buildings could be directly connected to the self-service knowledge store to access “whitelist” widgets (suitable for indoor exposure) and visualize business knowledge through the network of screens in the buildings of the enterprise.

This experience could be contextual (to space, seasonality, team) and bring relevant ideas to the right users at the right time. For example, public displays in the sales team field present business performance indicators while in the IT field, they experience network usage KPIs.

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