We’re talking about your Business Intelligence (BI) quotient, and it’s the only way to work efficiently in the digital age.
What is Business Intelligence?
Business Intelligence is a combination of AI-enhanced technologies that include data mining, Big Data analysis, reporting, and machine learning as well as the tools and infrastructure that tie it all together.
When designed and implemented properly, these applications provide an efficient platform for the type of data visualization that drives the decision-making process.
For example, a company may want to determine the factors that led to an unexpected spike in sales during a specific period.
Using Business Intelligence-based data and analytical capabilities, they can uncover historic trends and compare them to the influence of current consumer practices that were contributing factors.
That information and conclusions can be evaluated and shared with departments or staff members at the moment via a centralized system.
This will help them better prepare for such conditions in the future and limit business disruption or inventory depletion.
Business Intelligence Improves Efficiency
Whether you make enterprise-wide changes or only want to update certain portions of your business processes, BI will allow company leaders to more efficiently:
- Analyze and predict customer behavior
- Compare data with competitors, historically and in real-time
- Identify ways to increase revenue
- Identify market or consumer trends and respond accordingly
- Track and analyze the performance
- Uncover issues and optimize operations
Implementing business intelligence to any degree isn’t something that should be done on a whim.
Best Practices of Implementing BI Software
A staggering 92% of global companies are increasing investments in Big Data and AI in order to boost their agility and response to market changes, with investment growing steadily since 2017.
It takes careful analysis of how BI can further your company goals. You will need a plan for implementation, re-training or onboarding staff members, and provide a clear set of key performance indicators to measure success.
Security for data storage in-transit and at-rest is also an important element. Before you transition to a data-driven ecosystem, here are some best practices to help you do it right.
#1 Get Everyone On Board From the Start
Your first strategy should be to have all stakeholders involved in the decision-making and planning process.
This will help you understand the requirements, concerns, and readiness of each department at all levels of your enterprise from C-level executives to customer service and sales reps.
#2 Have a Strategy in Place Before Implementation
There are few things worse than investing in technology that doesn’t work in the way that you intended. Even small technological changes within an enterprise can cause a bit of disruption if you enter into the transition without a plan of action.
Rather than overwhelming yourself and your team with the complexity of it all, create a strategic plan that breaks the work down into manageable parts that are implemented gradually.
In this way, any obstacles can be identified and dealt with before moving on to the next phase. Constant interaction and feedback from key players will also alert you to problems and build a foundation for successful deployment.
#3 Create Priorities Before Rollout
How and when you implement new technologies is determined during the planning phase. The next step is to prioritize actions according to the dynamics and readiness of your enterprise.
When analyzing your priorities, consider:
- Which rollout phase will impact your business the most?
- What areas of your business will benefit most from a specific phase?
- Which phases of deployment will be most complicated or require further support?
- Which parts of your business or departments will be able to adopt the new technology the fastest?
- Which portions of your business or staff will have the time and flexibility to work on implementation?
- Will you need to call in a specialist for installation and training or can this be handled in-house?
- Which parts of your enterprise have the current technology or infrastructure already in place to handle more advanced tech?
These will help you determine which areas need most attention and preparation.
#4 Implement the New Applications in Phases
Once your priorities and rollout plan are in place, it’s important to launch your new system in phases that correspond to your initial analysis. This will help ensure a smoother transition.
Don’t try to implement new tech system-wide. Instead, focus on one area of your business at a time.
This will allow you to focus your resources and increase the odds of a successful rollout. Success in one area will have the added consequence of increasing enthusiasm and overcoming resistance to change.
#5 Evaluate and Retool as Necessary
Implementing any new process or concept into your busy isn’t a one-and-done routine. Nothing is perfect right out of the gate, and there will be a few bumps and pitfalls in the beginning.
One of the most important concerns is data integrity, which should be protected at every step.
In the early stages, before final quality controls and deployment, continual vetting and analysis for accuracy are essential for maintaining the faith of end-users in the process.
As technologically advanced as modern business applications are, and as precise as current data analysis has proved to be in relation to humans performing the same function, there is still some mistrust of putting all of one’s faith in technology.
If none of the premade BI systems seem to be good for you, custom enterprise applications might be a better choice. Custom solutions cost more as they will be developed for your business specifically, but it’s a sound investment if nothing else works well, as they will be designed to meet your needs at any stage in the life cycle of your enterprise.
Making sure that your new system is accurate, efficient, and bug-free before rollout will go a long way toward installing trust in the process and outcomes.
Business Intelligence technology is not as complicated as it seems. Modern Business Intelligence supports data-driven decision making by providing insight based on historical and current data.
This will allow you to base daily business decisions on facts gathered in real-time and tempered by deep analysis of past activities and trends.
In the end, it’s better to make the software fit your business than to try and mold your business to some static system that will become unsupported and obsolete within a few months.
Latest posts by Joe Peters (see all)
- 5 Best Practices for Implementing Business Intelligence Software - December 20, 2019