How can you create a successful BI problem solving framework?

Methodology, methodology, methodology, I can't stress this enough on BI projects. I often tell clients we race to the starting line because the goal of my BI solutions are to solve problems and to be used to answer questions. Therein lies the real work. I like lean 6 sigma process modeling with agile methods but ya'll decide which methods work best for you and stick to them.

To develop a successful framework for solving any problem is to undeeatand the problem first. To be precisely, know about the objectives, constraints, resources, the exact issues, and finding the gap. And then write a problem statement also it's important to get that verified with the actual stakeholders. This helps in formation of sow and communicate the real expectation and see if it is feasible.

To establish an effective BI problem-solving framework, it is imperative to initially clarify the purpose of the BI project. Given that BI projects often encompass various objectives, it is crucial to thoroughly comprehend each one. By gaining a comprehensive understanding of all project objectives, we can construct a solution that accommodates and aligns with each goal. This holistic approach ensures that the problem is viewed comprehensively, fostering long-term solutions that address the entirety of the defined objectives.

Define the objective - Understand the strategic goals and key performance indicators (KPIs). Dientify stakeholders. Identify relevant data sources, both internal and external.Choose appropriate BI tools.Develop intuitive and informative dashboards and reports. Train Users.Create a feedback loop with users and stakeholders. and please Document the BI framework, including data models, processes, and governance policies. and Design the BI framework with scalability in mind. Ensure that the infrastructure and processes can accommodate future growth and evolving business requirements.

I totally agree with performing some descriptive and diagnostic analytics to summarise and explain the data. In my personal experience I have seen this part very underrated when we are creating big, scalable and automated BI tools. This helps a lot in understanding what data is saying. You get a deep understanding of business processes, potential goals and pain points. Also, exploring the data visually before formal analysis through charts, graphs, or exploratory data analysis techniques can reveal initial patterns or outliers that might guide further investigation.

After gaining a thorough grasp of the organization's goals and objectives, the next step involves data discovery. It is essential to delve into the available data, discerning its nature and exploring how it can be effectively leveraged to fulfill the established goals. Ensuring that each stakeholder possesses a clear comprehension of the potential and attainability associated with the available data is paramount for informed decision-making and goal achievement.

Now after you have defined your problem clearly, pull off your socks and gear up . Analyze whatever you have in your hand. Start by analyzing for visible patterns. Draw true insights based on this data. Analyse and provide the right suggestions based on Prescriptive, predictive, diagnostic analytics. Use relevant charts to communicate the insights you drew from the data. Understanding data visualization nitty-gritty is also important in this case.

After establishing clear requirements, the subsequent phase involves pinpointing pertinent data to address the current problem. Emphasizing data quality is paramount, as inaccurate data and lacking context can yield incorrect results and misinterpretations. Engage with stakeholders, scrutinize documentation for a comprehensive understanding of the data, and conduct necessary quality checks before delving into Exploratory Data Analysis.

A critical step within the stage of Analyzing Data is to play back to your stakeholders your understanding of that data. You need to validate that you really understand it, and let your stakeholders confirm that this is indeed the right data and the right understanding of it. This not only gives you a solid fundament underneath your solution but will also avoid attacks on the data later on. For example when your BI solution produces uncomfortable truths.

Another factor to consider as part of designing the solution is to consider who are your users, so Persona- Driven BI design to tailor the BI solution to individual personas within the organization. Create a framework that understands and caters to the unique analytical needs of diverse stakeholders, fostering a personalized data experience.

Now that you have analyzed everything you have in hand, your job is to design a concrete solution out of the information you have in hand. Here you create a blue print or an outline on how you want to proceed further with available resources. You meetings have to have various meetings for discussing the requirement. Then we decide the data architecture, process flow and data flow while taking account of scalability.

Adding to the above perspective agile methodology works well while developing big projects.emphasizing iterative cycles, frequent feedback loops, and continuous improvement. This enables quick adaptation to changing business requirements and ensures that the solution remains relevant and valuable. Secondly, enabling self service analytics in BI is important as well. Tools like Qlik sense, PowerBI and tableau has features to perform this as well. These features should be kept in mind while developing. Also, scalability and flexibility of the tool should be kept in mind for future data growth and changing business requirements. It's very important to keep in mind while planning the development process.

While you make the solution to your new found and we'll defined problem it is also important to test the solution before finally handing it over to the end user. Testing involves unit testing, integration testing, system testing and finally it is essential to gey user acceptance test done by the end users. The benefit of this step is to reduce any possibility of having a bug or defect which can occur due to manual or system or even human error. Afterall it is always good to be sure while you are sharing it with the important stakeholders.

You can not leave the solution after you hand over to the end user and get it signed off. It is very important to be involved in regular sessions on feedback and maintenance. An ongoing support is what will help you with eternal relationships with the cilustomers. The training sessions and training material is slso essential with the rest of the documentation.

The success of a BI program would however be best gauged in terms of the consumption or actual utilisation by the end users to gather insights and deliver value to business. Too often we are saddled with cutting edge tools and technologies and plethora of reports that donot justify a return on investment . Hence continuous consumption analysis is essential which might lead to revising the problem statement. This is specially important given the current scenario and our pandemic driven experience.

As rightly pointed out by Moumita at any given point access control and data governance is key. Rule out some dos and don't. Some basic layers of access to be maintained to ensure the quality and security of the data in the application.