How to implement a BI approach to data analysis: practical recommendations

This article was prepared by Sergey Shopik, director and founder of the Client Experience Lab. Based on material published by Martha Bennett on the website of Forrester Research. We invite everyone to June 18 at 20:00 Moscow time. to a free workshop “Visualization from A to Z”. You can sign up here .

Too little data. Too much data. Incomplete data or limited access to it, reports and dashboards that take too long to form, which often do not meet your goals. Analytics tools that only a few trained specialists can use. All of this is a list of complaints from the field of data mining and business intelligence (BI). It is extremely long, and automation, unfortunately, does not serve as a solution to these problems. At the same time, BI has been one of the main priorities for implementation in the organization for several years, as companies begin to clearly recognize the value of data and analytics when it comes to optimizing solutions to get the best result.

So, what can you do to ensure that your BI initiative does not end up in the dump of unsuccessful projects? Finding the answer to this question is not something unusual and complicated, but it will require answers to clear questions and the separation of "grains from the chaff." Quite often, one can hear stories of how multimillion-dollar projects in this area have failed utterly. Often this was one of the following reasons that we will try to figure out.

What is the difference between a successful BI analytics project and a project stuck in a production hell? Studying the best practices of successful projects, the difference may seem obvious, but it is the differences that distinguish those whose BI projects do not meet the needs of the business (or, in principle, fail), from those whose projects succeed.

And so the most important thing: in what category of tasks will we assign a similar project? To corporate IT or to one of the business units whose reporting we want to automate and whose data we want to look at? Usually the whole problem is that the implementation of the project is completely at the mercy of corporate IT without involving business users in the process. At what often this happens precisely on the initiative of the latter - let them introduce it, and then we will press one button and the “analysis” will begin. Not really. The initiative should come precisely from business and business tasks, but not the other way around. Obvious, but complicated thing. How do we do this?

• Form clear objectives: WHAT for me is this dashboard, for what purpose will we take this or that indicator? The bad answer is to appease shareholders / management. ” A good answer is to evaluate the effectiveness of certain actions and, on the basis of this, decide a, b, c.
• Be flexible and do not try to close all tasks “at a time”. The second common mistake is writing the perfect TK. Automate one task, check the result, and move on to the next. Do not try to deploy a large-scale project for a year. Get to the goal in 12 steps - one step per month. At the same time, do not forget about the main goals and objectives.
• Understand the data. Projects do not take off due to lack of data. And this also happens. I recall the case when the project on segmentation of the client base was stopped due to the fact that the sales data in the enterprise’s accounting program was not tied to discount cards! Does this mean that you need to sprinkle ash on your head and abandon your plan? In no case! It only means that it is necessary to link the data and begin to accumulate it, so that later it fits the plan. At the same time, begin to build a system of indicators, which later will be integrated into the processes. Check if all data is available and repeat the cycle.
• Choose tools based on the task, and not vice versa. An old joke: we bought something, and now we are trying to shove our processes there. But it should be the other way around. The tool for BI analytics is selected for the tasks, and not vice versa. It’s good if all departments in the company are aware of the technologies that are used in it. Otherwise, you will have 20 programs in each department, each considers in its own way, but there is no single version of the truth. All you can do is implement the 21st tool.
• If necessary, do not be afraid to resort to external help. There is nothing special to add. It’s normal: to call a consultant or to engage an external team to solve the necessary issue in a short time. Not normal: do it yourself and do it for two years.
• Change management and training is an ongoing process. You can’t introduce something “forever”. The market is changing, goals, indicators, the situation in the company are changing. It is important to keep track of the relevance of decisions and develop them quickly if necessary. In the end, what works for a stand-alone store is unlikely to be fully relevant for a large federal network.

Where best practices are present, by definition there are pitfalls that should be avoided. We have identified the most common among them:

• Using IT in business intelligence seems easier than it really is. Until you have implemented the BI approach to data analysis, you usually have 1 question. After implementation, the number of questions increases tenfold, because opportunities for analysis are many times greater. Along with technology, develop a culture of working with data.
• Refusal to perform their duties after attracting external partners to provide assistance. So, unfortunately, it does not work. An external consultant or team will help you build the process. But the responsibility for him and working with him is yours.
• Focusing on technology development and implementation, rather than change management and training. It’s better to implement a small reporting system on Power BI and effectively make operational decisions based on data than to spend hundreds or thousands of hours implementing SAP and not use its functionality even by 1%, but continue to send tablets to each other in Excel.

Follow these simple rules and you will no doubt succeed. And the transition from decision-making on a hunch, to balanced and digitized decisions using BI-analytics will be as painless as possible. Good luck, friends!