User requirements are paramount for BI tool

In the previous blog, I discussed the importance of having a good business analyst as part of your data strategy. Obviously, this is a role that is critical for the successful deployment of any business intelligence platform, since it is the business analyst that is probably best placed to influence the choice of platform – something which is not always an easy decision.

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For one thing, there is no shortage of applications from which to choose. One only has to look at the 2018 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms to see that it provides an analysis of at least 20 vendors, and this of course does not include the countless others that opted not to pay to make it on to the quadrant.

In my opinion, the easiest way to make this decision is to begin by ignoring the application, at least at first. For one thing, it would be an overwhelming task to compare all the tools available in the market. Secondly, you should remember that any vendor demonstration will focus on the strengths of their particular application. For example, the demos are often done on a simple data source that exaggerates the ease-of-use, which at any rate may or may not be applicable in your specific context.

And while many vendors publish battlecards where they point out those areas they are superior to their competitors, they are obviously unlikely to highlight those areas where the competitor is superior. Ultimately, the vendor's job is to sell product, so they are quite likely to tell you that their application is best for your use-case, even if it is actually a case of trying to fit a square peg into a round hole.

The Gartner Magic Quadrant for Analytics and Business Intelligence Platforms is far better at providing an unbiased comparison of each vendor’s strengths and weaknesses; however, this is merely an overall market analysis, and therefore does not necessarily equip you to make a decision based on your company’s constraints and expectations.

So where does one start? There are several factors that will have an influence on your decision, the most important of these being data structures, user experiences, cloud versus on-premise and, of course, price.

The choice of cloud versus on-premise is, in itself, usually a decision influenced by a number of other factors, including data privacy, the location of other applications, the preference of opex versus capex and price. Meanwhile, price can also be quite a complicated issue, with certain hidden costs and other indirect costs associated with the software. Then, of course, it also depends on your data sources and the availability of an ETL tool as to whether your data structures might end up eliminating certain BI applications.

Therefore, I feel that the most important place to start is with the user experience. This is probably the most difficult set of criteria to answer, but it is also the most important, as it is critical to understand the types of users within your organisation.

These users may vary considerably, from the analyst, who needs the ability to explore the data and drill down, to the dashboard consumer, who needs high level KPIs, but who doesn’t need the ability to investigate further. Then there is the report consumer, who is interested in the detail, rather than the aggregated result, so a business intelligence application that allows self-service or data discovery is wasted on someone like this. And of course, there is also the governor, whose role it is to maintain data governance and security.

Where companies often go wrong is in trying to select a single platform to satisfy all of the above users. Furthermore, some vendors or re-sellers will also pitch along these lines, in order to maximize their sale value. However, in reality, you are unlikely to find an application that provides the best use case for each user type.

In fact, even the users within these user types may well differ. Some users may prefer the data pushed to them and others may be satisfied logging into the application to see the relevant data.

For example, I haven’t met a CEO yet that logs into a BI application to analyse the performance of the business. They prefer the summarized results or exceptions pushed to them, without caring about what tool is doing it.It is thus obvious that your users may have very diverse requirements. They may differ in their levels of skill and knowledge; they may expect a different experience with the application; and the relevance of the data provided will differ across user types and departments. Therefore, if you don’t meet the required experience and educate your users through a change process, the expected user adoption will likely not occur.

It is for reasons like this that a one-size-fits-all approach simply does not work. Satisfying your entire user base is best done through a hybrid approach instead. This could mean two or more tools, which may or may not actually mean more costs, but will definitely mean a more satisfied user base.

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The bottom line is that it is critical to understand your users’ requirements first and foremost, before you actually even think about taking a decision on which tool(s) to implement.