Choosing a BI Tool – Does It Matter?

Blog - Advanced Analytics, Analytics

Welcome to yet another blog post. We are adding an interesting topic: ‘Choosing a BI Tool – Does It Matter?’ to our Analytics Mindset Series. Catch up, if you have missed our previous blog, here:

In that discussion, I made this simple statement:  Don’t argue over BI.  That is, don’t make your analytics project about choosing “the best” Business Intelligence tool. Mostly, I argued, that is a personal preference statement, one that will not ultimately decide the fate of your project. Today’s user experience is mostly terrific in all of the leading tools, providing a rich visual experience that can produce a gazillion chart styles, dashboard templates, complex expressions, and guided data discovery pathways. Thus, trying to convince someone of the superiority of your favorite tool will probably be as fruitful as that argument supporting Android OS – move on mister, not giving up my Apple. The point – find something that fits your talent pool, budget, and licensing model, and get on with the work at hand.

Well, sort of.  You see, this point was made within the larger context of a specific subject, namely advanced analytics. When considered within that topic, amongst all of the decisions to be made that have a large influence on the success of those initiatives, the BI tool does not rank so high.  Indeed, when you consider the data, process, architecture, and algorithmic deployment challenges that demand attention within advanced analytics, this continues to be an entirely sensible standpoint to maintain.

Yeah, like I said – sort of. You see, there are a few considerations that – when taken within the context of your project – should result in some discussion about which BI is best for you. Here are some of those considerations.

Are you pre-packaged or custom?  Are your analytics needs mostly satisfied with pre-built constructs – users turning dials to adjust key dimensions (time, product, geography) – driving them down specific process steps? Or, are your needs more like an empty Bento Box that users want to fill with data from here, there, and everywhere, and then analyze together in a meaningful concoction that only they (and their department) can appreciate? Some might call this use case self-service or data discovery, and as expected most leading BI tools provide for this, but how they do it can differ greatly.

In fact, some BI tools take the modern approach to data discovery as the default journey/view within their user interface, making self-service analytics a core strength. Other tools prefer to maintain a balance between the traditional dashboard construct (pre-packaged model) and the ability to go off and perform self-directed analyses. Having an understanding of which use case holds more importance in your project will guide you to the best choice.

How big is your data? BI tools of yore would, for the most part, write and optimize the SQL needed to return the data requested. That SQL would then be executed against a relational database (most often) and return results – after some time. This approach still applies in some tools today and works well when you have data that spans multiple use cases, domains, or subject areas. The modern approach, however, is to bring the necessary modeled data into memory so that queries run entirely there, avoiding the performance penalty that databases and disk drives often suffer. The memory-optimized model is great as users do not wait long for most queries to return results.  Now, if you have terabytes of data to interrogate, shoving all of that into a single memory model might prove impractical.  Many tools can accommodate both models, but results will differ – so be sure to ask the vendor for real performance metrics.

Stay still, you’re making me dizzy. Well, not literally, but maybe you need to have your analytics running in various form factors: web/desktop, web/mobile, mobile app, tablet.  Most leading vendors claim they support all of these, and that’s true.  But just like we know that store brand cola just won’t cut it in our favorite cocktail (wink), some tools translate better than others in this any device world. If tablet functionality is important, ask the vendor to demonstrate applications in both web and mobile formats so you can be the judge.

Please, sir, I’ll have another.  License, that is. Vendors deploy licensing models that can vary from per user, per seat, per node/core, subscription, and many more. Yes, it can get confusing and costly, especially when you consider named (better for vendor) versus concurrent (better for you) licenses within these models. Get the real numbers around how big your analytics project might become so that you can determine the best approach for you.

So, I confess – there might be a reason to debate BI tools within the context of your analytics project. But please, don’t make this a drag-out knock-down deathmatch thing.  We have bigger fish to fry – such as, pondering whether all this can/should be done in the cloud? Hmm, an interesting topic that one…

This blog was originally published on LinkedIn


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