InetSoft Webinar: Information Architecture; Enabling Data Drilldown

This is the continuation of the transcript of DM Radio’s program titled “The Consumerization of Business Intelligence: How and Why,” recorded on April 18, 2017.

Eric Kavanagh:  Okay folks, we’re back here at DM Radio, talking all about the consumerization of BI.  It's BI for the masses.  That was the term used way back when, and it's here, it has finally arrived.  I don’t think there is any doubt; it's just all over the place.  But that doesn’t mean that there isn’t still a whole lot of work  ongoing to enable the data layer underneath all this nice UI type stuff that people play around with. 

So let’s get some recommendations from our experts on the panel about how you can provision the right level of data, the right amount of data and enable the right level of drill down.  So Byron, I will start with you.  So now what we are really talking about is information architecture, right, like the different layers of information architecture and how you get there?

Bryon Igoe:  Certainly, certainly.  And it's a very complex situation.  The best advice that I always give to our customers is, do not try to boil the ocean.  You are just not going to get it completely right the first time.  Gone are the days where you go away for six months, build a data warehouse, come back and you are done.  Many data warehouse implementations fail for exactly that reason, people think that they have the single version of the truth and that nothing is ever going to change. 

So then they realize that they have to modify this every week, every month, and they keep changing things.  The requirements change faster than they can update their data warehouse, and therefore, they are bound to fail.  And so, if you take a more nimble, more agile approach to the problem, addressing the needs that you have in the moment and not worrying about the big picture, the big picture will develop itself overtime.  So that’s my key point is iterate.  If the decision making process is iterative, building the data infrastructure should also be iterative.

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Eric Kavanagh:  The big picture will build itself overtime, that’s a great line.

Justin Kern:  And I like that agility, it keeps itself open to other aspects, too.  Heaven forbid that the tech industry innovates something, or if there is one person in the IT Department using what they call shadow IT, or something using an application that’s not condoned by everyone.  But keeping yourself agile keeps that kind of enterprise open to changes that will come.

Bryon Igoe:  Agreed, agreed.  And in fact, I don’t know, maybe it's a little bit off topic, but in terms of rules and regulations, we see this in certain government policies sometimes, where we try to prevent things by passing a law.  But it doesn’t actually prevent things; it actually just moves them into the black market.  So by sort of legalizing things and by bringing things out into the open air and shining some sunlight on them, you can actually address the needs and the concerns that the users have.  They don’t have to resort to their Excel spreadsheets if you enable them to do similar functionality using a standards tool.

Eric Kavanagh:  That’s exactly right and so funny because I am actually writing an article on that exact topic right now and something related to Big Data.  I will have to send it to you.  But that’s the exact terminology that I use too, when you try to enforce rules that are too strict, you lose control because you create the black market effect.  It's exactly what you said, and I think that’s just absolutely spot-on.  So let’s see Francois, let’s bring you into the picture.  What about enabling the right levels, provisioning the right levels of data granularity drill down, so forth, what’s some advice on how to do that?

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