InetSoft Webinar: Experimental Business Intelligence App

This is the continuation of the transcript of a Webinar hosted by InetSoft in April 2018 on the topic of "Data Discovery Tools and End User Mashup." The speaker is Abhishek Gupta, Product Manager at InetSoft.

Maybe there are some change requests, and we are supporting this as an experimental business intelligence app in a limited way or in a piloted way. Now, out of those, maybe some of those solutions become quite valuable, and the way we have integrated this data and analyzed it. It’s so useful that we want to publish this as system of record content to a wider audience.

And with each tier, there is more and more rigor and sanctioning of this information as yeah, this is valid consensus information. We just kind of go from conjecture at the bottom to consensus at the top and eventually we get to content that is published and ready to be disseminated.

And I think this is a real key, is real training and impress upon your users and your stakeholders the rule of an engagement that goes along with using information at each level or each tier. You might want to train users to be able to qualify them to build prototypes that can be subsequently promoted to further levels of consensus information.

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So that’s the second key issue on data discovery and the technology. Clearly, the semantic layer based BI platforms that a lot of organizations have been using have been challenged by data discovery. And I think we talked about in detail why they are being challenged. It’s that rapid prototyping capability, it's that unfettered drilling capability, and it’s that really compelling modern looking interface that’s very intuitive to use.

I think it's combination of all three of those, but particularly the data mashup really which gives that rapid prototyping and that agility that folks want that’s just disrupting the classic semantic layer and data warehouse architecture. So as we introduce these tools, I think we want to introduce them in a way that doesn’t throw out the baby with the bath water and keeps some form of governance and consistency.

Because as much as companies may need agility and responsiveness and to see information quickly, they also need to be able to say with authority and confidence that these are the dimensions and measures that describe my business, and this is what we are running on. And those last couple of slides, where we talked about promoting content and having tiered certification on prototype, pilot and production, I think it’s a good way you can have your cake and eat it too where we can deliver that agility and still remain governed and consistent information.

Okay, let’s move on the last key issue. We are talking about the organizational model. And here is a business analytics framework. There is a people process and technology or a platform pillar and people look at this and kind of say okay yeah, there is information management layer, and oh there’s their analytical capabilities layer and that’s on Microstrategy or Cognos or something.

I think this picture is a much more accurate picture where yeah, there is a centralized team of very confident people who know how to integrate and model data, and they’ve got a centralized data warehouse architecture like that first slide I showed you, but there is a whole lot of information being integrated, analyzed, and applied to the decisions in these companies that have got nothing to do with our centralized team.

Whether it's in the sales, service, marketing, HR, finance, and all the horizontal functions, whether it's in different lines of business units or different geographies, there is a lot of analytics going on that are autonomous and independent, and I think this is the picture that we have today. And as sort of a homework for you, I think as you have listened to me for an hour today, is to write what this document looks like for you guys.

I don’t know if it's two or three decentralized teams, six or seven decentralized teams or whether you have dozens and dozens of decentralized teams, but I would like you to get a blank piece of paper and draw this picture. You know, hey, here is our centralized team blending these different skill sets together to do BI centralizing, but where are all our de-centralized teams? How many do I have? What type of skill sets do I have in them because I think the knowledge may vary there.

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