InetSoft Webinar: a Single BI Tool Architecture

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

Number one, this is very proprietary, where the old architecture is very modular and you got Informatica and Oracle and Business Objects and lot of tools, lots of teams. These tools essentially integrate, store and present data all from the same tool, so it is a single BI architecture. That proprietary approach is very efficient. Sometimes you have one team, and in some cases one individual that is able to do a lot of this work very, very quickly.

Second is the notion that you don’t have to build the data model first. You don’t have to define things as dimensions and measures and hierarchies first. It’s just columns. We can just point at different data sources, mash them together and create an analytical view, and that’s how a lot of people are using our BI platform.

This is probably the primary use case of data discovery, the rapid prototyping. Now once you mash those data sets together, don’t get me wrong, the in-memory performance layer is nice, and that brings you that second use case of unfettered drilling. Now you have this built-in performance with no hierarchical drill path, so the analyst can do the unfettered drilling and really explore the data and find stuff that’s really interesting in it.

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And this is where lot of the diagnostics, root cause analysis type of questions, and that style of analysis is being done. And let’s not forget about that intuitive navigation. People really do like the interactive visualization capability quite a bit. So all three use cases I think are compelling and are driving the adoption of the data discovery architecture, but I think it’s primarily the first one is the real killer app so to speak.

Now let me talk about the right hand side of this slide a little bit and the different vendors that are out there. So early on, we talked about the notion of the peanut butter and jelly effect of combining visualization and memory, and really that’s probably not a good metaphor for our European listeners out there, but peanut butter and jelly means that the two things go well together.

By combining visualization and memory together we have a really strong framework. And I would say we have been able to win a lot of deals by creating a very innovative and new way of doing BI that just didn’t exist before in the old architecture for those three use cases you see on the left. We make this whole process even easier, doing the data mashup or data blending.

In essence when you have simpler data sets for sure, but I think it appeals more to an autonomous business analyst. A lot of analysts give us credit for pioneering in this space. Now when you go down in the middle of the right hand side, you see what is called search-based data discovery.

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And this space hasn’t taken off nearly as quickly, but this notion of using a search index to blend different data sets together to give you the performance against large amounts of data and to provide that very intuitive navigation experience. Endeca, which was acquired by Oracle, I think has had some strong success.

And I think we are going to see this as a slightly different use case than the visualization and memory case, but the real benefit is going to be the ability to blend not only structured data sets together, an Excel spreadsheet with a relational database, but also more unstructured elements together. And I think this is going to be an interesting sector of where data discovery goes.

Right now it’s kind of interesting. We are one of the disrupters disrupting the status quo. As we need to analyze a wider variety of data sets other than just structured or relational systems, and whether people are using search-based or some other technology that handles unstructured information, that could potentially be disruptive. It hasn’t hit mainstream yet. I don’t think it’s seeing mainstream adoption.

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