developerWorks: This is a developerWorks podcast. I'm Scott Laningham joined by Dr. Vladimir Bacvanski, Founder and Vice President of InferData, an IBM® Business Partner that does training and consulting in advanced software and data technology. Vladimir was awarded the title of IBM Information Champion.

He's here to give us a sneak peek into his virtual tech briefing on best practices with data modeling that will be coming up Thursday, May 13th. Thanks for doing this, Vladimir.

Bacvanski: Oh you're welcome.

developerWorks: Vladimir, with data just exploding exponentially from year to year, I'm assuming data modeling is just more important than it's ever been.

Bacvanski: That is correct. And we noticed one interesting trend, which is that the data modeling is not an isolated island anymore. We see that the different enterprise technologies, the importance of enterprise architecture, require a greater involvement of data architects. And without proper tools, their work cannot be particularly efficient.

What we also see is that when it comes to tools, we witnessed the emergence of some new tools. Some of them are accelerators, they enable us to do faster things that were difficult and just slow before. And we also have the tools that are enablers, tools that enable us to do something which was rather difficult, or so difficult it was an obstacle for doing before.

And one of these tools that I'm particularly fond of is the InfoSphere Data Architect, which utilizes not only data modeling capabilities, but also, as a part of the Eclipse ecosystem, can use a variety of other plug-ins, and provide for excellent integration platform for a variety of other IBM and Rational® InfoSphere tools.

developerWorks: And just so everyone knows what we're talking about, what are you doing when you're modeling data exactly?

Bacvanski: So, when we are modeling data we are working in several areas.

One area is an area that is popularly called the logical data modeling, where we are focusing on a platform-independent representation of data. We are not focusing on [a] particular database, but we are focusing on understanding the data and having our data systems be closely aligned with business needs.

The other aspect is dealing with physical data modeling, where we are trying to create optimal design for our databases. There we are working with [a] specific technology — in our application, this is mostly DB2®, but other systems are also supported. And we have a transfer of this knowledge from the problem domain into engineering details or having efficient data store.

developerWorks: Now, talk a bit, if you would, about your roadmap for this technical briefing next week. What are you planning to cover there?

Bacvanski: I am planning to cover a variety of best practices that will address how to productively use InfoSphere Data Architect. There are several ways how you can apply such [a] modeling tool, and it is interesting when people start using it, or if they migrate from some other tool, these best practices are not obvious.

And in this talk I will highlight a number of best practices that I think everyone should be doing, and these practices will allow modelers to work faster, with less friction, and with the technology and with other team members, and simply allow them to be more productive in their work.

developerWorks: Vladimir, how does data modeling work along with Agile development? We talk about that on this podcast and the growing importance of that in order to be swift and adaptable for development teams. How do they work together?

Bacvanski: Well Scott, this is an excellent question. This is an area where we see a lot of push by industry because we see that the data modeling is not such [an] isolated discipline, but now it needs to fit in the whole software development process.

In most organizations today there is a move toward more Agile, more flexible processes, and the data modelers must respond to that by being able to model much faster, and also being able to modify their models as the understanding of the requirement and problem domain changes.

So with the right tools, the data modelers and the DBA's are in position to quickly react to changes that are needed, and that allows the whole project to achieve much higher velocity and eventually gets to the end result much faster.

developerWorks: How fast, Vladimir, is the evolution of this space accelerating? Just your sense of it, you know, what it was like 10 years ago, what it's like now, what you think it'll be like in another 10.

Bacvanski: Interesting question. So 10 years ago, very few people talked about it, and we have seen that the data modeling discipline has a lot of inertia. Now we see that there is a lot of push from the development side, and the data modeling is part of the whole enterprise approach to software development. We feel the need that the data modelers must respond quickly, and without appropriate tools that is very difficult.

developerWorks: Dr. Vladimir Bacvanski from InferData. Thanks for your time, Vladimir.

Bacvanski: Thank you, Scott.

developerWorks: Thanks for listening, talk to you next time.

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About the author

Scott Laningham, host of developerWorks podcasts, was previously editor of developerWorks newsletters. Prior to IBM, he was an award-winning reporter and director for news programming featured on Public Radio International, a freelance writer for the American Communications Foundation and CBS Radio, and a songwriter/musician.

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