Future Shock: On the Pros and Cons of Data Modeling
Data modeling, proponents say, can help insulate an organization against change. Sounds good -- but is it true?
In every enterprise IT organization, change frustrates, impedes, and stymies the best-laid plans of CIOs, IT managers, and data warehouse architects alike. What if IT had a way to manage change?
Hundreds of change management solutions abound, but having so many choices does little to inspire confidence. That's one reason why proponents of data modeling are touting a better way to manage change.
Data modeling, they say, can help insulate an organization against change.
That's the strongest form of the argument, of course. At this point, no one's willing to say that judicious or prescient use of a data modeling tool can altogether future-proof an organization against change.
Several data warehousing vendors -- including marquee names such as Kalido and Sybase Inc. -- tout data modeling as a means to help safeguard against change and improve overall business agility. "It's an abstraction layer that insulates you against the effects of change," argues Kalido CEO Bill Hewitt. "You can make some significant changes to the physical implementation [of a warehouse] that only involves really simple changes to the business model, and if you have the right [data modeling tool], you can automate [those changes]."
Kalido has a dog in this race: recently the company announced a major update to its flagship data warehousing platform, the Kalido Dynamic Information Warehouse, along with a new business user-friendly data modeling tool, the aptly-titled Kalido Business Information Modeler.
The company says the latter offering gives business users a much bigger role in the data modeling process. It's a GUI-based environment that graphically displays all parts of a business -- e.g., customers, products, assets, transactions, and even people resources -- in the context of a model.
"It brings business units and IT closer together. It's a tool that's been designed for use for a business unit and IT to collaborate together [to define the model] -- [so] in its graphics and its interface, it's designed so that the business user can intuitively grasp it," he explains.
Kalido's Business information Modeler is a new offering, but Hewitt says many of the company's customers are already using data modeling in some way to help safeguard against disruptive change.
"We have a number of customers who have quite a bit of experience with modeling and the technology behind it. If we take, for example, companies like [some of our customers in] insurance, those companies very much are using the new technology and particularly are using that business model to link together common definitions across different business units."
Hewitt notes that data modeling used properly can genuinely help insulate an organization against disruptive change. Change itself is a constant, he allows. It isn't going anywhere and it can't be eliminated, much less forestalled. Judicious use of a data modeling tool can help ameliorate its more disruptive effects, he argues.
Compressing a Time Scale
"It's not magic, but it's one of the most important and dramatic shortcuts they can take. We're talking about compressing a time scale [adjusting to infrastructure changes] by a factor of ten. It's sort of finessing an adjustment cycle which happens in one or two days, as opposed to two or three weeks."
That's a theme that David Dichmann, senior product manager for Sybase's PowerDesigner data modeling tool, also endorses.
"The traditional bane of the business intelligence environment is that change makes what's already running obsolete more quickly," he said in an interview last September. For this reason, he argues, best-of-breed data modeling will grow in importance as enterprises adopt next-generation application architectures to better insulate their IT assets and business processes from change.
"We don't see the data modeling business as a pure business that's plateaued, but as an essential element to the entire [trend of building out] enterprise architectures. Modern, world-class data modeling is key. Tying that implementation together across every paradigm is key."
Industry experts concur -- to a degree. For one thing, says veteran data warehouse architect Mark Madsen, a principal with consultancy Third Nature and author of Clickstream Data Warehousing, what proponents such as Kalido and Sybase mean by "judicious" use of a data modeling tool takes an awful lot for granted.
"It presupposes that if you have all of your systems' data models in a tool, then changes that are imposed will be easy," he says, citing system changes, upgrades, and merger/acquisition activity that brings new systems into the fold as three among many common disruptions. "That's like saying that because you have a map of outer Mongolia, a trip from one side to the other will be a simple matter of driving."
Madsen isn't entirely dismissive, just skeptical. "I accept that having the data models together and linked will help things like compliance efforts. For example, Visa [requires] that you control access to all databases that contain credit card numbers," he acknowledges, "but that also presupposes that the models are somehow kept up to date, something that is generally pretty unlikely and usually a manual process."
That's assuming, of course, that an organization isn't using a data modeling tool. Most best-of-breed offerings provide replication, synchronization, and other built-in change-management amenities.
On the whole, Madsen thinks more strategic use of data modeling makes sense. "I … believe it will help with M&A [activity] and such, since you have an inventory of what data is where. What is probably missing is 'what data goes where,' which won't show up in a modeling tool unless you are building data flow or UML models of your data and systems integration."