DQ vs. BI

A long-time IT professional friend of mine and I recently had a very interesting discussion about the merits of different technologies. He works in the business intelligence (BI) space for a major financial institution and, as part of his job, has to oversee the implementation of different BI tools for his organization. In this post, I want to relay the crux of that conversation because it relates to organizations’ IT procurement priorities.

For any company in any economy, resources are scarce. There are limitations to even what cash-laden organizations such as Microsoft and Google can do. So, when faced with decisions related to data management, does one set of tools make sense for an organization?

Organizational Data Maturity Model

In his excellent book The Data Asset, Tony Fisher lays out the following model for organizational data maturity:

The model underpins his book and, in essence, can be summarized as follows: Organizations cannot leapfrog stages. Undisciplined organizations need to become reactive before progressing to proactive and governed states.

So, returning to the conversation with my friend, is DQ or BI more important to an organization? The answer, of course, is that it depends on the state in which the organization finds itself.

For example, consider Company X, an undisciplined organization barely able to keep the lights on. Data management is nonexistent and duplicate, incomplete, or inaccurate records make even basic reporting and operations difficult–if not impossible. As a result, a BI tool may yield insights into employee, customer, and/or vendor behavior. But will those insights be accurate? Doubtful. In this case, a DQ tool makes a great deal more sense. Clean up data and begin to instill a culture of data governance before getting all fancy with OLAP cubes.

Now, let’s look at Company Y, a proactive organization with very few data-oriented issues. While not perfect, master records are maintained quite judiciously and, unlike Company X, basic reporting is a breeze. Few people internally ever doubt the accuracy of reports and analyses. As such, it is ready for a BI tool. To be sure, a DQ tool couldn’t hurt, but Company Y is ready to take the next step.

Simon Says

Don’t look at the merits of BI and DQ tools in isolation. The state of your organization’s data management is an incredibly important factor in making the decision about which tools to deploy–and when. Further, resist the temptation (often driven by senior executives) to go with “quick fix” sexy BI tools when your organization isn’t ready. Yes, they are capable of producing interactive charts, reports, and dashboards. But that’s not the right question. Rather, are all of these potentially useful given the state of your organization and its data? Remember that better access to–and charts containing–bad data aren’t as useful as limited access to pure data.

DQ tools may not have the sizzle of their BI counterparts. However, if they are purchased, implemented, and utilized effectively throughout the organization, they sure will make BI tools more potent when you get there. The best organizations have a method to buying and implementing different tools.

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