Data Management Strategies

Quality doesn’t matter, until it suddenly does

Blogger: Lyn Robison

My colleague Jack Santos wrote in a blog post today, “Lyn Robison (from our Data Management group) loves to rant about how business people put up with bad information from their IT departments.  No doubt. The sad thing is businesses want more.” I think Jack’s observation is true that businesspeople always seem to ask for more information even though its quality is shoddy, but I also think that this situation won’t last forever.

During the 60’s and 70’s, the American auto industry assumed that American consumers would always ask for more cars, even though the quality was shoddy. That observation was true, but it did not last forever. Quality didn’t matter, until suddenly it did.

As soon as someone (the Japanese automakers in this case) demonstrated to American consumers that they no longer had to settle for shoddy vehicle quality, vehicle quality became paramount. Likewise, as soon as a few IT shops demonstrate to businesspeople that they no longer have to settle for shoddy information quality, information quality will become paramount. IOW, if word gets out that the information coming from IT systems need not be garbage, look out. Businesspeople will demand high quality information from IT systems all of the time. And those IT people who cannot deliver it will find themselves in the same situation as the American automakers.

Those IT shops that do not want to end up looking like Detroit (see my "A Crystal Ball shows the Future of IT, and it is … Detroit?!" blog post for background on this) will adopt the approach of the Japanese automakers and start focusing on information quality today. These visionary IT shops will put themselves ahead of the curve and will find their prospects for the future bright and secure.

The Algebra of Data Quality

Blogger: Lyn Robison

A basic algebra formula illustrates the value of data quality. To be sure, it is an illustration or a metaphor more than an actual formula for data quality, but it does demonstrate the need for and the value of information/data quality in enterprises today. The formula is a familiar one:

y = Ax² + Bx + C

In this illustration, "y" is the value that IT contributes to the business. "x" is the degree of data quality, ranging from 0 to 100. "A" is the money that the organization has put into BI systems. "B" is the money that the organization has put into operational systems. "C" is the money that the IT group spends managing complexity and building and running systems.

As you can see, if "x" (data quality) is zero, then the only value that the IT group contributes to the business comes from managing complexity and building and running systems. If x = 0, the value to the business of BI systems and operational systems (represented by "Ax²" and "Bx") are both zero.

You will also notice that when it comes to BI systems, the value is based on the square of the data quality (Ax²). If the data is of high quality, BI systems will yield highly valuable insight to the business. By contrast, if the data is of poor quality, the BI systems will produce nothing but garbage and will therefore be useless to the business.

There are many IT departments out there which are run by IT people who firmly believe that IT’s role is to manage complexity and to build and run systems for the business. In these IT organizations, data quality is not a high priority. As a result, the business derives little or no value from their BI systems and their operational systems. In these situations, the businesspeople are looking for ways to replace their IT groups, by adopting SaaS, cloud computing, and IT outsourcing. Clearly, IT people who believe that IT’s role is merely to manage complexity and to build and run systems are will find themselves competing unsuccessfully against external service providers who can do the job far more efficiently. That is not a good career path for IT people. On the other hand, data quality offers an excellent career path for IT people, because data quality is the key ingredient in the value that any IT group delivers to the business.

I recently read a fascinating article by a senior editor at Securities Industry News that illustrates how data management is no longer optional, especially for financial firms. Here is an excerpt:

… In fact, some firms may eventually decide to designate a chief data officer to manage a team of data specialists, such as data modelers, data stewards and taxonomists. The specialists, in turn, will figure out what data is stored where, verify its accuracy, build data models and create and attach metadata. The data officer and the team would decide who may access what data, integrate databases, and maintain archived data to meet regulatory requirements.

Most firms so far are maintaining the status quo -- keeping different pieces of data in many different databases.

Often, according to Atkin and other data experts, this can amount to as many as 30 different, somewhat redundant databases with discrepancies and errors, as well as 30 or more applications that consume reference data and incoming data feeds from over a dozen data vendors.

Such a hairball for collecting data just won't cut it any more.

"How a firm reacts to the need for accurate, timely, integrated data is now a critical differentiator in a competitive environment," warns Dayle Scher, a senior analyst with Tower Group, a Needham, Mass-based research shop. "Financial firms must be prepared to respond to increased due diligence on the part of investors and certainly more restrictive regulations looming."

After ensuring the data is accurate, says Scher, firms will need to share that data with downstream applications such as order management and portfolio accounting software and staff that need it, in the right formats and in a timely manner…

You can read the full article (entitled “Follow the Data When Tracking Systemic Risk”) here: http://www.information-management.com/news/data_management_risk_grc-10016449-1.html

I have discovered some excellent sources for eye-opening information on the need for data quality, including "Does Your Business Suffer From a Data Quality Reality Gap?", a YouTube "Information Management Fairy Tale", and www.IQTrainWrecks.com, a website dedicated to information/data quality disasters from around the world.

ROI on Data Quality and/or Data Management

Blogger: Lyn Robison

We’ve had several clients ask us for help in defining the return-on-investment for data quality and/or data management for the benefit of their CIO. I struggle with that ROI calculation because it is so basic and obvious to me that I almost can’t articulate the value proposition. It is as though I am being asked, “Can you give me the ROI on doing regular oil changes on our company-owned vehicles?” I am afraid that I would find it difficult to come up with an ROI calculation for that one too.

The value of any information system can be determined by measuring the value of the information that that system provides to the business. If you neglect to do data quality and data management on any information system, the information within that system will eventually deteriorate into a worthless, indistinguishable blob of goo. If the information within an information system becomes worthless, then the information system itself becomes worthless too. Any information system that provides useless information is useless. Like an airplane that can’t fly, it is unfit for its intended purpose.

What is the ROI on not letting a corporation’s valuable assets, such as its information systems, deteriorate? I guess you show that if you neglect any valuable asset, its value will deteriorate to nothing. So, the inputs are the value of the asset today (Vi), and the value of the asset after one year of neglect (Vf).  Then to calculate the ROI, (or in this case the yield, effective interest rate, effective annual rate, or annual percentage yield) you take (Vf – Vi)/Vi. As you can see, if Vf is higher than Vi, you will get a positive yield. If Vf is less than Vi (if the asset’s value is deteriorating) you will get a negative yield.

To do this calculation, you have to be able to quantify Vf and Vi. Coming up with precise, defensible numbers for Vf and Vi can be difficult. And as a result, many CIOs have thought (consciously or unconsciously), “Hmmm, there is no ROI for data quality or data management.” As a result, those CIOs have not prioritized data quality or data management work. Instead, they are passively letting the value of their existing information systems deteriorate to nothing. That approach does not make a lot of sense to me.

This is where information quality metrics come in handy. If an enterprise were to measure the quality (a.k.a. "the usefulness to the business") of the information coming from their information systems over a one-year period, they could calculate the yield (positive or negative) in the value of their information systems. If the IT organization can keep the yield positive, I guarantee that the business will be pleased with the IT organization’s efforts. I also guarantee that if the yield is negative (whether it is actually measured or not), the business will not be pleased with their IT people and will look for any opportunity to outsource or replace them.