The Data Intelligence Gap: Part Two

In part one, I wrote about the evolution of a corporation and how rapid growth leads to a data intelligence gap. It makes sense that a combination of people, process and technology combine to close the gap, but just what kind of technology can be used to help you cross the divide and connect the needs of business with the data available in the corporation?

Of course, the technology needed depends on the company’s needs and how mature they are about managing their data. Many technologies exist to help close the gap, improve information quality and meet the business needs of the organization. Let’s look at them:

CATEGORY	TECHNOLOGIES	HOW IT CLOSES THE GAP
Preventative	Type-Ahead Technology	This technology watches the user type helps completes the data entry in real time. For example, products like Harte-Hanks Global Address help call center staff and others who enter address data into your system by speeding up the process and ensuring the data is correct.
	Data Quality Dashboard	Dashboards allow business users and IT users to keep an eye on data anomalies by constantly checking if the data meets business specifications. Products like TS Insight even give you some attractive charts and graphs on the status of data compliance and the trend of its conformity. Dashboards are also a great way to communicate the importance of closing the data intelligence gap. When your people get smarter about it, they will help you achieve cleaner, more useful information.
Diagnostic and Health	Data Profiling	Not sure about the health and suitability of any given data set? Profile it with products like TS Discovery, and you’ll begin to understand how much data is missing, outlier values in the data, and many other anomalies. Only then will you be able to understand the scope of your data quality project.
	Batch Data Quality	Once the anomalies are discovered. A batch cleansing process can solve many problems with name and address data, supply chain data and more. Some solutions are batch-centric, while others can do both batch cleansing and scalable enterprise-class data quality (see below).
Infrastructure	Master Data Management (MDM)	Products from the mega-vendors like SAP and Oracle or products from smaller specialists like Siperian and Tibco provide master data management technology. It features, for example, data connectivity between applications, the ability to create a “gold” customer or supply chain record that can be shared between applications in a publish and subscribe model.
	Enterprise-Class Data Quality	Products like the Trillium Software System provide real time data quality to any application in the enterprise, including the MDM solution. Beyond the desktop data quality system, the enterprise-class system should be fast enough and scalable enough to provide an instant check of information quality in almost any application with any number of users.
	Data Monitoring	You can often use the same technology to monitor data as you do for profiling data. These tools keep track of the quality of the data. Unlike data quality dashboards, the IT staff can really dig into the nitty-gritty if necessary.
Enrichment	Services and Data Sources	Companies like Harte-Hanks offer data sources that can help fill the gaps when mission-critical data is missing. You can buy data and services to segment your database, check customer lists for change of address, look for customers on the do-not-call list, reverse phone number look ups, and more.

These are just some of the technologies involved in closing the data intelligence gap. In my next installment of this series, I’ll look at people and process. Stay tuned.

Are There Business Advantages to Poor Data Management?

I have long held the belief, perhaps even religion, that companies who do a good job governing and managing their data will be blessed with so many advantages over those who don’t. This weekend, as I was walking through the garden, the serpent tempted me with an apple. Might there actually be some business advantage in poorly managing your data?

The experience started when I noticed a bubble on the sidewall of my tire. Just a small bubble, but since I was planning on a trip down and back on the lonely Massachusetts Turnpike (Mass Pike) on a Sunday night, I decided to get it checked out. No need to risk a blow-out.

I remembered that I had purchased one of those “road hazard replacement” policies. I called the nearest location of a chain of stores that covers New England. Good news. The manager assured me that I didn’t need my paperwork and that the record would be in the database.

Of course, when I arrived at the tire center, no record of my purchase or my policy could be found. Since I didn’t bring the printed receipt, the tire center manager gave me a couple of options: 1) Drive down the Mass Pike with the bubbly tire and come back again on Monday when they could “access the database in the main office”; or 2) Drive home, find paperwork, come back to store... Hmm. Not sure where it was. 3) Buy a new tire at full price.

I opted to buy a new tire and attempt to claim a refund from the corporate office later when I found my receipts. The jury is still out on the success of that strategy.

However, this got me thinking. Could the inability for the stores to maintain more that 18 months of records actually be a business advantage? How many customers lose the paperwork, or even forget about their road hazard policies and just pay the replacement price? How much additional revenue was this shortcoming actually generating each year? What additional revenue would be possible if the database only stored 12 months of transactions?

Finding fault in the one truth - data management is good - did hurt. However, I realized that advantages of the poor data infrastructure design at the tire chain is very short-sighted. True, it actually may lower pay-outs on the road hazard policies short-term, but eventually, this poor customer database implementation has to catch up to them in decreased customer satisfaction and word-of-mouth badwill. There are so many tire stores here competing for the same buck, eventually, the poor service will cause most good customers to move on.

If you're buying tires soon in New England and want to know what tire chain it was, e-mail me and I'll tell. But before I tell you all, I'm going to hold out hope for justice... and hope that our foundation beliefs are still intact.

New Data Governance Books

A couple of new, important books hit the streets this month. I’m adding these books to my recommended reading list.

Data Driven: Profiting from Your Most Important Business Asset is Tom Redmond’s new book making the most of your data to sharpen your company's competitive edge and enhance its profitability. I like how Tom uses real-life metaphors in this book to simplify the concepts of governing your data.

Master Data Management is David Loshin’s new book that provides help for both business and technology managers as they strive to improve data quality. Among the topics covered are strategic planning, managing organizational change and the integration of systems and business processes to achieve better data.

Both Tom and David have written several books on data quality and master data management, and I think their material gets stronger and stronger as they plug in new experiences and reference new strategies.

EDIT: In April of 2009, I also released my own book on data governance called "The Data Governance Imperative".
Check it out.>>