Blog: Jill Dyche

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Our Data Sucks--the CDI Remedy

In which Jill gets away with using the phrase "our data sucks" yet again, explains why data quality is one (of many) components in the CDI stack, and a critical one at that, and starts to lay the groundwork for a topic she'll cover in upcoming blogs: CDI is operational, not analytical..

The more things change, the more they stay the same.

This saying, coined by the French, probably applies more to life than it does to IT, Moore’s law being what it is. But every so often it’s true here too. In 2000 Addison Wesley published my first book—e-Data. While certainly exploiting the “e” that prefaced almost everything in those days, e-Data was less about “electronic” data and more about “enterprise” data. In fact, the book explained how smart companies could leverage data across their various silo-ed systems to make better business decisions. e-Data profiled companies like Aetna, Bank of America, Hallmark, and Twentieth Century Fox, all doing great things with newly-integrated “e” data.

Back then the web was still big news. Business processes and workflow automation were de rigueur. As companies tried to figure out their new e-business infrastructures and protect themselves from hackers, information took a back seat. That is, until companies couldn’t do what they needed to do with their data warehouses/CRM systems/packaged applications. Then they collectively turned their heads toward data. And they didn’t like what they saw.

Problems with data—data quality, to be specific—turned out to be the most underestimated barrier to the success of these and other strategic systems. As IT analyst firms spewed statistics on CRM failures and CIO surveys bemoaned the high cost of implementation efforts, bad data became the proverbial albatross of systems integration projects. “We didn’t know out data was dirty!” was the surprisingly surprised refrain from IT practitioners whose missed deadlines and cost overruns were attracting the attention of executives.

Enter Customer Data Integration (CDI). While there’s still a lot of noise around the topic, three items about the emerging trend of CDI are definitive:

1. CDI takes the data warehouse one better in terms of enforcing a “single view” of the customer. It is the authoritative system about your company’s customers, and it's operational, meaning it processes and propagates data to the systems that need it.
2. As such, CDI is broader than customer analytics. It’s often recognizing customers in real-time, and deploying that information back to other systems and applications as it's needed.
3. Given the business problems CDI solves, data quality is “baked in” to the core functionality. This is why CDI tools are operational, and processing-intensive. Data matching, reconciliation, and standardization is hard work.

Consider the culmination CRM, data warehousing, real-time processing, and data cleansing rolled into a single solution that provides the so-called “golden record” about each customer. The sum of all its parts, CDI must enforce data accuracy and meaning natively, and continuously. CDI combines these proven capabilities into a new set of business competencies that could have a huge impact on customer loyalty, compliance levels, investment decisions—indeed, on the bottom line.

So, perhaps it’s fitting to turn the French aphorism upside down: The more things stay the same, the more they change. For CDI at least, it’s about time.

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