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Data quality: The foundation for business intelligence

CRM and enterprise integration initiatives often stumble at their first step: the collection and verification of accurate and complete customer data. Yet quality customer data is the critical foundation for successful business intelligence. SearchCRM spoke with R. Jeffrey Canter, executive vice president of operations for Innovative Systems, who offered his perspective on how to achieve and utilize good customer data, regardless of who your customers are or what products you're trying to offer. Pittsburgh, Penn.--based Innovative Systems offers a variety of data cleansing, maintenance and householding products, from standard name/address correction software Innovative-Dictionary to advanced customer linking tools Innovative-Find and Innovative-Household.

What's your definition of data quality and why is it such a critical business function?
From a technical standpoint, data quality results from the process of going through the data and scrubbing it, standardizing it, and de-duplicating records, as well as doing some of the data enrichment we talk about with CASS and that sort of postal validation. But more important to me is the emotional response to it, the fact that you have a high degree of trust or confidence in the data that you are using to run your business. Data quality is really the only truly unique asset that a company has. A company can have products, but those products can be and are regularly copied. A company can distinguish itself by its service, but its service is highly reliant upon good data. Data is very strategic, because it's used for both internal and external decision-making. You need that high degree of reliability from and high degree of confidence in your data because it impacts your operation capabilities on a day-to-day basis. What about how that customer relates to your other customers?
That's something that's often overlooked about CRM. You need to understand what those relationships are, because those relationships are your future, your best hope for cross-sell and expanded sale within your customer space. As an example, if I've got a household with customer and his wife, and a separate household with his father, and another separate household with his sister, all with different last names and physical addresses, is there a way to pool that information together to relate those individuals so that I can understand what the potential is for someone in the first household to influence the buying and holding decisions of someone in one of those other, related households? How would you define good customer data, and how do you think companies can achieve it?
The broad definition is that good customer data is data that accurately reflects those individuals or organizations that have some sort of a product relationship with your organization. You must have a single, unique view of the customer across the enterprise, meaning that if the customer holds multiple products, and that information is stored in multiple disparate systems, you are able to pool all of that information together to achieve a single perspective on that customer across all their different product holdings. This isn't rocket science, this has been going on in the CRM realm for a number of years, but really at the base line this is what it's all about. Business Intelligence (BI) is the buzzword of the moment. What do you mean when you refer to BI from a data quality perspective?
When we refer to BI we mean the ability to understand these advanced relationships between customers. BI is built on a foundation of high quality data. Fortunes are made and lost quickly even in companies that are supposed to be outstanding relationship management firms, and these organizations can no longer afford to pay minimal attention to the issue of data quality, because it will be a significant differentiator in the future. We in the traditional data quality space had always touted our ability to do 99% or better accuracy, a major differentiation between our competitors and us. When the data warehousing revolution started, the market took a different view of the requirements for warehousing, and they said, ?well, we're just pouring all of this data together and we don't need to have 99% levels of accuracy, we can be satisfied with lesser quality.? Well, I'm here to tell you - that philosophy is coming back to bite them where it hurts. How does this relate to standard householding approaches?
Standard householding was always taking a look at same surname, same physical address. In recent years, the contemporary view of householding had us recognizing that there are children of a previous marriage who aren't going to have the same last name, so we can do address-only grouping. This goes beyond that, to what we refer to as "super-householding" or "influence marketing." You have to understand the relationships between your customers and those who are not customers of yours, but who may be prospects, in order to be able to get a clear understanding of your potential risks and benefits are, and how to manage those customers more effectively. So many companies are trying to achieve multi-channel status -- where customers can interact with them from many different means. How does Innovative Systems address this issue?
We have callable API products that are deployed in Web server settings now to allow you to enforce data quality standardization rules across the Web. Now that the customer himself is entering in his information over the Web, we have a host of new data quality challenges. We have customers that tell us about getting names of cartoon characters back in the name field, or in the e-mail field, because people don't want to give their real names. Potentially far more damaging than that is the case of people putting profanities into those same fields. Without some sort of filter that information would then going out directly onto a mailing label and going straight out to a customer, with the company's logo right above it. The way that we've addressed that is that we've begun to build knowledge bases of these kinds of words and phrases, cartoon characters if you will, these non-standard categories of information so that our tools can be used to try to intercept some of that before it has a chance of getting out to the customer. What does the future hold for the data quality market?
In general we think the data quality market is going to explode, because of issues connected with customer privacy. My prediction is that you're going to see in the next 24 months a groundswell of public outcry over data privacy because we're really far behind, in this country, with respect to data privacy laws. And managing data privacy, so that you will not be subject to fines and lawsuits is all going to be driven from data quality. You're going to have to know who that customer is, and be able to identify that customer when they're interfacing with you so that you do not violate their privacy preferences.

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