Manage Learn to apply best practices and optimize your operations.

Customer data integration: Reaching a single version of the truth

In this excerpt from Chapter 6, "Who Owns the Data Anyway? Data Governance, Data Management, and Data Stewardship," authors Jill Dyché and Evan Levy discuss the rampant corporate conversation of data ownership, and the oft-cited but little-identified phenomenon of data hoarding.

The following is an excerpt from Chapter 6: "Who Owns the Data Anyway? Data Governance, Data Management, and Data Stewardship", of Customer Data Integration: Reaching a Single Version of the Truth.



Sturm und Drang of Data Ownership

It's the single most prevalent question we hear from our clients, prospects, and conference audiences: "Who in the company should own the data?" Moreover, when we ask people about who owns the data in their companies, the answers are tentative, as if the respondent had never considered the question. However, the lack of a clear, concise answer to the question of corporate data ownership has daily consequences. Consider these three scenarios:

  1. The Marketing organization at an automobile company won't share its data. With anyone. Ever. The data mart contains important strategic information like the customer segments who responded most frequently to high-profile campaigns, and what the return on marketing costs were. The Product Design organization would like to see the data as they target certain demographics for new model features. But Marketing won't budge.
  2. An international bank we work with recently assembled a Compliance SWAT Team to examine the various operational systems that could provide loan data for support of Basel II. (Basel II regulation dictates that financial services companies must understand and communicate their corporate exposure when they make loans to certain parties.) The company's loan origination was homegrown and had been in place for almost 20 years. It contained proprietary codes without supplementary definitions requiring a team of highly specialized programmers to routinely maintain it. Unfortunately this group of specialists didn't have the time or extra resources to help the SWAT Team decipher the proprietary codes.
  3. The Sales team at a semiconductor development company has a rich database of customer contact information. But the sales people are stonewalling. They don't want to share their contacts with other departments, especially Customer Service, which was recently transformed from a cost center to a revenue center. "Once a CSR gets the names at my accounts and starts dialin' for dollars," a sales manager told us, "there go all my commissions." Consequently he and his colleagues are uploading their contacts less frequently to the corporate server as their laptop spreadsheets become richer with customer detail.
  • The phenomenon of data hoarding is pervasive at most companies. People won't avail their data to a larger audience for fear that they will have to be accountable for fixing it or, by extension, be accountable for the decisions they've made based on data that's essentially inaccurate. Business organizations, already overwhelmed by their data volumes, see sharing data as a hindrance to getting important work done. It slows them down. More insidiously, some embrace the philosophy that "knowledge is power" and consider their data as political capital.

    And some organizations haven't been staffed sufficiently to maintain the data. They don't have the resources to support the sharing, management, and correction of the data. As we'll discuss later in this chapter, data stewardship costs money.


    This is certainly a barrier to Master Data Management (MDM) initiatives, since you can only truly manage data you can access. However, underneath the "we can't get the data" issues is a cultural awareness of the impact of information on the business' bottom line.


    The challenge is to stop thinking about data in the way we've been thinking about it. The flow of data is not linear—out of one system and into another—as we explained in our discussion of the corporate data supply chain in Chapter 1. Data flows across systems, often multiple times. And the administration of data is not an isolated function, confined to applications or individuals within various departments who are responsible for looking at a small but redundant subset of a company's data through a tiny colored lens. The challenge is to start thinking about and treating data as a corporate asset.


     Truth About Managing Data as An Asset


    Nowhere is this truer than with customer data. Indeed, our esteemed Foreword-writers, Don Peppers and Martha Rogers, have advocated that customers themselves should be considered assets to a company. *


    It's helpful to consider the definition of the word "asset" here. Webster's defines an asset "a valuable item that is owned," but in general an asset has four qualities:

  • It has value
  • Its value can be quantified
  • It helps a company achieve one or more of its strategies
  • There is an awareness of the asset's importance among company management and employees

    For example, a retailer's inventory is usually considered an asset. A bottle of shampoo on the shelf has value—the retailer has paid for it and a customer will hopefully buy it. The shampoo's value can be quantified, since it has a cost and a purchase price. And the shampoo can definitely help the retailer fulfill its strategy of generating revenue. Other examples of corporate assets include a company's stock, its fleet, its cash, its knowledge, and its real estate.


    If a company believes its customers are indeed assets, information about them should likewise have value. And it does. Many companies have quantified the value of their data in different terms, but most often:

  • The data's contribution to revenues and profits
  • The data's role in enabling efficiencies and cutting costs
  • The opportunity cost of not having the data

    It's not hard convincing executives that data is an asset. In fact, many already use that vocabulary when describing success factors of critical projects. But it's a bit more challenging to get executives to step up to the plate and invest in their data asset. Sometimes if we're friendly enough with a Chief Information Officer (CIO), we try the following test. We ask her if she considers her data to be a corporate asset. Most of the time she'll agree that data is indeed a corporate asset because "it's very important to our business."


    We then ask, "Does that mean that you're investing in data proportional to your other corporate assets?" You can usually cut the silence with a knife.


    Here with a sobering exercise to share with your executives, preferably your CIO. Give the CIO 2 points for every "Yes" answer, 0 points for every "No" answer, and -1 point for every "I don't know."

  • Is the company giving data the resources comparable to your other corporate assets? ___
  • Are you dedicating technology to data comparable to your other corporate assets? ___
  • Relatively speaking, is the funding you're allocating to data equal to the funding of your other corporate assets? ___
  • Do you measure the cost of poor, missing, and inaccurate data? ___
  • Do you understand (or have you quantified) the opportunity cost(s) of not delivering data to the business when it's needed? ___

    The issue of investing in data is an interesting one, since many executives immediately go to their comfort zones and begin discussing headcount. The assumption is that the company is already investing in data since it employs some data modelers and database administrators. However, as important as the people issues are, there are organizational and cultural changes likely if management is serious about sustaining data governance.


    The fifth point on the list is one that gets executives to stop and take note, since many companies have made serious investments in strategic projects only to see them scrapped for lack of good or available data. This was the case a few years ago with CRM. At one time, most executives and CRM project managers considered data as an implementation afterthought only to discover that delivering key CRM functionality would be impossible without customer data that was clean, consistent, and useable. The resulting botched cross-selling campaigns, over-communicated marketing messages, customer churn, and abandoned shopping carts cost companies tens of millions of dollars. The opportunity cost of nonintegrated or dirty data can be staggering.


    By way of scoring the above test, any score below 6 usually connotes trouble with information, and that trouble's likely due more to issues of poor data enabling processes or political and ownership issues than it is due to the lack of technology. In fact, these problems are so rampant that we recommend avoiding a foolhardy career move and taking the test yourself before giving it to your CIO.


    * We particularly like the chapter in their book, Return on Customer (Doubleday, 2005) called, "Violate Your Customers' Trust and Kiss Your Asset Goodbye."


Dig Deeper on MDM best practices

Start the conversation

Send me notifications when other members comment.

Please create a username to comment.