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Feature: The Next Wave of Data Management
The search for better data management practices has led to data stewardship and data governance efforts, but confusion over roles and disconnects between IT and the business have led to gaps in cooperation. The next wave will bring customer data integration and master data management initiatives that promise to relieve business experts from the drudgery of defining and maintaining customer data, while developers will dodge the chaos of point-to-point integration.
June 20, 2006
13 Min Read
To say that companies thrive on information states the obvious. Skim the front page, turn on the Weather Channel, watch a press conference or crack open an annual report, and you'll witness the extent to which organizations depend on data. Regulations and increasingly savvy consumers are raising the ante, turning executives' heads, not only to today's data, but back to yesterday's and toward tomorrow's. From the FCC to the SEC to your marketing department, organizations need to know more in order to do more for customers. Requests to report what we know, make smart predictions, store historical records and track the lineage of our data continue apace.
So you'd think we would have mastered it by now.
But as demand for customer information grows, so does the data. The amount of data now captured and stored by businesses nearly doubles every 12 to 18 months, according to InformationWeek. Today's packaged applications are our new legacy systems, and external data is increasingly a de facto component of the information inventory.
Nevertheless, tactics and processes for managing customer data come and go. (Remember CASE tools?) Executives earnestly discuss customer data management in meeting rooms and boardrooms as the data continues to slip through our fingers. Thankfully, companies are beginning to confront the realities of their data management processes--or lack of them--and acknowledge failed attempts.
The search for a better way has led to data stewardship and data governance efforts, but confusion over roles and disconnects between IT and business have led to gaps in cooperation and frustration for participants. The next wave will bring customer data integration (CDI) and master data management (MDM) initiatives that promise a new "single version of the truth" to relieve business experts from the drudgery of defining and maintaining customer data while developers dodge the chaos of point-to-point integration.
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The Mythology of Data Governance
The phrase data governance is one of the most misused in business. IT organizations attempt to institute data governance to engage the business in bona fide data ownership discussions. Vendors throw the phrase around to convey any number of data management practices, from modeling to quality automation. As it's embraced, the term is watered down until--like knowledge management and CRM--data governance becomes a rubric for a hodgepodge of unrelated practices. Eventually, it's rendered a dirty word.
"We'll do it, but we can't use the word governance here," one IT executive told us. "Let's call it stewardship." And therein lies the problem: The vocabulary of data management is defining our very practices.
Unlike management or stewardship, data governance implies a level of organizational oversight that includes both business and IT. More important, it involves executives who are willing--indeed engaged--in defining their companies' data policies, with one eye on internal and external regulations and another on customer-focused strategies. We define data governance as the mechanisms and decision-making structures for treating data as an asset, instituting formal policies and overseeing the management of corporate data.
Data governance is ambitious. The process itself revolves around an executive committee that establishes policies, resolves conflicts and questions, remains mindful of customer commitments and measures success. It is often embraced prematurely by companies lacking the processes or skills to manage their data. Consequently, executives quickly sour on the monthly council meetings, rigorous measurement and accountability metrics required for effective governance. Until legitimate data management and stewardship take hold, data governance is just talk.
Both business and IT executives have come to realize that well-meaning policies like, "Everyone is a data steward" do little to address significant reconciliation, semantic and integration challenges that can make or break customer-facing programs. Having someone accountable for the definition, quality and traceability of customer data is no longer a luxury, but a legal and competitive mandate.
But many companies that rushed to embrace data stewardship never defined the role, thereby introducing a set of roving linebackers into an organization already weary of protracted data modeling and requirements-gathering sessions. The question, "Who should own the data?" lives on in companies that have institutionalized data stewardship, but have nevertheless failed to established its boundaries.
Companies that have implemented, and later retracted, the role of data steward usually have three prevailing conditions:
• Poorly defined job descriptions. The absence of formal job descriptions conveys a lack of institutional commitment to the role of data steward. Companies serious about the role should establish a formal job description, lay out a reporting structure, delimit its business and process domains, understand its key performance indicators (KPIs) and deliverables, define the necessary skill sets and work with HR to sanction the position.
• Reluctant business stakeholders. It would be ideal for the data steward to work on the business side--after all, he or she understands why the business needs the data and how it will be used--but business managers are reluctant to fund the head count until they see the results. Though this may sound like heresy, the data stewardship position should start in IT and migrate to the business only after it's proven its worth. This puts the burden on IT both establish the role of data steward and to detail the tactics and work involved.
• Operational systems off the hook. Some IT departments have been successful convincing business users to climb aboard as subject matter experts--a duty added to an already long list of job responsibilities. Nevertheless they willingly begin establishing data definitions and quality improvement metrics. Meanwhile, the operational systems, typically measured on operational uptime, functional accuracy and response time, continue business as usual, and the owners of these systems aren't responsible for addressing data sharing, accuracy or correction. Thus, subject matter experts, recruited by IT to initial data stewardship processes, lose patience with the lack of change resulting from their efforts and subsequently renege on their duties.
The foundation of data stewardship is to help IT do its job. IT practitioners can't (and shouldn't) understand the content and meaning of customer data elements across the enterprise. IT should enlist business people who are intimate with the desired outcomes of improved customer data to help in these efforts. If executives truly believe--and most insist they do--that data is a corporate asset, they must drive the cultural, investment and organizational changes necessary to make data stewardship work.
Data integration is hard work. Until recently, we relied on extant technologies to address the problem, resulting in taxed platforms, poor system performance and disaffected stakeholders. Despite heavy investments in CRM, data warehousing and packaged applications, we still can't tell whether the person on the phone or the Web site is already a customer.
All that's changing with customer data integration. Simply put, CDI is the real-time automation of data matching, reconciliation and integration processes necessary to avail a single, customer "master" record to the business in a consistent and immediate way.
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CDI is part of the larger master data management practice, which seeks to combine processes and automation in order to integrate data across subject areas in a sustained way. CDI is MDM for the customer subject area, and it's being embraced as the most effective on-ramp for MDM at many companies.
"We've relied too heavily on our incumbent enterprise data warehouses," says Andy Hayler, founder and chief strategist at Kalido, a data warehouse and master data management software provider. "Emerging MDM solutions provide a faster, more automated way to integrate data. People are realizing that they need to take a broader view of information management, and this goes well beyond just building a mammoth physical data store."
Central to the concepts of CDI and MDM is the existence of a hub, which functions as the point of reconciliation for data across a company's heterogeneous systems and applications. The hub communicates bidirectionally, not only providing integrated and accurate data to a range of applications, but maintaining that data as applications update their individual records (see the diagram).
The CDI hub becomes the new single version of the truth for the enterprise. It relieves data stewards from manually defining and maintaining customer data, and frees applications and systems developers from inventing point-to-point data integration solutions in each new development effort.
Your Customer Data Management To-Do List
To improve your organization's customer data management capabilities and raise executive awareness, here are several steps to consider:
• Understand your current data management capabilities. How official are data quality and correction processes? What about metadata management or data privacy and security policies? Who are the subject-matter experts for customer data and do they participate in this work? Inventory the roles and tactics around data management, understand the enabling toolsets and then do a gap analysis and a skills inventory to see what's missing. This approach can build a business case advocating additional investment in data management resources.
• Engage the operational systems owners. Understand their responsibilities and limitations. Be careful about making commitments on future data quality without first securing participation from source system owners.
• Don't assume metadata is the holy grail. The end game is meaningful information, which implies not just metadata, but accurate, usable data. To deliver customer data to the business, data analysis, error detection and correction should be regular tasks. Only when these steps are addressed, in collaboration with subject matter experts on the business side, can IT deploy metadata with sustained value.
• Research existing IT governance. Far more companies have formal IT governance frameworks than have data governance, and the topic is top-of-mind with CIOs. This may be an opportunity to piggyback a new governance effort, instituting data as another leg on the IT governance stool (along with architecture, platforms and investment).
The need for rigorous and sustained data management has never been more urgent or more visible. In their effort to retain customers and drive additional revenues, companies continue to emphasize more relevant interactions, refined marketing programs, and immediate recognition and decision making around individual customers in order to improve their experience. Lines of business are thus turning to IT to drive data quality, prove the economies of scale from data integration and data stewardship, acquire solutions for customer data integration and management, and measure the resulting improvements. When data management teams succeed in delivering these objectives, everyone wins.
Jill Dyché and Evan Levy are partners at Baseline Consulting and authors of the new book, Customer Data Integration: Reaching a Single Version of the Truth (John Wiley and Sons, 2006).
It's one thing to understand the strategic value of data, but when your entire brand revolves around information, there's a lot at stake. As the leading provider of research services to a range of industries, from commercial businesses to law enforcement, the very foundation of LexisNexis is information.
As much a technology business as an information provider, LexisNexis offers a comprehensive range of information and solutions. The company's U. S. Legal Markets (USLM) business alone supports more than 3 million subscribers through various entry points such as order processing, contact management and call-center functions.
"We needed to consolidate our data from multiple sources," explains Gordon Schick, LexisNexis' vice president of enterprise information solutions. "The vision was to create a single customer view ... and enable our USLM group to be more effective in the way they treat customers based on an aggregated view of more than 3 million subscribers."
Technically seasoned, the team thought of leveraging its incumbent CRM and data warehouse platforms. It even considered building a customer data integration (CDI) solution in-house. Schick and his team rigorously addressed their business requirements, understanding that their incumbent solutions weren't designed for the real-time subscriber data reconciliation and matching mandated by the business.
In late 2004, the firm selected the Siperian Master Reference Manager (MRM) product. The solution took eight months to deliver and provides an integrated foundation for both operational and analytic processing of subscriber data. It consolidates data from seven different LexisNexis source systems.
The company's new CDI Hub has significantly decreased the time and labor required to resolve data duplication and matching issues, while increasing data accuracy--match rates alone are three times higher than in the pre-CDI environment. Customer-facing staff can now simply access assigned accounts that have been deduplicated and that include all accompanying agreements and contracts.
The CDI Hub has also streamlined data migration in support of the company's recent Siebel upgrade. With more seamless access to operational systems, bidirectional data correction and the elimination of duplicate contract and customer records, LexisNexis is getting more from its CRM investment than ever. In fact, Siperian MRM has become the cornerstone architecture for the company's CRM initiative, so it will soon map to new sources and be tailored to meet any Siebel-specific requirements.
With measurable improvement in subscriber data matching and data quality, LexisNexis has reduced the number of lost sales opportunities and has tracked revenue increases to its new CDI capabilities.
"We now truly have a single customer view," Schick says, "and it has more than justified the investment." --Jill Dyché
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