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Introduction to Unified Data Management

By Philip Russom


In most organizations today, data and other information are managed in isolated silos by independent teams using various data management tools for data quality, data integration, data governance and stewardship, metadata and master data management, B2B data exchange, content management, database administration and architecture, information lifecycle management, and so on. In response to this situation, some organizations are adopting what TDWI calls unified data management (UDM), a practice that holistically coordinates teams and integrates tools. Other common names for this practice include enterprise data management and enterprise information management. Regardless of what you call it, the “big picture” that results from bringing diverse data disciplines together yields several benefits, such as cross-system data standards, cross-tool architectures, cross-team design and development synergies, leveraging data as an organizational asset, and assuring data’s integrity and lineage as it travels across multiple organizations and technology platforms.

But unified data management isn’t purely an exercise in technology. Once it paves the way to managing data as an organizational asset, the ultimate goal of UDM becomes to achieve strategic, data-driven business objectives, such as fully informed operational excellence and business intelligence, plus related goals in governance, compliance, business transformation, and business integration. In fact, the challenge of UDM is to balance its two important goals—uniting multiple data management practices and aligning these with business goals that depend on data for success.

The purpose of this report is to help organizations plan and execute effective UDM efforts. Many need the help, because UDM is a relatively new shift in best practices for data management. Toward that end, the report drills into the business initiatives that need UDM, the data management practices and tools that support it, and the organizational structures that enable the cross-functional collaboration that’s critical to UDM success.

Definitions of UDM

With all the above in mind, here’s a nutshell definition of UDM:

TDWI Research defines unified data management as a best practice for coordinating diverse data management disciplines, so that data is managed according to enterprisewide goals that promote technical efficiencies and support strategic, data-oriented business goals.

The term UDM itself seems focused on data management, which suggests that it’s purely a technical affair. But this is misleading, because UDM—when performed to its full potential—is actually a unification of both technology practices and business management. For UDM to be considered successful, it should satisfy and balance both of the following requirements:

  • UDM must coordinate diverse data management disciplines. This is mostly about coordinating the development efforts of data management teams and enabling greater interoperability among their servers. UDM may also involve the sharing or unifying of technical infrastructure and data architecture components that are relevant to data management. There are different ways to describe the resulting practice, and users who have achieved UDM call it a holistic, coordinated, collaborative, integrated, or unified practice. Regardless of the adjective you prefer, the point is that UDM practices must be inherently holistic if you are to improve and leverage data on a broad enterprise scale.
  • UDM must support strategic business objectives. For this to happen, business managers must first know their business goals, then communicate data-oriented requirements to data management professionals and their management. Ideally, the corporate business plan should include requirements and milestones for data management. Hence, although UDM is initially about coordinating data management functions, it should eventually lead to better alignment between data management work and information-driven business goals of the enterprise. When UDM supports strategic business goals, UDM itself becomes strategic.

Let’s expand TDWI’s terse definition of UDM by drilling into more specific details and issues.

UDM is largely about best practices from a technical user’s viewpoint. Most UDM work involves collaboration among data management professionals of varying specialties (such as data integration, quality, master data, etc.). The collaboration fosters cross-solution data and development standards, interoperability of multiple data management solutions, and a grander concept of data and data management architectures.

UDM isn’t a single type of vendor-supplied tool. Even so, a few leading software vendors (including all the vendor companies sponsoring this report) are shaping their offerings into UDM platforms. Such a platform consists of a portfolio of multiple tools for multiple data management disciplines, the most common being BI, data quality, data integration, master data management, and data governance. For the platform to be truly unified, all tools in the portfolio should share a common graphical user interface (GUI) for development and administration, servers should interoperate in deployment, and all tools should share key development artifacts (such as metadata, master data, data profiles, data models, etc.). Having all these conditions is ideal, but not an absolute requirement. As one interviewee put it: “The tools’ servers have to interoperate or—at the end of the day—the solution isn’t unified. So that’s a ‘must have,’ as is shared metadata. If there are multiple development GUIs, I can live with that.”

UDM often starts with pairs of practices. UDM is a matter of degree. In other words, it’s unlikely that any organization would want or need to coordinate 100% of its data management work via UDM or anything similar. Instead, organizations opportunistically select combinations of data management practices whose coordination and collaboration will yield appreciable benefits. The most common combinations are pairs, as with data integration and data quality or data governance and master data management. Over time, an organization may extend the reach of UDM by coalescing these pairs and adding in secondary, supporting data management disciplines, such as metadata management, data modeling, and data profiling. Hence, the scope of UDM tends to broaden over time into a more comprehensive enterprise practice. And the scope can get rather broad, as a user interviewed for this report explained: “Enterprise-scale data management is like most things: it’s a mix of people, process, and technology. The range of each is diverse, so there’s potentially a place for just about anything.”

A variety of organizational structures can support UDM. It can be a standalone program or a subset of larger programs for IT centralization and consolidation, IT-to-business alignment, data as an enterprise asset, and various types of business integrations and business transformations. UDM can be overseen by a competency center, a data governance committee, a data stewardship program, or some other data-oriented organizational structure. UDM is often executed purely within the scope of a program for BI and data warehousing (DW), but it may also reach into some or all operational data management disciplines (such as database administration, operational data integration, and enterprise data architecture).

UDM unifies many things. As its name suggests, it unifies disparate data disciplines and their technical solutions. On an organizational level, it also unifies the teams that design and deploy such solutions. The unification may simply involve greater collaboration among technical teams, or it may involve the consolidation of teams, perhaps into a data management competency center. In terms of deployed solutions, unification means a certain amount of interoperability among servers, and possibly integration of developer tool GUIs. Technology aside, UDM also forces a certain amount of unification among business people, as they come together to better define strategic business goals and their data requirements. When all goes well, a mature UDM effort unifies both technical and business teams through IT-to-business alignment germane to data management.

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Related Terms and Concepts

Most likely, you’ve never heard the term “unified data management.” After all, most organizations coordinating diverse data management disciplines do so without giving their actions a name. For example, the survey for this report asked: “What do you call the coordination of data management disciplines in your organization?” Two-thirds (66%) of survey respondents answered: “We don’t have a formal name for it.” (See Figure 1.) Likewise, in the user interviews conducted for this report, users and consultants alike described how they coordinate data management work and align it with stated business goals for data—but few had a name for it. Even stranger, most software vendors that offer a portfolio of multiple data management tools have no term for the coordinated use of the portfolio!

However, a third of survey respondents (34% in Figure 1) have given coordinated data management a name, and they typed that name into the online survey. The names they report using reveal much about how users think about such coordination. (See Figure 2.)

Generic terms for UDM. A lot of users keep it simple by referring to their coordinated efforts as simply data management (16%) or information management (11%). In fact, most users interviewed for this report stated that they just assume that good data management involves technical people of diverse disciplines learning from each other, complying with data and development standards, considering cross-discipline architectures, and the other best practices this report associates with UDM.

Generic terms, but with enterprise aspirations. If you put the word “enterprise” in front of common terms like data management and information management (thus denoting a broad enterprise scope), then you get two of the most popular terms entered into this report’s survey: enterprise data management (EDM, 15%)1 and enterprise information management (EIM, 11%)2. By coincidence, these acronyms are strongly associated with vendors that have promoted them, namely EIM with SAP and Business Objects and EDM with DataFlux and SAS.

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UDM as a subset of other programs. A number of survey respondents called their UDM-like activities by the names of other data management practices, in particular master data management (MDM, 11%), data or information governance (8%), BI or data warehousing (7%), and data or information architecture (5%). Each is a rather broad program, and each typically involves multiple data management practices. So it’s possible that users responding to the survey were thinking of UDM as a subset that helps unify the data management solutions created and maintained by these programs.

Why Care about UDM Now?

There are many reasons why organizations need to step up their efforts with UDM:

Technology drivers. From a technology viewpoint, the lack of coordination among data management disciplines leads to redundant staffing and limited developer productivity. Even worse, competing data management solutions can inhibit data’s quality, integrity, consistency, standards, scalability, architecture, and so on. On the upside, UDM fosters greater developer productivity, cross-system data standards, cross-tool architectures, cross-team design and development synergies, and assuring data’s integrity and lineage as it travels across multiple organizations and technology platforms.

Business drivers. From a business viewpoint, data-driven business initiatives (including BI, CRM, regulatory compliance, and business operations) suffer due to low data quality and incomplete information, inconsistent data definitions, noncompliant data, and uncontrolled data usage. UDM helps avoid these problems, plus it enables “big picture” data-driven business methods such as data governance, data security and privacy, operational excellence, better decision making, and leveraging data as an organizational asset.

1 See the 2009 TDWI Monograph Enterprise Information Management: In Support of Operational, Analytic, and Governance Initiatives, online at monographs.
2 See the 2009 TDWI Checklist Report Enterprise Data Management, online at

Philip Russom is the senior manager of TDWI Research at The Data Warehousing Institute, where he oversees many of TDWI’s research-oriented publications, services, and events. He can be reached at

This article was excerpted from the full, 27-page report, Unified Data Management: A Collaboration of Data Disciplines and Business Strategies. You can download this and other TDWI Research free of charge at

The report was sponsored by ASG, DataFlux, Informatica, SAP, Talend, Teradata, and Trillium Software.

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