Tuesday, June 10, 2014
In most organizations today, information is managed in isolated silos by independent teams using various tools for data quality, data integration, data governance, metadata and master data management, B2B data exchange, content management, database administration, information life-cycle management, and so on.
In response to this situation, some organizations are adopting 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 departments and technology platforms.
But unified data management is not purely an exercise in technology. Once data becomes an organizational asset, the ultimate goal of UDM becomes to achieve strategic, data-driven business objectives, such as fully informed operations 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 them with business goals that depend on data for success.
What is UDM? It is the best practice for coordinating diverse data management disciplines, so that data is managed according to enterprise-wide goals that promote efficiency and support strategic, data-oriented business goals. UDM is unification of both technology practices and business management.
For UDM to be considered successful, it should satisfy and balance the following requirements:
1. UDM must coordinate diverse data management areas. This is mostly about coordinating the development efforts of data management teams and enabling greater inter-operability among their participants. 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. The point is that UDM practices must be inherently holistic if you are to improve and leverage data on a broad enterprise scale.
2. 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.
UDM is largely about best practices from a user’s standpoint. Most UDM work involves collaboration among data management professionals of different specialties (such as data integration, quality, master data, etc.). The collaboration fosters cross-solution data and development standards, inter-operability of multiple data management solutions, and a bigger concept of data and data management architecture.
UDM is not a single type of vendor-supplied tool. Even so, a few leading software vendors 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 inter-operate 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.
UDM often starts with pairs of practices. 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 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 combining 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.
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 integration 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 inter-operability 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.
Why Care About UDM?
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, non-compliant 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.
To be successful, an organization needs a data strategy that integrates a multitude of sources, case studies, deployment technologies, regulatory and best practices guidelines, and any other operating parameters.
Many organizations have begun to think about the contributions of customer information in a more holistic way as opposed to taking a more fragmented approach. In the past, these efforts were undertaken mainly by organizations traditionally reliant on data for their model (like finance and publishing), but today, as more and more businesses recognize the benefits of a more comprehensive approach, these strategies are gaining much wider deployment.
What should a good strategy look like?
First, identify all data assets according to their background and potential contribution to the enterprise.
Second, outline a set of data use cases that will show how information will support any of a variety of customer marketing functions.
Next, create rules and guidelines for responsible use and access, making sure that the process is flexible and transparent. Keep in mind that not all data should be treated the same way; rather, it should be managed according to its sensitivity and need.
Finally, make sure that this process is ongoing so that tactics can be evaluated and adjusted as needed.
Such a strategy combines the best practices with responsible data governance and smart organization. Everyone wins - the employees who gain quick access to essential information, the enterprise that is running more smoothly; and of course, the customers who are served by a resource-rich organization!