News
2/7/2011
06:43 PM
Rajan Chandras
Rajan Chandras
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10 Tenets Of Enterprise Data Management

Data quality, master data management, metadata management, data warehousing architecture and data integration: These are all pieces of the data management puzzle, but rare is the enterprise that has assembled these pieces into a cohesive and coherent picture. Get it right and you can count on clean and consistent data from transaction systems and reliable insight from business intelligence systems. It doesn't end there. Your data management strategy must also consider business processes and busi




A tenet, according to the Merriam-Webster dictionary, is a principle, belief, or doctrine generally held to be true; especially one held in common by members of an organization, movement, or profession. Enterprise data management (EDM) is a concept that refers to the ability of an organization to precisely define, easily integrate, and effectively retrieve data for both internal applications and external communication, according to Wikipedia. EDM emphasizes data precision, granularity, and meaning and is concerned with how content is integrated into business applications as well as how it is passed along from one business process to another. What follows are ten tenets that will help you define and deliver quality data, ensuring consistency, and sufficient granularity to power business processes and empower decision makers. Technology is but one ingredient to success. Here's how to plan your strategy and get the right people and processes in place.

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Data quality begins (and often ends) with our transactional (OLTP) systems. "Garbage in, garbage out" remains the driving force behind all our data-quality initiatives. Yet, a small investment in ensuring data quality in your OLTP systems will go a long way toward reducing future expenses and hassles in getting quality data to your users. Don't forget, also, that data quality in turn begins with data model quality. Not all truisms carry equal weight: "An ounce of prevention is worth a pound of cure" works better than "What cannot be cured must be endured."

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Does master data begin with transactions, or do transactions begin with master data? That debate is irrelevant. The more practical question is: How do you weave MDM into your enterprise architecture? Here's one answer: Application by application. Grand visions of master data governance are empty without ground-level efforts, and these begin with each application that you develop and implement.

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Debates about Inmon, Kimball and other data warehouse architectures are all great fun... but try and not fall into the trap of dogma. Data warehouse architectures aren't defined by schools of thought -- they are defined and built, ground up and usually with great effort and pain, by flesh-and-blood people. More often than not, nobody has the time to herd architectural trends. Do the best you can; you might be surprised by how well it works.

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Face it, your business intelligence initiatives are going nowhere without significant buy in from the business. It's not so much about who's funding the effort; the big question is, who has the time to work with you in defining and implementing that BI solution -- and who can then get people to use it? If you don't have business by your side on this journey, you're going nowhere very, very slowly.

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Here's something that might not seem totally intuitive: data integration isn't about integration tools and technology capabilities at all -- it's about your integration architecture. Examples abound of creative data integration solutions put in place using the most mundane of tools, but with an extra dose of creativity tempered by some very wise (and experienced) thinking. Let your collective intuition foster rather than fester. The tools are just that -- tools; they don't make the solution.

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As the saying goes, "Many are the slips 'twixt the cup and the lip." Between all the talk about metadata management and real action, there yawns a wide chasm. This is due in part to the fact that vendors have been unhurried at best about incorporating metadata management capabilities and interoperability. Take them on, and make it clear that it matters. But even before that, educate your business users on the benefits of metadata management. And create space in that tight project plan for metadata management activities.

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The first rule of business rules management (BRM) is that not all applications need industrial-strength BRM capabilities -- or a separate, powerful tool to deliver that capability. Look closely at the real benefits of BRM in a forthcoming application; then pick a tool -- or not (just let your application team build rules using some basic best practices). Vendor influence, existing BRM investments, or starry-eyed stakeholders are no reason to unquestioningly adopt formalized BRM.

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Competency centers have been all the rage for years, but do you really want to end up with one competency center for data warehousing, another for BI, a third for MDM, a fourth for BPM, a fifth for BRM, a sixth for -- you get the drift. Start by defining exactly what you understand a competency center to be, and then closely examine the benefits (and drawbacks) of creating one within your organization. Start with this simple question: Who will lead it? If you can answer this satisfactorily, you're halfway there already.

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Business Process Management may be all about processes, but it's not without data considerations. How does an orchestration consume data, where does that data come from, and where should the data come from? What data transformations are occurring inside the BPM solution, and (how) can they be leveraged for wider consumption? Finally, what, exactly, is your definition of BPM, anyway?

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Pencil pushers might classify them as expenses, but your people really belong on the balance sheet under "Current Assets." That's because they know the data. The challenge is how do you institutionalize this knowledge? Do your subject matter experts and data stewards seem to be powers unto themselves? Are others suffocating from a lack of the right kind of data in the right place at the right time? Your insight into data is incomplete without your insight into the people that manage, consume, and influence data.

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