With the New Year dawning I wanted to look back at some industry trends from the past twelve months, and then look at ahead at what we’re likely to see in 2010. So, this week: recap. Next week: predictions.

Just this week, leading research firm Gartner, Inc. published its 2010 predictions for MDM. There is one prediction related to multidomain MDM that I found particularly interesting. It mentions that the number of companies shopping for multidomain MDM solutions has increased. Now, why is that?

To get some insight into MDM purchasing and implementation trends, we simply need to look at companies that began their MDM journey in the past five years, especially those companies that started off with a single domain, such as customer data.  Many of these MDM pioneers have since expanded their implementation to other domains such as finished products, materials, price, employees, and so on. But how did they do that? By using the same multidomain MDM platform? Or by separately implementing distinct single-domain MDM applications, such as one for customer data and another for product data?

Over the past two years, the demand for Master Data Management (MDM) has remained strong, despite the economic downturn. This isn’t surprising, since an effective MDM implementation is one of the few IT initiatives companies can pursue to realize near-immediate business process improvements across many different areas within the enterprise. However, the question confronting IT and business decision-makers is which MDM solution and methodology will work best to resolve their master data challenges? One possible route for many large organizations with extensive SAP implementations is to make their SAP enterprise resource planning (ERP) application suite the focus of their MDM initiatives. It seems like an enticing proposition, given the possibility of capitalizing on existing IT infrastructure, investment, institutional knowledge and so forth. Yet SAP ERP systems are not designed to support master data management, and are simply not the right place to master data.

The current hot topic in the MDM space is multidomain master data management. And rightly so, as multidomain MDM has the potential to drive far more value for companies than limited single-domain MDM initiatives (aka CDI, PIM) that focus on a specific class of data such as customer or product.

We’ve heard interesting discussions that multidomain MDM is just about storing the multiple domains within the data model. That is a major misinterpretation. While it’s certainly true that you need to have a data model that’s flexible enough to accommodate multiple data domains (e.g. product, customer, supplier), the data model itself is not the be-all and end-all of multidomain MDM. It’s a requisite starting point, sure, but you need to be able to do so much more. For instance, the ability to match and merge data across various domains is extremely important. Same goes for data cleansing.

Think of it this way: you’re using a dedicated PIM system. It does a great job of matching data fields in ways that are very valuable in addressing problems with product-centric aspects of your business: supply chain, inventory management, etc. Can this system do a good job matching and merging data fields from multiple domains? Can it provide the kind of data cleansing capabilities you’d need if you wanted to incorporate customer data?

Now that we’re back in the office from last week’s Siperian’s 5th user conference, it’s apparent that the event was quite a success. The two days went quick, which says to me that we got the format, presentations and atmosphere right. There was a lot of enthusiastic response to the technical tracks, and with 270 people, this was our largest user conference, ever!

I wrote in a previous post that multidomain MDM was emerging as Topic A at the conference, and that was definitely the case. John Radcliffe’s keynote addressed it and several customers gave presentations demonstrating multidomain implementations as well. It’s a subject we’re definitely going to be hearing a lot more about. Two other trends to watch: increasingly creative use of Siperian Business Data Director, and corporations turning to system integrator partners to gain value from MDM as quickly as possible. Our inaugural GOOEY Award winners were a clear indication on this score.

The early pioneers of Customer Data Integration (CDI) had a primary goal – to create the most reliable customer data for the company by bringing together and resolving inconsistent, and oftentimes incomplete, customer data which was at the time siloed in disparate applications across the enterprise. The CDI promise was to have reliable customer data available to benefit a myriad of customer-facing operations in marketing, merchandising, contracting, account management and invoicing, to name a few. Focusing on these priorities, many organizations selected a CDI technology that enabled them to solely optimize customer data. Other enterprise data such as products, locations, and accounts, for example, were considered out of scope and not necessary for the initial CDI project.

Typically, in large organizations data governance and its practice germinates either from a top-down approach led by a key executive– say a Chief Financial Officer, or from a bottom-up approach driven by business unit stakeholders who understand the importance of data ownership and data management to their success. Yet, either approach can readily stall, run into a political logjam, or fail if critical questions have not been raised and addressed in advance of embarking upon a solid data governance initiative. In a report entitled, A Data Governance Manifesto: Designing and Deploying Sustainable Data Governance, Jill Dyché co-founder of Baseline Consulting and author of the report warned, “Without a sound description of the problems being solved, as well as clear communications around key decisions and the authority to make them, data governance can fail before it really begins.”

Information Technology managers are facing a dilemma given the current economic climate – despite budget cuts, companies do not expect reductions in the service levels provided by IT to the business. Under such circumstances, how do you maintain or improve service levels, and continue to run the business efficiently? Smart IT decision-makers are seeking out technology investments that can help to accelerate cost reductions and at the same time streamline business processes.

Master Data Management (MDM) is exactly this kind of investment. MDM ensures that critical enterprise data is validated as correct, consistent and complete when it is circulated for consumption by internal or external business processes, applications or users. A recent Gartner report on 2009 Enterprise Software Spending mentions that “the business case for many application and infrastructure initiatives are now aligning to cost reduction and risk management,” and that MDM is key to transforming business processes to streamline them further in current market conditions.

But not all MDM technologies can provide these cost benefits. Only an integrated, model-driven, and flexible MDM platform with easy configurability can provide rapid time-to-value and lower total cost of ownership. Consider the following seven ways a flexible MDM platform can lower costs:

There are two kinds of organizations: those which have implemented a master data management (MDM) solution and those which haven’t but undoubtedly will in the near future. Despite where each organization may be in their MDM effort, what each have in common are questions about achieving better business processes. Companies planning an MDM implementation tend to have important questions regarding vendors, strategies and creating road maps to ensure success and a healthy return on investment. Fortunately, these companies can learn and benefit from the experiences of organizations that have pioneered MDM and organize their efforts around a common set of principles. For those organizations whose initial MDM efforts brought early success, the question is how can the lessons learned be leveraged to drive more effective data governance across other lines of business and more elements of the system’s infrastructure?

Companies wishing to start a master data management (MDM) project may be unsure where and how to begin. After all, MDM is a journey and success or failure at the first step either defines or dooms the further evolution of the project. Recently, industry analysts have been recommending a cautious approach to starting with MDM – suggesting that companies start with a single data type (such as customer), implement MDM using a small footprint (such as registry style) or deploy MDM solely with a data warehouse to improve reporting. Inherently these technology focused approaches reduce project risk and relieve the data governance burden. Companies may readily adopt these approaches as perfectly reasonable starting points and lean to a more risk-averse approach to their initial MDM implementation in hopes of mitigating risks. However, these same approaches may limit the scope and potential return on investment (ROI) from MDM since they do not attempt to solve the most pressing and difficult business problems.