Special Note: This analysis was written by Philip Russom, Wayne's colleague in TDWI's research department who covers MDM and data integration.
I don’t know about you, but when I read two, similar announcements from competing software vendors, delivered at pretty much the same time, I can’t help but compare them. So that’s what I’m thinking about today (February 3, 2010), after hearing that IBM intends to acquire Initiate Systems. This bears strong resemblance to Informatica’s announcement a few days ago (on January 28, 2010) about their completion of the acquisition of Siperian.
If you work in data management or a related field, these are noteworthy announcements coming from noteworthy vendors, and, therefore, worth understanding. So let me help you by making some useful comparisons – and some differentiations that you may not be aware of. I’ll organize my thoughts around some questions I’ve just seen in the blogosphere and my Twitter deck.
1 – Are both acquisitions about MDM?
Well, sort of. Siperian is well-known for its master data management (MDM) solution. It has attained one of the holy grails among MDM solutions, in that it works with multiple data domains (that’s data about customers, products, financials, and other domains).
Initiate, on the other hand, is well-known for its identity resolution hub. Initiate’s identity resolution capabilities are commonly embedded within applications for MDM and customer data integration (CDI). The way that I think of it, Initiate’s hub isn’t for MDM per se, but it can improve MDM when embedded within it.
At this point, I need to cycle back to Siperian and point out that it, too, provides identity resolution capabilities. And I forgot to mention that Initiate also has some MDM capabilities. You could say that Siperian is mostly MDM, but with identity resolution and other capabilities, whereas Initiate is mostly about identity resolution, but with MDM and other capabilities.
2 - So, the two acquisitions are about identity resolution?
Yes, but to varying degrees. For example, IBMers were very clear that their primary interest in Initiate is its ability to very accurately match data references to patients in healthcare and citizens in government. IBM’s campaign for a Smarter Planet has strong subsets focused on healthcare and government, two industries where Initiate has reference clients doing sophisticated things with identity resolution. My impression is that IBMers are hoping Initiate’s identity resolution functionality will help them sell more products and services into these industries.
Returning to Informatica and Siperian, let’s recall that for years now the Siperian hub has been integrated with Informatica PowerCenter (similar to pre-existing integration among IBM and Initiate products). Among other things, this integration enables Siperian’s identity resolution functions to be embedded within the PowerCenter platform under the name Informatica Identification Resolution. Hence, identity resolution was one off the key capabilities paving the path to this acquisition.
3 – What do these acquisitions mean for IBM and Informatica?
As noted, IBM is counting on the Initiate product to help their campaigns for Smarter Healthcare and Smarter Government. Informatica has now filled the largest hole in its otherwise comprehensive product line, filling it with one of the better tools available via acquisition. Both IBM and Informatica are aggressively building out their portfolios of diverse data management tools, driven both by user demand and competitive pressures. Since both have customers with growing demands for more diverse data management tool types, both will have no trouble cross-selling the new tools to their existing customer bases, as well as selling their older products to the newly acquired customer bases.
4 -- What do these acquisitions mean for technical users?
In my experience, Informatica and IBM both have rather faithful customers, in that they tend to get most of their data management tools from a primary supplier. Technical users from both customer bases now have more functionality available from a preferred technology supplier.
But these aren’t just any data management functions. The two acquisitions focus the spotlight on two of the hottest functions today, in terms of user organizations adopting them, namely: MDM and identity resolution. More than ever, organizations need trusted data, in support of regulatory reporting, compliance, business intelligence, analytics, operational excellence, and other data-driven requirements. MDM and identity resolution are key enablers for these requirements, so it’s no surprise that two leading vendors have chosen to acquire these at this time.
Posted by Wayne Eckerson on February 3, 20100 comments
Master data management (MDM) enables organizations to maintain a single, clean, consistent set of reference data about common business entities (e.g. customers, products, accounts, employees, partners, etc.) that can be used by any individual or application that requires it. In many respects, MDM applies the same principles and techniques that apply to data warehousing—clean, accurate, authoritative data.
Not surprisingly, many data warehousing (DW) professionals have taken the lead in helping their organizations implement MDM solutions. Yet, even grizzled DW veterans pose fundamental questions about how to get started and succeed in this new arena. Here are answers to the seven most common questions:
1. What’s the best place to start with MDM? People want to know whether it’s best to start with customer, product, or account data, or whether the finance, service, or marketing department is most receptive to MDM. The actual starting place is determined by your organization and the amount of pain that different groups or departments feel due to lack of conformed master data. The only surefire advice is to start small and work incrementally to deliver an enterprise solution.
2. How do you fund MDM? Few people have succeeded in funding stand-alone MDM projects, especially if their company has recently funded data warehousing, data quality, and CRM initiatives. Executives invariably ask, “Weren’t those initiatives supposed to address this?” Replying that MDM makes those initiatives more efficient and effective just doesn’t cut it. The best strategy is to bake MDM projects into the infrastructure requirements for new strategic initiatives.
3. How do you architect an MDM solution? The right architecture depends on your existing infrastructure, what you’re trying to accomplish, and the scope and type of reference data you need to manage. A classic MDM hub is essentially a data reconciliation engine that can feed harmonized master data to a range of systems—including the data warehouse. MDM hubs come in all shapes and sizes: on one extreme, a hub serves as the only source of master data for all applications; on the other, it simply maintains keys to equivalent records in every application. Most MDM solutions fall somewhere in the middle.
4. What’s the role of the data warehouse in MDM? There is no reason you can’t designate a single application to serve as the master copy. For example, you could designate the data warehouse as the master for customer data or an Oracle Financials application as the master for the chart of accounts. These approaches are attractive because they reuse existing models, data, and infrastructure, but may not be suitable in all situations. For instance, you may want an MDM solution that supports dynamic bidirectional updates of master data in both the hub and operational applications. This requires a dynamic matching engine, a real-time data warehouse, and Web services interfaces to integrate both ends of the transaction.
5. What organizational pitfalls will I encounter? Managing the expectations of business and IT stakeholders is nothing less than a make-or-break proposition. “Change management can derail an MDM project,” says one chief technology officer at a major software manufacturer that implemented a global MDM project. “When you change the data that end users have become accustomed to receiving, it can cause significant angst. You have to anticipate this, implement a transition plan, and prepare the users.” In addition, don’t underestimate the need to educate IT professionals about the need for MDM and the new tools and techniques required to implement it.
6. What technical pitfalls will I encounter? First of all, MDM requires a panoply of tools and technologies, some of which may already exist in your organization. These include database management systems, data integration tools, data matching and quality tools, rules-based systems, reporting tools, scheduling, and workflow management. Buying a packaged solution alleviates the need to integrate these tools. But if you already have the tools that exist in a package, negotiate a steep discount. Early MDM adopters say the biggest challenges are underestimating the time and talent required to define and document MDM requirements, analyze source data, maintain high-performance Web services interfaces, and fine tune matching algorithms to avoid under- or over-matching.
7. How do I manage a successful MDM implementation? To succeed, MDM requires business managers to take responsibility for defining master data and maintaining its integrity. This involves assigning business executives to stewardship roles in which they drive consensus about data definitions and rules and oversee processes for changing, managing, auditing, and certifying master data. Good data governance may or may not involve steering committees and meetings, but it always involves establishing clear policies and processes and holds business people accountable for the results.
MDM is a major undertaking and there is much to learn to be successful. But hopefully the answers to these seven questions will get you moving in the right direction.
Posted by Wayne Eckerson on November 6, 20090 comments
I recently reviewed the course materials for a class titled “A Step by Step Guide to Enterprise Data Governance” taught by Mike Ferguson at the TDWI Munich conference in June. Mike did a tremendous job covering the full scope of the data governance topic.
Mike defines enterprise data governance as “the set of processes by which structured and unstructured data assets are formally managed and protected by people and technology to guarantee commonly understood trusted and secure data throughout the enterprise.”
The elements that Mike puts in the data governance bucket are: data definitions and shared business vocabulary; metadata management; data modeling, data quality; data integration; master data management; data security; content management; and taxonomy design and maintenance.
This is a big vision, and certainly elevates the discussion to its proper perspective: that is, data is a business asset and it’s the responsibility of business to oversee and manage this resource. The corollary here is that IT plays a supporting, not supervisory, role in managing the company’s data.
Central to Mike’s vision of enterprise data governance is a Change Control Board, which is the “gatekeeper” for the shared business vocabulary. This board, which is comprised of data stewards from the business, is responsible for approving requests to change, add, or decommission data items. Implicit in this is that the Change Control Board manages data names and definitions, transformation rules, and data quality rules. And these get baked into data models, BI metadata, MDM models, and taxonomies.
Given how fundamental data is to a business (whether it knows it or not), it’s imperative that a senior executive oversee the data governance team that is comprised of senior business managers and stewards. Maria Villar, owner of MCV LLC, writes, “A business data steward is a leadership position…. who understands the importance of data to their part of the business.” (See “Establishing Effective Business Data Stewards” in the spring 2009 edition of the BI Journal.)
Villar says a business data steward “understands the priorities and strategies of the business unit, commands respect within the organization, builds consensus across a varied set of business priorities; influences and drives changes to business processes, enjoys strong support from senior business leaders, can communicate to business and technical teams, and builds a diverse team of technical and business data experts.
Now that we have the verbiage straight, we have to execute on the vision. And that will keep us busy for years to come!
Posted by Wayne Eckerson on July 14, 20090 comments