March 6, 2011 marked my five-year anniversary with Forrester Research — and as an industry analyst. Back in 2006 I made a pivotal career decision and decided to depart the end user world where I spent almost 15 years across multiple organizations fighting the good fight building data management solutions that delivered trusted data to the people, processes, and systems that needed it. Amazing to me that I graduated college as a finance major with no IT experience and at the time wouldn’t have known a database if it crashed right in front of me.

For my first job out of school, I worked in Newark, NJ, for six years with Thomson Financial Services (TFS) on its Global Mergers & Acquisitions database product, eventually taking on the role of research manager for that product. In that time I foolishly thought I was gaining M&A expertise, but in reality I was learning how to ensure that the data about these M&A transactions was of the highest quality because data at TFS is not just an asset — it’s its product. To accomplish this, I had to became very proficient with this random programming language called SQL, which I assumed was some niche thing and most likely irrelevant beyond TFS (remember: finance major!). Still being the days of green-screened dummy terminals before the PC and GUI revolution, learning SQL was an incredible eye-opener.

So after an extremely brief stint as a Y2K consultant in 1998 (stop laughing — it got me out to Silicon Valley, and I got out in 1999!), I found my way into Cisco Systems, which hired me as a business analyst to help support the development of some homegrown customer data rules engines that derived many of the key attributes Cisco uses to analyze its customers, channels and partners. After managing the development of a number of customer data solutions at Cisco, I moved on to manage Intuit’s enterprise data quality efforts and launched its first customer master. After 3+ years at Intuit, I made the move to Forrester as an analyst focusing on master data management (MDM), data quality, data integration, metadata management, and data governance.  

So I accepted my new role as a tech industry analyst — along with the traditional jabs from my former colleagues (My favorite: “Those that can’t do, teach — and those that can’t teach become industry analysts!”). 

I took on this analyst role because I looked at what I enjoyed and didn’t enjoy about being a data management professional, and in a nutshell, I discovered that the most frustrating part of my career to date was this: I spent 75% of my time trying to convince senior management to prioritize and provide resources for what they hired me to do, and only 25% of my time actually doing it. (“It” = deliver trusted data). As an analyst, I committed myself to helping other data management professionals flip that value ratio.

After 1,200 client inquiries, 1,500 hours of consulting, and innumerable conversations across various events, tweets, blogs, briefings, etc., I know that most of you share this frustration with me. Even today, business leaders more than ever want high-quality data but don’t accept accountability for it. But it’s getting better . . .

Here's what my client interactions looked like five years ago:

  • MDM was trendy, but no one really understood it. The majority of MDM inquiries were trying to define and justify it, not implement.
  • Many thought “big bang” MDM was a good idea, and worth considering.
  • Data quality was a nice-to-have, but no one was really investing seriously.
  • Data governance was a four-letter word. Some targeted evangelists thought it was a good idea, but mostly grassroots. Minimal resource investment or executive sponsorship.
  • Top-down enterprise data management strategies were not happening. Data management projects were driven from the bottom up to support individual application, line-of-business, or functional needs.

But five years later, the conversations have matured significantly . . . in most areas:

  • Vendor marketing confusion aside, most enterprise architects at large $1 billion+ organizations understand and can articulate what MDM is trying to accomplish and how it fits into their data architecture.
  • I’ve witnessed a 90% increase in pure MDM-related inquiries from the beginning of 2008 through the end of 2010.
  • MDM is no longer a nice-to-have. MDM scope is often cross-functional, if not cross-enterprise. (These and other MDM-specific trends are outlined in much more detail in my recently published research “Trends 2011: It’s Time For The Business To Own Master Data Management Strategies”).
  • A significant increase in organizations' adoption and interest in embedding data quality best practices, methodologies, and technologies into their enterprise data management strategy and architecture.
  • Real resources and investment are being committed to build sustainable data governance programs, although most organizations still have not solved the data governance puzzle and overall data governance maturity remains extremely low. 
  • Developing comprehensive enterprise data management and information management strategies is becoming the best practice and norm for large, complex organizations.

There’s a lot of work still to be done to get data management professionals the visibility, executive sponsorship, and resources they need to deliver trusted data to the right person, at the right time, in the right context, to support the right business processes and decisions. But the wind is at our backs, and momentum is building across multiple entry points ranging from traditional priorities like ERP and BI consolidations, enterprise data warehousing projects, and legacy database migrations to the new trendy data-dependent initiatives including mobility, cloud computing, predictive analytics, social analytics, and big data.

Looking forward to the next five years — it’s going to be a great ride!