I once had a pedicure on Madison Avenue and Ivana Trump was there. I tried to seem nonchalant but was secretly...
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smug that I had chosen a New York venue frequented by The Donald’s ex, complete with callous remover and a fabulous shade of Thrilla from Brazilla, a hot magenta, for my toes. When they were dry, I hailed a cab to La Grenouille but was turned away by a maitre d’ who essentially explained that the power of a perfect pink pedicure was insufficient currency for a seat at the banquet. So I skulked back to my hotel room where I ordered tomato bisque from room service and contemplated my shiny tootsies, my mood sinking along with the croutons atop the soup.
My point here is that for better or worse, managing perception is as much art as it is science, and that goes for business intelligence and master data management (MDM) projects. That’s why there’s so much spin. People need to position new initiatives in order to sell them, often mislabeling them to enhance their glow. For example, I have a client who has just branded its data warehouse a “CDI hub.” They’re also calling operational reports “strategic BI.” Don’t get me started about their ETL programs.
True, some people are overawed by unfamiliar terminology and colloquialisms and use them to earmark new efforts. But such attempts at spin have left business users confused and disillusioned. “This BI stuff really isn’t all it’s cracked up to be,” one finance user admitted when discussing a recently introduced business intelligence center of excellence (COE). His department’s revamped BI reports are prettier but not as detailed as what they got in the past, and their data quality remains a mess. But now that a formal COE has been rolled out everything is somehow much more official and important sounding. Everything’s just shinier!
But a lot of the shine is just the veneer. There is still somehow a solid line between “The Business” and “IT.” Okay, so it’s a brick wall. The “us versus them” phenomenon flourishes as IT disappoints the business (“How many more times are they going to ask us for our requirements?”) and the business disappoints IT (“They’re never happy with anything we deliver, and we can’t hold their attention for more than six seconds!”). At the risk of sounding like an ‘80s new age band, people are people, and people have a lower bullsh*t threshold than they used to. They recognize spin for what it is. You’re either doing advanced analytics or you’re not. You’re either democratizing business intelligence or you’re running reports for people. A gathering of like-minded stakeholders does not a data governance process make. The vague ownership and hazy terminology simply prolong the pain.
A large part of this is simply the vocabulary we use to communicate with our business constituents. That means you, Mister IT Spin Meister. The table in Figure 1 reflects an example of the “before and after” language of a typical IT shop when it comes to business intelligence:
|IT Says||The Business Thinks|
|"Program Management Office"||"Another layer of project management"|
|"Requirements"||"What my report should look like"|
|"Architecture"||"What my report should look like?|
|"Enterprise data model"||"Oh please God, no."|
Taking the time to establish a common vocabulary around our business intelligence and master data management projects not only gets everyone on the proverbial “same page” relative to these efforts, but also is a means by which to enlist business involvement. After all, it’s not as easy as simply offering a lexicon to business users and telling them to begin changing how they talk. It’s also about changing the rules of the game and getting everyone to agree on common goals and objectives.
For instance, we recently delivered a BI Portfolio™ for a regional HMO. During our engagement we realized the degree to which there were no commonly shared goals between the business people – including doctors, nurse practitioners, and executives – and IT. As part of the project, we developed a set of critical success factors (CSFs) that were both business-oriented and data-enabled, and gained agreement on them. Figure 2 illustrates some of these CSFs and their definitions.
Figure 2: Data-Enabled Critical Success Factors for an HMO
Critical success factors – indeed, measures in general – are central to the way healthcare companies do business. The fact that CSFs were already part of the healthcare company’s culture wasn’t lost on us; we decided to use them as a mechanism for socializing the BI Portfolio and to get everyone off the dime. Managers across IT and the business agreed on the CSFs, and that they could be either realized or improved with better data that was accessible on demand. This allowed us to not only define and publish the BI Portfolio for the HMO, but also to distill and prioritize the data that would support each individual objective and result in observable and sustained business improvements.
Moreover, consensus between the business and IT on critical success factors lit a fire under management, whose understanding of the role of data-as-asset was sketchy at best. “You mean in order to actually succeed at these things, we need to harness our data?” asked the CEO when we explained the list along with the rest of our deliverable. The answer was, “Yes. And soon.”
Using CSFs as a mechanism for tightening consensus and establishing your program vocabulary is only one technique. The point here is not to go too far outside the box, but rather to leverage the language your organization already speaks. For instance, we recently watched another consulting firm try to push through balanced scorecard concepts in order to justify a corporate performance management effort at one of our clients. Unfortunately, the consultants underestimated the missionary work and the need for buy-in from top management. The result was unempowered people trying to grasp concepts that their company simply wasn’t ready for and that their executives weren’t behind.
As an internal consultant in your organization, you need to begin strategic projects like enterprise business intelligence by meeting your company “where it is” and avoiding revolutionary or complex concepts that may threaten people’s paradigms too dramatically. Business intelligence, master data management and data quality are disruptive in their own right. Design these programs so that they deliver incremental benefits over time. In this way, BI will earn the right to drive change – and that goes for the people delivering BI too.
Exploiting components of your current environment can be a helpful way to kick things off, but we still need to innovate. When it comes to data-enabled initiatives like BI and MDM, innovation is a key element. If you’ve spent any time at all within a corporate culture, you know that innovation is rarely institutionalized. Mechanisms like critical success factors and other incumbent practices that have been proven at your company can be leveraged to drive new change, not only propelling business intelligence forward, but accelerating the overall pace of change.
A journalist once asked country chanteuse Dolly Parton about her look. She commented that she liked to spend time primping but that “…I could look cheap in so much less time!” The point is to know what you’re ready for, what existing capabilities to leverage, and when to paint the toenails pink.