In which Jill turns to eastern philosophy before the next office smackdown.

I always like predictions. Every January I write a blog post about predictions for the coming year. Analysts and magazine columnists also routinely ask me what’s next. In past years I’ve predicted the increasing importance of data governance, the rise of cloud computing, the changing role of the executive sponsor, and the widening abyss between the business and IT. It’s sort of like being an armchair quarterback. And it’s all so, well…predictable.

This year I wanted to weigh in on corporate politics, which is a theme that reliably shows up in my predictions lists year after year. Corporate politics are like Seinfeld reruns: hard to spot initially then ultimately unavoidable.

You’re probably thinking that I’m going to go all Art of War on you. After all politics are about diverging ideologies, hierarchical power struggles, and land grabs. Crowded meeting rooms and heated arguments eventually cede to passive-aggressive attacks and organizational maneuvering. Meanwhile the beleaguered staff charged with executing on the outcome hunker down in their cubicles, waiting for someone to sanction an actual decision and surreptitiously exchanging Dilbert cartoons. But the skirmishes continue and no decisions are forthcoming except for the ones that exclude key players from updates and fuel below-the-radar efforts intended to derail competing initiatives.

No wonder corporate politics use conflict as metaphor. In the Art of War, Sun Tzu explains that a superior warrior foils enemies’ plots, ruins their alliances, and besieges their cities. And while we’re on the topic of war, don’t forget General George S. Patton’s quote, “No bastard ever won a war by dying for his country. He won it by making the other poor dumb bastard die for his country.”

Many of us have performed heroic acts on behalf of our companies. Swigging coffee in proposal-writing all-nighters. Enduring multi-leg flight itineraries to deliver the client presentation. Forsaking the third gin-and-tonic in the interest of the early-morning start. You’ve been there and you have the battle scars to prove it.

But the war metaphor is wearing thin. We’ve had enough of it. Plus, the smarter side doesn’t always win.  In his new book, Betterness: Economics for Humans, author Umair Haque asks corporate employees the following question:

“Did what you do have a positive, lasting consequence that was meaningful in human terms?”

I know, that doesn’t sound very hawkish. But according to several studies, employees are at their happiest not when they’re backstabbing their colleagues or angling for the corner office, but when they’re challenged with difficult but realistic goals and making progress in achieving them. We can pursue corporate profits and do our jobs while staying true to our own personal ideals. It’s the Golden Rule writ large on business. Your colleague is your neighbor is your friend is You.

In 2012 I’m putting Sun Tzu back on the shelf and picking up Lao Tzu, the father of Taoism. He said:

“Knowing others is intelligence; knowing yourself is true wisdom. Mastering others is strength; mastering yourself is true power. If you realize that you have enough, you are truly rich.”

I dare you to read this quote out loud right before your next meeting.

Now go knock ‘em dead!

Photo by Beggs via Flickr Creative Commons License

It’s always interesting to hear somebody dismiss a trend.

“That’s not new!” he might say as he strokes his beard, lights a pipe, and mixes himself a Manhattan.  “I worked on that stuff my first job out of college for cripes sake!” Then he flips the Peter, Paul, and Mary record, remembering the good old days of bra-burning and punch cards.

And so it is with the newest trend, Big Data. High tech companies looking for more efficient ways to process and store their web transactions are often credited with lighting the big data fire. Big data represents the collision of data warehouse, search, visualization, and storage worlds, and it brands the conundrum we’ve been facing (and largely ignoring): information is hitting companies at a faster rate than ever, and incumbent technology solutions are often too cumbersome or expensive to solve the problem.

So IT slips off its Birkenstocks and jumps into the technology sandbox to play with new toys like Hadoop, NoSQL, and grid computing. But in our conversations about big data, we overlook something just as important as the enabling technologies: the business-driven policy-making and oversight of all that big data. Yep. We’re forgetting data governance.

When it comes to big data most of my clients are still in research mode. As their advisor I’m bound to ask them that trite-yet-requisite Management Consulting Level-Setting Question: “What’s the need, pain, or problem you’re trying to solve?”

Often clients explain that they need to treat transaction data differently than they need to treat, say, customer master data. Fewer business rules, more history, that kind of thing.  That’s when we start the work of classifying different data domains according to varying business policies:

(click to enlarge)

Figure 1: Establishing Data Categories

Data classifications can get quite detailed, and there can be many categories. But if you’ve designed your data governance program the right way you should be able to apply Guiding Principles to each category.

This strategy can then be used to gain consensus around optimal data management tactics, business rules, provisioning processes and, yes, technology for each category. Maybe that technology includes grid arrays or Hadoop. Maybe you’ll realize you don’t need new technology for a given category. Either way, you’re circumscribing a taxonomy for your data. That’s when the realization hits that the size of the data doesn’t matter as much as how you use it.

A 1982 photo of Microsoft founders Bill Gates and Paul Allen shows the young entrepreneurs pausing from a heavy-duty design session to smile for a reporter’s camera. Back then Microsoft was on the cusp of becoming a household name – the company announced a fresh logo design and hired a new president – but yachts had not yet been purchased, nor had foundations been launched. Call it the DOS Age of Microsoft, to be followed much later by the Bing Dynasty.

Self-assured, though just shy of media-polished, Allen and Gates were already comfortable donning the technology innovator mantle, and they would spend their careers wearing it proudly. But an even better glimpse into the future is the set of scribbles on the white board behind them. In the middle of a flywheel partially-obscured by Gates’ head is the word "Data."  The drawing suggests a core principle: that even as Microsoft continued to innovate, data would be the common denominator in its evolving product portfolio.

Whether Microsoft has stayed true to the vision of data-as-central-to-software-functionality is up for debate.  The point here is that all these years later the vision holds up.

IT departments furtively investing in successive integration efforts, hoping for the latest and greatest “single version of the truth” watch their budgets erode and their stakeholders flee. CIOs praying that their latest packaged application gets traction realize that they’ve just installed yet another legacy system. Executives wake up and admit that the idea of a huge, centralized, behemoth database accessible by all and serving a range of business needs was simply a dream. Rubbing their eyes they gradually see that data is decoupled from the systems that generate and use it, and past infrastructure plays have merely sedated them.

Our corporate leaders are awakening to the understanding that data is an asset in its own right. They need to begin investing in data (and the processes, skills, and enabling technologies to cleanse, integrate, annotate, and deploy it) not in the context of systems, but as a business enabler. It means investing in human and process capital. It means divorcing data governance from IT governance. In the era of big data, leaders need to – co-opting the tag line of another renowned software innovator – “think different.”

Simply put, we need to design our data strategies just as we design our organizations, our business processes, and our systems. If we’d done that at the very beginning, when we were younger and had a vision for how far we could go, just imagine where we’d be now.

When I was 9 years old I heard my mother remark to my father that the neighbor’s house had a wet bar. “What’s a wet bar?” I asked. “She doesn’t need to know that,” my father told my mother.

“You don’t need to know,” my mother told me. But my imagination had already begun gyrating. A wet bar sounded forbidden and vaguely sinister. Why did our neighbors have one and we didn’t? The neighbors had painted their living room chartreuse and they played chess, so they were obviously hippies. A wet bar was probably something sexy where you got to wear your bikini, no kids allowed.

I’m reminded of the wet bar story when clients ask me about starting data governance programs. They have lots of questions and certain assumptions about data governance, though most have never seen it in action. It’s exciting but dreadful, important but dangerous.

They imagine themselves part of an omnipotent committee of decision makers that convenes monthly in the executive boardroom. The CEO and his staff are convivial and earnest, the lemonade flows, and plates of brownies crowd the credenza. The CEO gives an impassioned speech about how data is strategic. “Data is an asset!” the attendees cry while enthusiastically jotting down their action items. The CMO personally asks you to chair the next meeting, which will feature cupcakes and iced green tea. That went well, didn’t it?

The reality of companies new to data governance is that it’s launched before it’s ready by people for whom the need to manage data is suddenly terribly urgent. Stakeholders are confused, and participation is reluctant unless it’s enforced. Data governance hasn’t even hit the CEO’s radar, never mind that none of the vice presidents have responded to your meeting invitation. Someone wonders “whose budget is paying for this?” The only meeting room available is the one along the inside hallway with spotty internet access and a broken projector. And when people do show up they appear beleaguered and gaunt, as if they’ve just escaped from filming Dr. Zhivago.

An effective data governance program obviously falls somewhere in the middle of these two extremes. The executives in question might not be C-level, but they do have the organizational authority to incent participation and secure funding for data governance. The program itself has been designed and communicated so that when people meet to discuss it, they know what it looks like and how it drives business benefits. The team is ready to establish decision rights, so participants understand what they’re accountable for and how they’ll accomplish their goals. Delivery capabilities are examined honestly for the first time.  Efforts are prioritized based on ROI and strategic alignment. And outcomes are clear so there’s no question about what success looks like.

Of course much of what makes data governance work is how it’s designed around (or in spite of) a company’s cultural norms. How disruptive data governance should be depends on the organizational appetite for change. How you grab people’s attention has everything to do with its long-term traction.  If you can reserve the boardroom, so much the better. And if the boardroom has a wet bar? Trust me, you’re golden.

A few years ago I read a book called Death Sentences in which author Don Watson argues that the use of empty words erodes business messages. Watson cites words like “flexible,” “value” and “bottom line” as some of the main culprits, claiming that vague language begets vague meaning. The resulting fallout can extend all the way to a company’s customer relationships.

Watson made me think about words I’ve used too frequently in my blog posts and articles. “Innovation,” “leverage,” and “synergy” are now largely absent from my vocabulary. Indeed, despite his calling out consultants as “the plague rats of the language virus” – ouch! – I liked Watson’s book.

(Okay, I was tempted to use the word “enjoyed” right there but caught myself just in time. You’re welcome.)

I was thinking about clichéd words and phrases the other day as I was teaching our Data Governance Executive Overview workshop. There’s a slide titled “Common Data Governance Aphorisms” where I take on some well-worn data governance platitudes. (“Data governance is a program,” anyone?) This started me thinking about other common data governance clichés:

• Data governance brainstorming. Don’t go into the light! (Sorry. Second cliché.) If you’re doing a brainstorming session relative to data governance, you’re probably not giving it enough rigor. Data governance requires contemplation, but it demands design. Brainstorming is the enemy of accountability and structure. It often masquerades as deliberate planning. And that’s a fast road to nowhere. (Cliché number 3.)

• Agile data governance. I get it. I do. The term “agile” grabs people’s attention, so someone’s decided to apply it to data governance, like painting racing stripes on a Volvo. After all, if we tack agile onto data governance, they’ll think it’s easy and fast. They’ll be comfortable. Except for this: data governance is hard. Data governance can be disruptive. So go ahead and Scrum away! You’ll be finished with data governance before you know it. And that’s not the goal.

• Convene a data governance council. That’s right. Data governance councils have become self-parodies. And here’s why: forming a data governance council, steering committee, or advisory board is not Job One. In fact, if you’re doing data governance the right way it’s more like Job Four or Five. Here’s a test: count the number of times people make reference to an existing or future data governance council. Then compare it to the number of times people mention “data management processes,” “workflow,” “risk mitigation,” or “decision rights.” If “data governance council” wins, you lose.

• Data governance in the cloud. ‘Nuff said. (Yup, clichés 4 and 5.)

The point is that no two data governance efforts are alike. Go to a conference and talk to vendors and consultants to get your feet wet (6), but if someone offers you a template, drop your logo-ed tote bag and run like the wind (7). That will be in the direction of cultural awareness, clear goals, and clear expectations. Otherwise it’s just the blind leading the blind.

That’s 8. So sue me. (Oops, 9.) I’m outta here. (10)

P.S.: Tweet me your favorite #businesscliche at @jilldyche.

Hey, who’s that dude? He looks familiar. Didn’t we meet him over cheap rosé at one of those mega-vendor conferences? The one where some executive was talking about The New Age of Information, Part Deux.? Or maybe it was someone who looked like him. They all blend together after a while, don’t they, collapsing under the weight of so much spin?

Then there’s the Pacific Northwest BI Summit, which Claudia Imhoff once anointed “an event like no other.” This invitation-only affair is hosted by PR powerhouse Scott Humphrey, President of Humphrey Strategic PR. Scott represents many of the hottest players in the BI and data integration spaces and as the event’s impresario, he hand-picks a guest list replete with leading vendors,  prominent media, and industry experts who, when it comes to industry vision, bring it. (They bring the tequila, too. More on that later.)

This was my seventh BI Summit and it was the best one so far. The vendors were a collection of both emerging visionaries and established players:  IBM, Lyzasoft, Composite Software, Predixion Software, DataFlux, JackBe, WhereScape, Paraccel, Talend, QlikView, Teradata, and SAP all represented their philosophies and product directions, not to mention some trenchant industry insights.

Speaking of which, fellow expert panelists William McKnight, Colin White, Claudia Imhoff, Merv Adrian, and Shawn Rogers all offered observations on the latest industry trends and buzz. Here are summaries of our more lively discussions:

• Customers will have more control over their own data—even if it means lying. Shawn Rogers, formerly a co-founder of B-Eye-Network and now an industry analyst with EMA, anointed Facebook as “the new white pages.” As such, the behemoth social site is learning more about its members—and could share more data about us. Distrustful of sites that track their information and behaviors, social media users are increasingly misrepresenting themselves on-line, or, as William McKnight called it, “gaming their data.” Companies will increasingly be at the mercy of customers’ willingness not only to share their data, but to share the truth.

• Companies are still immature when it comes to their use of social media. Shawn Rogers and Lyzasoft’s Tamara Dull are the savviest social media experts I know. Our social media conversation focused on what companies are doing—and what they’re not. In the days after my CRM book was published I maintained that companies that don’t have a culture of listening to their customers wouldn’t start just because they’d adopted CRM tools. Ditto social media.

• As cloud adoption continues, data integration will remain a challenge. Bob Eve of Composite Software observed that the cloud was freeing his CIO customers from having to track their back-end systems. Commodity or no, the cloud introduces new challenges to IT departments. Getting data into and out of the cloud requires standards, not to mention solutions companies might not yet have. New cloud solutions can actually exacerbate data integration challenges by introducing new data in new formats that companies that don’t have the hooks in place.

• Big data is as much about “other data” as it is about BI data. As we know from the adoption of Hadoop, parallel execution has allowed companies to handle more and more data. Merv Adrian presented some use cases for big data (or, as Gartner calls it: extreme information), including industry-specific applications, pattern discovery, and horizontal apps. Colin White put it well when he said that big data is about “data workloads we couldn’t support before.” But these workloads might not be geared to BI. As clickstream volumes skyrocket the data may not ever make it onto the data warehouse. Thus the BI Competency Center should only engage in the ownership of big data if it’s necessary to deliver a business capability in the context of analytics.

The post-session offline discussions were just as entertaining, lubricated as they were by some high-end tequila. Another of the many debates not directly won at the summit was the throw-down between the Clase Azul Reposado—smooth, lovely aroma, beautiful bottle—and the Don Julio 1942—smoky caramel flavor with a vanilla finish and…I can’t remember the rest.

As anyone who’s lost a loved one knows, the memories are bittersweet. Recently our dog Colby died from lymphoma, and I’m at once shattered by her loss and grateful for what she taught me.

I was Colby’s fourth owner. The prior three had various excuses for not keeping her. She jumped a 7-foot fence. She didn’t do well in an apartment. She didn’t get along with the pot-bellied pig. She wasn’t affectionate. She had epileptic fits. Most dogs in shelters are thrilled to be on their way to a real home, but Colby seemed indifferent. She’d seen it all before.

I soon realized that I was in over my head. Colby was impossible to walk on a leash. She didn’t come when called. She was food-aggressive with my other dog. Worst of all, as soon as we hit the hiking trail she would bolt. The first time I let her off the leash, she disappeared for nine hours.

But I’d made a commitment to her and I was bound to see it through. Through the rehabilitation process—hers and mine—I learned some lessons that I return to now in my work life. Here are some lessons Colby taught me:

Do what you’re good at. I’ve always rescued homeless dogs, mostly ending up with border collies and shepherd mixes, those loyal working types that gaze at you fondly while doing your bidding. But as anyone who’s ever had a beagle or a basset hound knows: hound dogs are special. They are beholden to every scent wafting through the air. So if it’s a choice between your redolent human pheromones or the scent of a squirrel or raccoon, well, the varmint always wins. Colby didn’t herd on command or obey a series of hand gestures. She’d just lift her nose, breathe in, and go. She could chase a single deer, sight unseen, for miles and be the happiest dog in the world.

Pick a team you work well with. Colby liked other dogs well enough. She’d greet them with a brief sniff and a raised tail and move on. But she had a special affinity for yellow Labrador retrievers. When she spotted a yellow lab Colby would play-bow and give chase. She and her lab friends would swat each other around, hunt for ground squirrels together, and generally tire themselves out, going home exhausted and anticipating their next reunion. Colby didn’t like every dog she met, but she definitely knew who she wanted in her circle.

Don’t let your weaknesses define you. We first met Colby at a pet adoption event in L.A. A man was walking her across the parking lot. “Is that dog up for adoption?” I blurted in spite of myself.

“You don’t want this dog,” the man replied. “She has epilepsy.”

Visiting Colby at the pound, I confronted the crudely-wrought sign on her cage that obscured the dog behind it. “EPILEPTIC!!!” The sign might as well have read, “Don’t Adopt Me!” She peered out from behind the sign and looked up at me as if to ask, “You up for it?”

We took her home anyway. Our vet prescribed a small dose of Phenobarbital. In the five years we had her, she never had a single seizure.

Remember who you work for. I eventually took Colby to a trainer, who set her up with an e-collar. She emerged a better-behaved dog, responding to the sound of a small beep from the e-collar. And by attaching a GPS antenna to a harness I could track her whereabouts on a hand-held device. Our hikes were transformed: Colby would hunt and I would hike, monitoring the GPS device and beeping her e-collar when she was out of range. We worked well this way, each aware of the other, checking in when necessary, doing what we were there to do.

Avoid the drama of others. Occasionally other dogs would try to pick a fight with Colby, but she was expert at avoiding eye contact while backing away from a brawl. Colby wasn’t interested in dog-on-dog rumbles, preferring the company of humans and assorted fleeing mammals. When she didn’t have a vested interest in the outcome, she just refused to play the game.

Ask for what you want. Colby was not a barker, but she made it clear when she wanted something. “Food” was a tentative handshake. “Ready for a hike” was a series of whimpers. “I smell a varmint” was a long, plaintive bray. And if she just wanted to hang out, she’d go sit on your lap on the floor, facing away from you as if announcing: “Prey vanquished!”

I did things for Colby I never thought I’d do for a dog. Shock collar? Never! A GPS? Please. Chemotherapy? Are you serious? But I did all those things and more for Colby and she paid me back in a thousand small ways. Colby taught me to stretch and adapt. She opened my mind to new ways of problem solving. I abandoned my bad habit of snap judgments. I learned my own capacity for patience. I’m a better dog owner because of her.

And I’m a better manager. Colby gives new meaning to Take Your Dog to Work Day. I know when to slow down and when to speed up. I know when to sit back and think. I know when to admit what I don’t know, and when to ask for help. And I know that even a crazy wayward coonhound can leave a lasting legacy.

There are people who have broken a leg skiing double black-diamond runs. Others have landed hard skydiving. Still others have taken a face-plant while running from rabid baboons. Me?  I tripped down the stairs of my office building’s parking structure on a Saturday. I ended up with a stainless-steel rod in my leg, screws in my ankle, and a Darvon-tinged memory of the most excruciating pain ever.

This turned out to be a hard-won karmic lesson: if you’re thinking of working on a Saturday, take the elevator.

Indeed, things go wrong, and many are the consequences of well-intended choices. If you’re trying to create a utopian society of polyglot mutants, for example, you have to brace yourself for what comes next. If you’re fleeing crazed primates, you’ve found yourself in the wrong place at the wrong time. If you’re trying to get a data governance program off the ground, you simply need to craft an unthreatening message for a non-disruptive program.

Except for this: Data governance is often a disruptive program. And we shouldn’t be afraid to manage that expectation. Like many corporate changes, data governance is hard before it’s easy. It has to be disruptive in order to become systemic.

Most of my new clients ask me where they should start data governance. And in that conversation there’s often a “BUT.” We need to convene a governance council, BUT…we can’t bother business people. We need to draft Guiding Principles…BUT we’ll need our legal guys to take a look. We need to appoint data stewards…BUT we can’t get headcount. We need data governance…BUT we can’t call it data governance.

It’s time for data governance at your company, and you—that’s right, you—are its footsoldier. Don’t sweat the battle scars, though. The worst that happens is that people raise their voices in meetings. They’ll insist that corporate data is already perfect. Passive-aggressive types will nod their heads while simultaneously making a mental note to question your authority. Or no one will do anything much at all.

Un-sheath your dagger, soldier, for there is work to do. On you, we rest our high expectations as we hand you a mug of mead, lay a laurel wreath atop your noggin, and send you a-field. On them? We sick the rabid baboons.

Break a leg, ugh, I mean: Good luck!

Photo by Abulic Monkey via Flickr Creative Commons License.

Recently the chair of a data governance council at a bank asked for my thoughts on how to handle dormant data. The data—some cryptic financial rollup tables that hadn’t been accessed in a few months—was taking up space on the data warehouse. More importantly it would soon be subject to a major financial metadata effort. My client didn’t want his staff spending time defining data that no one was using.

(When the topic of de-commissioning data comes up I think about Information Lifecycle Management (ILM), which is the management and storage of data as it changes over time, from its initial creation through its eventual use and disposition. We’ve talked a lot with clients about data being dynamic and having a lifecycle. We call this lifecycle the “data supply chain.”)

I asked my client a series of questions about the data. Why was it loaded onto the data warehouse in the first place? Did it map to critical business requirements? Who’d requested it? What business processes did it support? How latent was it? Ultimately after some discussion I recommended that my client pull the data from the data warehouse and archive it. Then we’d sit back and await the backlash.

A risky move? You bet. I compare this strategy to the one used in the Aristophanes play “Lysistrata.” As you’ll remember from high school English, in Lysistrata the women of Greece decide en masse to withhold sex from their husbands until there’s an end to the Peloponnesian War. This was the ultimate power play and gave the term “cease fire” a whole new meaning.

A week later my client received an e-mail from a data analyst in the finance department inquiring about the missing data. It seemed as if this analyst used the data to create quarterly top line measure reports for the CFO. She’d just returned from a 3-month sabbatical and wondered what had happened to her tables.

This conversation resulted in the addition of two new guiding principles for the data governance council:

1: Usage of the data on the warehouse would be regularly monitored.

2: Data unused for a period of 4 months or longer would be archived unless the data steward requested an exception.

These new guiding principles invited a new level of—dare I say—intimacy with less-visible data by data stewards who had until then been focused on high-profile or heavily-queried tables. But just because the data wasn’t widely used didn’t mean it wasn’t beholden to the standard policy-making and oversight processes established by the data governance council.

The lesson? Sometimes you’ve got to take something away to recognize its real value. Just ask those randy Greeks!

photo by nan palmero via Flickr (Creative Commons license)

I used to attend a certain conference popular with data modelers. Data modelers are a peculiar breed. They all know one another. They stop each other in the hallways between workshops, arguing over modeling conventions, debating semantic layers, and commiserating over user availability. During coffee breaks you can read the bubbles above their heads that say: “My management doesn’t really understand what I do.”

I know this because I myself was once a data modeler. I kept various iterations of an E/R diagram on my office wall. I could argue Kimball or Inmon with equal ease. I used the word “tuple” in mixed company. Known for lobbying executives to attend model walk-through sessions—yet never too confident to forget the requisite haul of baked goods—I once tried explaining the transgression of a “dangling foreign key” to a CFO who had innocently asked about customer-account linkage. My bad.

Then one night, two masked men threw me in the back of a van and forced me to drink an opaque elixir. The next thing I remember I was waking up screaming, “JUST GIVE ME MY FRIGGIN REPORT ALREADY!!!” with an incinerated copy of Ted Codd’s 12 Rules still smoldering under my bed.

That was years ago. I’m now starting to come around again to thinking that data modeling is important. Before you back up the Econoline, let me explain that though I do believe that modeling data itself is important, it’s the message around the data model that gets us into trouble. As much mastery as we’ve achieved around our data models and subsequent database designs, our attempts to convince business colleagues to appreciate our work are still met with eye-rolling and impatient sighs.

Our fanatical emphasis on the data model belies the more important point: that we have cultivated a set of disciplines to design information that enables business needs.

Nowadays I recommend data modeling as the foundation for broader and more visible data governance efforts. The same skills that have been classically applied to developing and maintaining a data model can in fact accelerate nascent governance efforts by translating business requirements into data requirements, deploying new business rules and policies, leveraging incumbent metadata, and formalizing data stewardship.

Indeed, show me a data modeler intimate with his company’s data requirements and sources, and I’ll show you the newest member of the Data Governance Council. With our clients we’ve discovered that the existence of a robust data model is a positive indicator of the company’s data governance readiness.

In my dilettante data modeling days I learned a lot about data relationships, structures, and usage. My colleagues and I applied that knowledge to broader business initiatives. We made sure that data had a seat at the table with every new business program.

So, my data modeler brothers and sisters: you may have new career “dimensions” to explore. What? You’re good, you say? Okay. I’m in your driveway right now in a big white van. Get in. Now drink this.

Photo by thunderchild7 via Flickr (Creative Commons license).