Some definitions (courtesy of Wiktionary):

• status quo — the way things are, as opposed to the way they could be
• inertia — The property of a body that resists any change to its uniform motion
• cognitive dissonance — a conflict or anxiety resulting from inconsistencies between one’s beliefs and one’s actions or other beliefs

The first two of these can be applied to any sort of technology or process change being introduced to an organization — entire careers and companies are built around trying to figure out how to effectively drive change within organizations. In the case of data management, the third defintion — cognitive dissonance — comes into play as well.

As a brilliant and phenomenally handsome man* once said, “Customers are people, and people are messy.” Customer data is inherently incomplete and imperfect. Any process or system that captures and stores customer data stores flawed data as soon as it rolls out for two reasons:

• It is not reasonable to add to any process all of the overhead required to rigorously capture and validate all attributes of a customer — it’s a balancing act between the efficiency of the process and the quality of the data captured
• Customer data decays, and it decays a lot more quickly than we like to admit; customer data maintenance tends to be an afterthought that gets addressed only after time has degraded the data to the point that it starts causing the company real problems

Once we hit the point where we really need to tackle our customer data management head on, we have two options, of which one option is completely inviable:

1. Throw out all of our customer data, customer data processes, and customer data systems and start over, but “do it right this time”
2. Identify the most broken parts of our processes and start fixing them — going after the lowest cost and highest benefit ones first and then working our way down the list until we hit a satisfactory point (which is, typically, never)

Clearly, the first option is not an option. No company would survive if they tossed out their customer base, barred their doors, and conducted no business for a year or two while they rebuilt their process and technology infrastructure.

That leaves us with the second option (technically, “do nothing” is an option as well, but that’s only an option if the pain hasn’t reach the point where it’s not an option!), and, thus, we reach a cognitive dissonance conundrum:

We know our customer data is dirty — customer service reps complain about the number of duplicate records in their systems, sales reps complain of the incomplete pictures they have of their customers (which hinders their ability to prep for and conduct customer visits), marketing complains that they can’t effectively segment and target their database because the customer data is bad, customers complain because the company keeps screwing things up in one way or another…

BUT

…as we start to explore and design replacement processes, we realize that these processes are going to be inherently imperfect, too. We may accept that the new process will be better (even significantly so), but we obsess about the flaws.

We don’t want to repeat the mistakes of the past and roll out something that is not bulletproof — a chink in the data management armor is a chink, no matter how small. So we obsess about the chinks. We propose process changes to accomodate the identified gaps. Even for the gaps that are purely theoretical (“yes, I see, but what if the poles reversed at the exact same point that pigs learned to fly — the process would break!”) We’re trying to do the right thing. We’re aiming for perfection — for a flawless process.

But we’re talking about customer data, and customers are people, and people are messy.

We find ourselves (and/or the people who will ultimately need to adopt the process) paralyzed, caught in an endless cycle of Visio vetting and process rework, perpetually getting halfway to the “perfect” process, but never actually getting there. At some point, due to impatience or frustration, someone stands up and yells, “Enough! Just build what you’ve got!”

And then we realize we’ve designed a process that is so complex and unwieldy that the cost to implement it would wipe out any hope of the company having a profitable year for the ensuing decade.

Of course you’d like a more tangible example:

Let’s say we’re trying to clean up our customer’s mailing addresses (which, thankfully, is now an exercise from my past, but that’s more a digression for a discussion over drinks than for a blog post!). Let’s say that, for any 1,000 customer addresses, we have conclusively demonstrated that at least 50 of them are bad — the postal service is going to struggle to deliver mail sent to them, and the postal service is going to fail more often than not. Now, let’s also say that we’ve demonstrated that, by introducing some automated cleansing processes, we can: 1) identify those 50 addresses, 2) “fix” 30 of them, and 3) flag the remaining 20 as being known problems that need some sort of manual intervention. Let’s say that, rather than 1,000 records, we’re talking about 10 million.

“Hurray!”

“Sounds great!”

“Awesome!”

“Gimme some of that!”

Ah…BUT…

…we have also  determined that, as part of those automated cleansing processes, we might actually take 1 of the 950 addresses that were already good…and make it worse.

Logically, the project should still be a go. We’re making 30 addresses better and only might be making a single address worse!

Ohhhhh…that single address. That molehill that eats its Wheaties, regularly applies cream provided by a shady character, and injects itself in the buttocks with a substance its cousin purchased over the counter in the Dominican Republic. The molehill grows. It grows quickly. Suspiciously quickly…yet no one seems to notice. It becomes a hill, and then a big hill, and then a mountain! The project manager is left scratching his head and wondering how a theoretical aside in a meeting three weeks ago has now become a virtually insurmountable issue that has put the entire project at risk of ever being implemented!

Cognitive dissonance — simultaneously recognizing that things are bad and must be fixed, but also accepting that the status quo is “right.”

The answer? I’d like to say it’s just a matter of putting the dissonant perspectives side by side and forcing objectors to reconcile them. That should work, right?

Alas!

As it happens, the current debate about healthcare reform in the U.S. prompted James Surowiecki to right a column on Status-Quo Anxiety in The New Yorker a couple of weeks ago. Surowiecki discusses the “endowment effect:”

“…the mere fact that you own something leads you to overvalue it. A simple demonstration of this was an experiment in which some students in a class were given coffee mugs emblazoned with their school’s logo and asked how much they would demand to sell them, while others in the class were asked how much they would pay to buy them. Instead of valuing the mugs similarly, the new owners of the mugs demanded more than twice as much as the buyers were willing to pay.”

Surowiecki goes on to relate this effect to the healthcare debate: “What this suggests about health care is that, if people have insurance, most will value it highly, no matter how flawed the current system.”

The same applies to customer data management all too often — we know we have a flawed system, but it’s the system we have, gosh darn it, and I don’t want your new system if I can find any imperfections in it!

This really has been a farewell post of sorts. Rambling, yes. Academic, yes. Lacking any prescriptive solution. But, hopefully at least a little entertaining, and maybe even with an insight or two that may come in handy to you. Look for a topical shift to measuring digital media going forward.

So long, and thanks for the fish!

* Dramatic license — I said that in this post, and “brilliant and phenomenally handsome” is perhaps a bit of an overstatement.

Photos courtesy of Olof S and bobster855