I only heard the term a few weeks ago in a presentation at Social Media Week NYC about Open Data for the Public Good about the challenges of working with governments to open their data.
Data Shame is an organization’s resistance to opening up their data to outsiders because they fear that, if they did make their data visible, someone would find out how bad the organization’s data really is.
What struck me about the concept was not the idea that an organization would have a less-than-perfect data set. Every research paper I’ve ever read has had an inadequate data set— that’s the norm. What was surprising was the idea that organizations would experience shame about their data.
‘Data’— cold, hard, rational — doesn’t seem to fit with ‘shame’. Shame is so acutely human it’s hard to connect it to the numbers, labels, and measures that pile up inside machines.
Whoever coined this phrase was articulating something painful about the experience of our data being inadequate. And what I wondered was— how is pain like shame involved in creating boost relationships between organizations?
Business to Business Relationships that Boost Both Partners
Boost relationships require a particular type of openness between the partners. For the relationship to give the participating organizations a ‘pop’ of energy, a pump of momentum, that they use to carry themselves forward, the relationship has to be closely fitted to what the partner organizations deeply need.
Boosts can come from (1) effective transactions and exchanges that accelerate what is, (2) combining our organization’s strengths to create something new, and (3) helping each other fix something that’s not working well.
If an organization needs something fixed (e.g., their inadequate data), but are reluctant to share this because they feel shame about it, how can they create the kinds of boost relationships that will actually help them? Here’s what I’ve come up with so far:
All Data Is Inadequate
Data shame is a “rampant if little acknowledged condition.” It’s rampant because its nearly impossible to have built a data base that’s error free and perfectly suited to today’s information tasks. Data shame is little acknowledged because who wants to let other businesses know that their business has done something (anything) poorly?
Think about it. Who wants to tell a prospective business partner
“Hey, our organization is kind of broken because we messed up. Can you lend us a hand?”
“Help seeking” is too often interpreted as a sign of weakness. In the business world, admitting weakness is akin to saying
“Please, come take advantage of us. Get a better deal because we’re not able to take care of ourselves.”
We’ve been taught to expect that when a business sees a weakness in a potential partner, that business is less likely to help than it is to pounce. Thus, we don’t feel good about business weaknesses, and in some cases we feel shame.
What Shame Says About An Organization
Shame is about being ‘not good enough’.
Data shame reflects that the organization knows it’s screwed something up with their data. The data might show something embarrassing about the organization’s results or the data maybe be misused. Sometimes the database itself is poorly designed or incomplete. And even worse, the data that exists and the forms in which it exists can reflect bad decisions about what the organization thought was important enough to measure.
Internally, data shame creates a challenge for the organization because the data isn’t able to meet the needs of the people who have to use it. And when organizations want to work with each other, data shame gets in the way of building a relationship.
Dealing With Data Shame
The organization with the inadequate data has to deal with data shame, and so does its potential business partners.
The organization with the inadequate (shameful) data has to find the courage to disclose that their data is, indeed, flawed. They have to own the fact that imperfect data is what they’ve knowingly been using. Imperfect data is part of what they’ll bring to the partnership. And, the organizational flaws that the data exposes come along too.
In projects where our firm has been brought in to help people lasso their data, we have come to expect the moment of pause, then followed by apologies and looks of mild embarrassment, just before our clients reveal the uncomfortable secret of their data’s deficiency… and the workarounds, assumptions, and wild guessing that they are forced to do in order to get work done despite imperfect information.
Disclosing that their data is inadequate makes an organization vulnerable, because data shame demonstrates one or more types of organizational incompetence.
Let me illustrate with a simplified example drawn from the Open Data talk: A city’s Housing Agency wanted help with building a data-reporting application that showed which homes in a damaged area had been inspected, which ones had been condemned, and which ones were already demolished. When the consulting developers started working with the Agency’s data, they uncovered entire categories of missing data and incorrectly entered data. When they went to analyze (learn something from) the data, the consultants discovered that different inspectors from the Agency had visited the same houses to evaluate them twice, demonstrating inefficient routing. They also discovered that inspectors had given the same house different evaluations, suggesting that their conclusions were arbitrary. In the meantime, citizens were waiting for insurance checks that depended upon their house’s evaluation.
Bad data, bad information, ineffective management, and upset citizens. No wonder they were reluctant to share their data.
Can we get past the shame of shame?
Given how exposed and embarrassed an organization could be if others discovered their inadequate data, what could encourage them to share their data anyway?
It turns out that a little disclosure can go a long way in building a strong relationship with a business partner.
Disclosure. Disclosing the organization’s weakness(es) and asking for a potential partner’s help is the only way to get the kind of boost that fixes things, because only real disclosure shares the specifics about the organization’s deep need.
Acceptance. If an organization reveals their data deficiency, their potential partner needs to affirm that the business relationship is important enough that they’ll move forward — despite the data deficiency and the organizational failings the data deficiency suggests.
Helping. Once the potential partner has a sense of what the organization’s weaknesses actually are, they can establish a connection that provides specifically what the organization genuinely needs. In the example of the Housing Agency, the consulting partners might offer help with data base design (a new facet of the relationship) as well as the mobile apps for inspectors that they were initially contracted for.
Disclosure + Acceptance + Helping = Boost
The disclosure-acceptance-helping dynamic does more than build functional interdependence in the business to business relationship. It also helps to build trust.
Being able to trust a business partner to accept your organization’s weakness and help you learn how to work through it expands the potential for the relationship itself. Trust makes it more likely that the two businesses can continue to explore how they might boost each other’s capacity.
If data insufficiency is rampant, then organizations shouldn’t be ashamed of it. And good partner organizations will be ready to deal with it. As Gartrell explained:
The thing is, data could always be better. And helping define and get to ‘better’ is what we do.
Gartrell noticed that the organization his was partnering with worked through their data shame. Instead of holding back, he writes that the organizations found
“There is a strong sense of commitment to change, and I’m gathering that it is now being accompanied by a growing sense of optimism that the changes they seek are within reach.”
Should organizations feel Data Shame?
Of course, we should question whether or not organizations ‘ought’ to feel shame about their data.
Data systems are never complete, 100% accurate, or fit to the questions we have weeks, months and years after they were first designed.
No organization ever has perfect data.
Where the shame resides seems to be in what that data says about the organization: The organization is inadequate, The organization is imprecise, The organization has low standards, and so on.
Data shame is actually organizational shame.
But maybe the way that organizations learn to deal with the dynamics of shame around data can help them deal with the shame around organizational inadequacy and failings. Maybe working through ‘data shame’ can create a special kind of opportunity:
If we can figure out how to help organizations disclose, accept, and help each other with data inadequacies, we might be able to open relationships up so that organizations help each other fix deeper inadequacies. These might be the kinds of boost relationships that lead to real organizational transformation.