People who commented or wrote to me talked about the areas that reflect their roles and how they feel responsible for data in their career.
Some sought truth in governing data entry in fields for product codes, income brackets or gender and were concerned about the high incremental costs of fixing the root causes of the last 10 or 5 percent of foundational data and whether it was worth it. That is important and nuanced work, as Jim Harris spoke to in his blog not long ago.
But more readers were looking at the distinction between foundational data and the way it is assembled and interpreted once it has been reliably produced. Some focused on data definitions that led to the numbers chosen to steer decisions based on rules and policies. And others looked at the application of hierarchies and metadata that combine and recombine data reporting and performance measurement.
A small but clear majority felt we are getting past the “single view” mentality as a prescription for all our data problems. Jon B wrote that there’s seldom one truth, especially in metadata, and that is just the nature of things. “Sales, operations, product, and even the customer look at things in different ways. They may 'agree' on what is in the ERP master, but will use different ways, usually Excel, to look at it the way they need to get things done. Plus things are changing in terms of customers and products so quickly a rigid data governance process may actually slow down progress.”
Wayne K. bought into Chai Lam’s view in last week’s article about stewardship being the heavy lifting of operationalizing data in the course of business, though unlike Chai, gave the job to IT. “Our goal should not be "a single version of the truth" but how do we enumerate, rationalize and manage all the different versions of the truth. I see this as a driver for new essential work for IT people to do and that's a good thing.”
Peter P took a temporal view of “truth versioning” where veracity depends on the time frame. “A person may be located at one address today, but another tomorrow. In many business applications, we need to maintain both locations in relation to the time frame when they were 'true.'"
He also took the wider view of attributes and entities as contextual states. "’Customer’ is not the entity. A customer is a role played by a person, organization or group, which is the entity ... ‘Bob’ can be a student, a donor, a supplier, an employee and even a customer all at the same time ... So how can they possibly know the "truth" about Bob across these multiple roles?
And, we all want a piece of Bob for our own purposes, don’t we?
Richard R said the needs and timeframe of the user outweigh the value of creating of data suitable for all purposes. “A LOB manager in the FMCG industry can't wait until every minor adjustment has been processed to see their sales figures (timeliness), any more than a mortgage manager would make a loan decision based solely on a current snapshot without considering the customer's history (interpretation).”
By these parameters, Richard said, lower quality data can be more useful than perfect data. “As long as the information is good enough for the recipient to make sound business decisions, taking longer to make the data more accurate actually diminishes its value rather than improving it.”
Readers span a curve of preference between empirical and utilitarian views, perhaps the way a chemist’s view of quality and purpose is different than a butcher’s. And let’s be honest; compromise is part pragmatics and part politics.
That might make a good visual to show that managing data is not a guns and butter problem and that accuracy and compromise will continue to coexist across the span of information management.