Guest blog: Reactions to the Data Citizens Bill of Rights

TomRedmond ReactionstoDataCitizensBillofRights

I recently had the chance to meet with Tom Redman, aka “the Data Doc.” During our conversation, we discussed our Data Citizens Bill of Rights. He thought it was an interesting piece of content, and so we’ve invited him as a guest blogger to share his perspective.

I recently had the opportunity to review the Collibra Data Citizens Bill of Rights .  And my simple summary is this:  The notion that people, for no other reason than they use data, have several essential rights within their company, is an intriguing one.  There are obvious parallels to the Bill of Rights in the United States, which has certainly stood the test of time.  Wouldn’t it be terrific if people could find and access the data they need, trust it, and use it as they see fit? It is a compelling objective.

At the same time, the Data Citizens Bill of Rights cannot stand alone.  It is completely silent about how such rights would be secured.  There is nothing about the roles data citizens must play, nor the responsibilities they must take on.  In the United States, citizens are expected to contribute—they vote, pay taxes, serve in the military, and so forth.  Further, the Bill of Rights is associated with the Constitution and an infrastructure to secure the rights it guarantees.

Thus, for the Data Citizens Bill of Rights to prove helpful, it must answer the question, “who secures these rights and how?”  It can do so either directly, or by attaching to “organizations for data” that help secure those rights.  I have several specific suggestions for getting started.  But these are only a start.

First, some background.  I use the term “organization” (after Roberts, The Modern Firm) to include:

  • People,
  • Structure,
  • Policy and Control, and
  • Culture

Unfortunately, as I’ve observed and you well know, “today’s organizations are ‘unfit for data.’”  For example:

  • People: There are too few skilled people,
  • Structure: Silos get in the way of data sharing,
  • Policy and Control: Responsibilities for data are mis-assigned, and
  • Culture: Companies claim that “data is our most important asset” with no idea what that means.

Importantly, data management, as currently practiced, is not powerful enough to deal with such issues (indeed, it probably contributes to them!).

Let me use a specific example, data quality, to illustrate further.  Today, people are naturally suspicious of the data they need to complete their day-in, day-out work; to plan; to make decisions; and to conduct forward-looking analyses.  So they search for shortcomings in the data and go to elaborate lengths to deal with them.   When they find correctable errors, they make corrections in their private copies and go about their work. This business of searching for and correcting data errors is tough, often performed under considerable time pressure and without the right resources.  One almost admires the efforts people make to ensure the data they use is up-to-snuff!

Not so fast!  It is rare indeed that people also seek to get to the root cause, make corrections in their source(s), or even take time to advise original sources of the errors they found. Best case, this means that others will waste time and resources re-making corrections. Worst case, they leave others will be victimized by bad data.

Please reflect on this example for a minute.  Such behavior is completely unacceptable!  Yet today, people do just this all the time, and without consequence.  There is nothing in the Bill of Rights to counter it and such behavior directly contravenes Articles II (the right to trust) and IV (the right to accurate data)!  All by itself, this example makes clear why more powerful organizations for data are essential.

It bears mentions that there should be considerable urgency in putting better organizations for data in place. After all, the cover of the May 6, 2017 The Economist proudly announces that “data are now the world’s most valuable resource.”

Addressing behaviors such as those in the example above, or more pro-actively, getting the right people in the right places with the right roles, responsibilities, and doing the right things; putting the right structures in place; developing and implementing the right policies and controls; and building a culture that values data will require fundamental changes up, down, and across the organization chart.  Such changes can make the rights promised in the Data Citizens Bill of Rights, some of them anyway, a reality.

While it will take some time for a full transformation to occur (bear in mind that today’s organizational forms were developed to support the Industrial Age and have proven highly effective. They should not be cast out lightly), some good ideas are emerging.  In particular, many companies are getting good traction on data quality.  When they get the right people in the right roles with the right responsibilities, quality improves quickly.

For most of the rest of this review, I propose and explore two roles and four responsibilities for data citizens that can help make Articles I – IV (on quality) a reality.

It is clear enough that data citizens play two critical roles, as data customers and data creators. As data customers, they use data created by others to do their work. And in doing their work, they create more data, used in turn by others. Failure of data citizens to assume basic responsibilities in these roles are the two primary reasons so much data is bad.

First, consider customers.  As the example above illustrates, customers are more likely to correct errors themselves than to speak up, sort out what they really need, communicate their needs to those who create data, and provide feedback.  Unless they do these things, they can have no reasonable expectation of high-quality data!

Next, consider creators.  In this role, too many data citizens are blissfully unaware that what they do impacts others. This must change. They must reach out to customers to understand their most important needs, measure the quality of data they provide against those needs, conduct improvement activities to close gaps, and put controls in place to stop root causes from recurring.

Implied here is a new way of working.  Data citizens simply must get out of their silos and work together. While I find that end-to-end process management provides the superior infrastructure for this, in most cases, simply applying the customer-supplier model works extremely well.  In the figure below, I put the data citizen in the central role. The flow of data goes left to right and of special importance here, are the requirements and feedback, or communications, channels. Data citizens simply must build and maintain these channels.

Reactions to the Collibra Data Citizens Bill of Rights

I want to add a fourth responsibility for all data citizens.  It stems from a social factor that contributes mightily to the poor state of quality in most organization.  Simply put, too many data citizens, at all levels, are way too tolerant of bad data and the harm it causes. Things simply do not improve until they start demanding better. Thus, the fourth responsibility is growing increasingly intolerant of bad data!

Thus data citizens have four basic responsibilities for data quality:

  • Ensuring the data they need to do their jobs is of high-quality,
  • Ensuring that they create high-quality data (in the eyes of customers, those that use this data),
  • Building the communications channels needed to between customers and creators, and
  • Becoming increasingly intolerant of bad data.

And of course data citizens must also play roles and accept responsibilities for privacy, security, analytics, and so forth. Further, ensuring that these responsibilities are met, and making the Data Citizens Bill of Rights feasible, also takes management infrastructure. These are subjects for another day.

Tom Redmond

Tom Redman, the “Data Doc,” helps companies, including many of the Fortune 100, improve data quality.  Those that follow his innovative approaches enjoy the many benefits of far-better data, including far lower cost.  He is the author of Getting In Front on Data: The Who Does What (Technics Publications, 2016) and Data Driven (Harvard Business Review, 2008).  His articles have appeared in many publications, including Harvard Business Review, The Wall Street Journal and MIT Sloan Management Review.  Tom started his career at Bell Labs, where he led the Data Quality Lab.  He has a Ph.D. is in Statistics and two patents. Follow Tom on Twitter.

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