“Data literacy” is misleading – What does leadership really need to know?

Metaphors are hard to implement.

This is the first in a series of blog posts to address “data literacy.”  Data literacy is in quotes, because the current accepted definition is wholly inadequate to address the real problems.  To say (using the accepted definitions) that a data driven organization needs data literacy to succeed, is to miss 80% of the problem. 

Most current definitions focus on “teaching management to use, interpret and communicate with data.  This is closely connected to a dictionary-based definition of “literacy.”  Most current definitions focus on “teaching management to use, interpret and communicate with data.  This is closely connected to a dictionary-based definition of “literacy.”  Nothing is wrong with that.  But to turn around and say “teaching data literacy will result in data driven” is to ignore the hard issues.  The current definition of literacy is a requirement as part of a training plan.  It won’t get the job done. For these blogs I use a different definition. Literacy is a bad word for this. Competency is better but we seem to be stuck with a debate on literacy. 

“Data literacy is the ability to find, access, analyze, interpret, apply, manage and communicate with data, while being aware of the opportunities, ramifications and risks.”

The purpose of this blog post is to refine the perceptions of literacy and provide the requirements for what leadership and management needs to know. This means we need to go beyond the “reading and interpretation” definitions.  Portions of these blogs are from my book “Data Governance, How to Deploy etc.” second edition supplemented with new use cases and examples.

An organization’s leadership can successfully approach data governance two ways. They can embrace it as part of the process to get to monetization of data assets, plunge into An organization’s leadership can successfully approach data governance two ways. They can embrace it as part of the process to get to monetization of data assets, plunge into artificial intelligence, or lower costs, and therefore support the capabilities required for that to happen.  The second way is to set a vision for an organization with better managed data, authorize the necessary capabilities for the various steps that will be taken, and then let subordinates work out the details.  Either way, there is a mandatory set of concepts in which organizational leadership must be made literate.  To be frank, ten years ago, it would have been adequate to just let the subordinates work it all out. 

For example, a very common barrier to success of data governance starts with the impression that it is a “new department,” or a new means to fix data issues.  Far too often I have heard an executive say, “Data stuff – oh, that goes to data governance to be fixed.”  To leave this impression go uncorrected is to ask for a mountain of problems later.  

But data is now such a pervasive and mandatory aspect of organic growth that leadership needs to be more than just aware; they truly need to develop a solid level of understanding of those mandatory data concepts.

The concept of Data is an Asset 

As often stated, 21st century organizations need to manage data as an asset.  But what does that mean?  “Information is an Asset” is an extremely common statement, and probably the most common information principle published within organizations.  The subsequent explanation is that assets are managed, so information has to be managed.”    

For data governance to work, “asset” must be more than a metaphor.  While many experts discuss data value appearing on a balance sheet (Laney), there is a long road before accounting methods catch up to that level.  You also need to look at the Liability side of a balance sheet when discussing data and information – because it can hurt as well as help.

The “value” of data appears when it is used, such as in deciding.  Conversely, the negative value of data happens when data is used incorrectly or is incorrect when used.  All other data activities are essentially sunk costs. This is key for leadership to understand. Using data (the current definition of literacy) is the only way to improve organization value with data.  But there is a COST to getting there. You cannot focu on the benefits and not understand the COST and call yourself a leader. 

Data governance plays a key role in the definition and treatment of data assets.  “Data as an asset” means data CAN be used as an asset through DG ensuring its proper treatment.  

Asset treatment means DG goes beyond watching projects do cool things with data or clean up a tactical issue.  Small victories are good, but eventually the organization is overwhelmed.  A colleague of mine calls this “data whack-a-mole,” referring to an old carnival game.  This type of cultural data fatigue costs organization trillions per year.2

The concept that Data Governance IS GOVERNANCE 

If the concept of managing information assets in a formal manner is accepted, we need a process to ensure that management takes place—and is being done correctly. Unplug your technology thinking and turn on your accountant thinking. Accountants manage financial assets. Accountants are governed by a set of principles and policies and are checked by auditors. Auditing ensures the correct management practice of financial assets. Principles, policies, and auditing accomplish for financial assets what data governance (DG) accomplishes for data, information, and content assets. 

DG is defined in the Data Management Body of Knowledge (DMBOK) as, “The exercise of authority, control, and shared decision making (planning, monitoring and enforcement) over the management of data assets.”3  In turn, governance is defined as, “The exercise of authority and control over a process, organization or geopolitical area. The process of setting, controlling, and administering and monitoring conformance with policy.”4 This definition is, of course, roughly synonymous with government.

Slightly different definitions are often stated with an emphasis on the policy and programmatic aspects of DG. An example of one used in my consulting work is, “Data governance is the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline and enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets.” Regardless of style of definition, the bottom line is that DG is the use of authority combined with policy to ensure the proper management of information assets.

Starting about 2015, I began to use a shorter definition to avoid controversial words like “accountability” when the situation became tense.  “Data Governance is a required business capability if you want to get value from your data.” 

To get started with this conversation on “data literacy,” we need to make sure all the conversation about the wonders of using data is grounded in the understanding that all the glitzy value comes at a possible cost.  That cost is simple understanding that data can affect the balance sheet, as an asset and a liability and that Data Governance is part of corporate governance.  (We will talk about the real cost of DG in the 3rd blog in this series)

The next entry will cover what leadership needs to know about the concept of separation of duties as it applies to data governance. 

1 Ladley, John “Making EIM Work For Business,: 2010, Morgan Kaufman
2 Bad Data Costs the U.S. $3 Trillion Per Year, Dr Tom Redman, Harvard Business review, 2016 https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
3 DMBOK ,2.0, DAMA Publication, 2017
4 Ibid

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