Data Strategy Component: Govern

This blog is the final installment in a series focused on reviewing the individual Components of a Data Strategy.  This edition discusses the component Govern and the details associated with supporting a Data Governance initiative as part of an overall Data Strategy.

The definition of Govern is:

“Establishing, communicating and monitoring information practices to ensure effective data sharing, usage, and protection”

As you’re likely aware, Data Governance is about establishing (and following) policies, rules, and all of the associated rigor necessary to ensure that data is usable, sharable, and that all of the associated business and legal details are respected.   Data Governance exists because data sharing and usage is necessary for decision making. And, the reason that Data Governance is necessary is because the data is often being used for a purpose outside of why it was collected.

I’ve identified 5 facets about Data Governance to consider when developing your Data Strategy.  As a reminder (from the initial Data Strategy Component blog), each facet should be considered individually.  And because your Data Strategy goals will focus on future aspirational goals as well as current needs, you’ll likely want to consider different options for each.  Each facet can target a small organization’s issues or expand to focus on a large company’s diverse needs.

Information Policies

Information policies are high level information-oriented objectives that your company (or organization, or “governing body”) identify.  Information policies act as boundaries or guard rails to guide all of the detailed (and often tactical) rules to identify required and acceptable data-oriented behavior.  To offer context, some examples of the information policies that I’ve seen include

• • “All customer data will be protected from unauthorized use”.
  • “User data access should be limited to ‘systems of record’(when available)”.
  • “All data shipped into and out of the company must be processed by the IT Data Onboarding team”.

It’s very common for Data Governance initiatives to begin with focusing on formalizing and communicating a company’s information policies.

Business Data Rules

Rules are specific lower-level details that explain what a data user (or developer) is and isn’t allowed to do.  Business data rules (also referred to as “business rules”) can be categorized into one of four types:

• • These are the “things” that represent the business details that we measure, track, and analyze. (e.g. a customer, a purchase, a product).
  • The details that describe the terms and related details about a business (e.g.  The customer purchases a product,  Products are sold at a store location).
  • These are the details associated with the various items and actions within a company (e.g. The company can only sell a product that is in inventory).
  • The distillation or generation of new rules based on other rules. (e.g. Rule: A product can be purchased or returned by a customer.  Derivation: A product cannot be returned unless it was purchased from the company).

While the implementation of rules is often the domain of a data administration (or a logical data modeling)  team, data governance is often responsible for establishing and managing the process for introducing, communicating, and updating rules.

Data Acceptance

The term quality is often referred to as “conformance to requirements”. Data Acceptance is a similar concept: the details (or rules) and process applied against data to ensure it is suitable for the use intended.   The premise of data acceptance is identifying the minimum details necessary to ensure that data can be used or processed support the associated business activities.  Some examples of data acceptance criteria include

• • All data values must be non-null.
  • All fields within a record must reflect a value within a defined range of values for that field (or business term).
  • The product’s price must be a numeric value that is non-zero and non-negative.
  • All addresses must be valid mailable addresses.

In order to correct, standardize, or cleanse data, data acceptance for a specific business value (or term) must be identified.

Mechanism

A Data Governance Mechanism is the method (or process) to identify a new rule, process, or detail to support Data Governance. The components of a mechanisms may include the process definition (or flow), the actors, and their decision rights.

This is an area where many Data Governance initiatives fail. While most Governance teams are very good in building new policies, rules, processes, and the associated rigor, they often forget to establish the mechanisms to allow all of the Governance details to be managed, maintained, and updated.  This is critically important because as an organization evolves and matures with Data Governance, it may outgrow many of the initial rules and practices.  Establishing a set of mechanisms to support modifying and updating existing rules and practices is important to supporting the growth and evolution of a Data Governance environment

Adoption Oversight

The strength and success of Data Governance shouldn’t be measured by the quantity of rules or policies.  The success of Data Governance is reflected by the adoption of the rules and processes that are established.  Consequently, it’s important for the Data Governance team to continually measure and report adoption levels to ensure the Data Governance details are applied and followed.  And where they challenges in adoption, mechanisms exist to allow stakeholders to adjust and update the various aspects of Data Governance to support the needs of the business and the users.

Data Governance will always be a polarizing concept. Whether introduced as part of a development methodology, included within a new data initiative, required to address a business compliance need, or positioned within a Data Strategy, Data Governance is always going to ruffle feathers.

Why?

Because folks are busy and they don’t want to be told that they need to have their work reviewed, modified, or approved.  Data Governance is an approach (and arguably a method, practice, and process) to ensure that data usage and sharing aligns with policy, business rules, and the law.  Data Governance is the “rules of the road” for data.