A Data Governance (DG) roadmap provides a detailed plan or strategy, normally with a list of tasks that must be accomplished for improving or “maturing” a DG program. It should also list the people who are responsible for completing specific tasks. A Data Governance roadmap often includes incorporating Data Governance best practices and is typically based on a DG assessment. (A “maturity assessment” is often used for this purpose).

A Data Governance roadmap is a communications tool. It provides management, staff, and investors with a plan for moving forward to achieve the DG goals.

Communication is an important part of a Data Governance roadmap. Publishing and distributing the roadmap to the entire staff provides a schedule and lets people know how long the journey will take. (Periodic updates will keep people engaged and explain delays or early completions and can be used to supply educational tidbits.)

If the organization does not currently have a Data Governance lead, this would be a good time to hire one (or promote from within), and then put that person in charge of creating the roadmap. 

The Assessment

An assessment of the Data Governance program is often made before developing a roadmap. While an objective assessment does have value, subjective feedback represents human desires and can be included. 

An assessment can help an organization examine its Data Governance program for weaknesses and out-of-date processes, and using a maturity assessment, for most organizations, is the most efficient way to do it. Organizations often request a Data Governance assessment to find out:

• What is needed to develop a data warehouse, or another initiative supporting business intelligence
• The current state of metadata management
• The current Data Governance processes
• The ability to adjust to technical or organizational changes 
• About desired infrastructure or architectural changes

An in-house maturity assessment of the Data Governance program seems to be the most common way of gaining an assessment. Versor and Datacamp offer online assessments. Recommendations from an outside contractor should be viewed with a certain amount of skepticism, as they may receive a commission for products or services they “sell.” Alternative products and services should be investigated before making any commitments.

A good assessment should include a review of the Data Governance changes and improvements already made, to see what is working. This allows businesses to learn what is working, what is not working, and the reasons. (During the assessment, it should not be forgotten that Data Governance was originally developed to provide accurate, quality data, and still has that purpose.) A good data maturity assessment should supply answers for the following questions:

• What technologies and software are currently being used?
• What are the business goals?
• What organizational changes need to be made?
• What is needed to accomplish each goal or change?

The Data Governance Roadmap

Building a DG roadmap requires an understanding of what needs to be done and how to do it. 

A dream list of Data Governance improvements should be created, with feedback from the staff. Some staff may have suggestions that would make their jobs easier, which management or the techs hadn’t considered. 

The early stages of developing the Data Governance roadmap should involve a variety of people, particularly those using the data. (It should be emphasized that the list will be cut to make the process affordable, and that it is a “dream” list.)

Keep it simple. The easier the process for both the roadmap builder and those providing input, the better. A large whiteboard can be quite useful to express ideas and store information and suggestions. 

An important aspect of the Data Governance program is its ability to respond appropriately to the various laws and regulations developed by other countries (and California). This helps to minimize regulatory violations (GDPR, CCPA) and helps to improve data privacy and security. It should also improve the system’s operational effectiveness by eliminating duplication and rework.

When you have defined both the goals and the courses of action, related goals can be grouped into groups called “initiatives.” These initiatives are loosely defined and can be projects or software programs, or both. (As with the dream list, some initiatives may have to be slashed.)

Aligning the Data Governance Roadmap with the Business Plan

A business plan is a formal document that defines the goals of a business in detail and its plans for attaining those goals. (It’s essentially another roadmap.) It lays out the business’s goals from financial, marketing, and operational perspectives.

These goals should be built into the Data Governance roadmap. The broad goal of increasing online sales may be a business goal that can be broken down into smaller goals, some of which are covered by Data Governance. For example, many businesses have discovered that cross-selling can increase their profits by creating relevant and useful consumer experiences. Cross-selling – which includes the use of recommendation engines – is a powerful tool supported by DG.  

A good Data Governance program can be used to build trusted data sets, which are needed for effective cross-selling. Examples of this include Netflix, which uses a “collaborative filtering process” to examine the viewer’s likes and dislikes, and then offers a list of movies and shows it predicts the viewer will enjoy. Similarly, Amazon offers customers products they predict will be personally relevant.  

Including Best Practices

A well-governed data system will improve access to data and support the efficient use of analytics and business intelligence. Policies define what data is relevant, accurate, and accessible. Here are some of the key best practices you should follow:

• Use automation as much as possible. Automation reduces human error significantly and operates significantly faster than humans. 
• Apply format standards for the data. Software can be used to enforce these standards during both post-processing and data ingestion.
• Think big picture but avoid unnecessary complications. Always apply a philosophy of keeping things simple. 
• Establish Data Governance team roles, including a Data Governance lead. Data managers may be assigned to work with data users for training and guidance and to facilitate communication.
• Establish standards for the metadata that promote the organization’s business goals and allow for data to be reused. Classify and tag stored data, unmanaged data, and incoming data. 

Changing the Workplace Culture

Changing the behavior of individuals is an important aspect of developing a Data Governance roadmap. Changes don’t have to be massive and, for the most part, involve learning only 20 to 40 new vocabulary terms and altering how people handle data. 

If the position of Data Governance lead doesn’t currently exist, it’s time to create it. The Data Governance lead manages how staff organize and work with data. This person becomes central to organizing and maintaining the technical and the cultural changes.

Changing the language of a culture alters the culture, as language is the foundation of communication, and cultures are built on communication. 

A good place to start changing the culture is the distribution of a list of vocabulary words to all staff, and which management should memorize and begin using with staff regularly. Adopting a standardized workplace language (shop talk) requires the staff to learn and use it (examples include business intelligence, metadata, data analytics, regulatory compliance, some technical terms, etc.). 

Management must set the example by using the new vocabulary and terminology.

Modifying staff behavior becomes the third step. This has to do primarily with how they operate their computers and handle data. How management relates to staff, or how customers and staff interact, may change very little. It is how the staff interacts with the data that changes.

A Work in Progress

Developing a Data Governance program is no small task and is a continuing process. It should not suddenly appear as a fully formed, mature system but should be allowed to evolve incrementally, being adjusted and modified with each phase. The roadmap helps in achieving business goals on a schedule, and the payoff of a well-designed DG program can be enormous. 

A DG program should be flexible and ready for continuous improvements as economics and technology change. Keeping a record of the Data Governance roadmap allows it to be used as a foundation for future changes and additions.

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