How do you educate data users on data governance?

Like a diamond that is measured with 4Cs (cut, clarity, color and carot), Data users are also identified with four Cs.. who Creates data who Curates the data (maintain sanity) who Consumes data who is Custodian of data (that may make the whole organization as the data users). Each of them has different needs and the learning should be from their perspective.

In my experience data users vary greatly, some don't identify as data stewards when they are. Creating personas to describe the different types of data users in the organisation and conducting a maturity assessment to determine where they are on the development path for the persona they most align to is the method I think has the most promise.

That is actually a crucial point, and it is often neglected because it seems too obvious. Usually, the users believe they should be able to access all the available data. However, there is much more than just another database. Identifying the data users can help to speed up the process of working with data in general and mitigate the time spent requesting access.

"Identifying who the users are" is so important - and knowing the various roles the users play, how they interact (or do not), and what is expected of them for data usage (and what they expect from the data) sets the foundation for all the following activities. Education is impossible without knowing whom to educate, and in what areas they need the education.

In an effort to educate on data governance, the users of data need to be identified, especially when data governance program is rolled out. Data users may already exist with different titles. We should be able to find out data enthusiasts, in each business line, who love and understand data. They are the easy targets, who understands data language from us, to get support from.

Identifying the learning objectives follows from the results of identifying the cast of characters (users and roles). Also, having a solid foundation in best practices for data governance and how the organization plans to (or is) implementing those practices leads to knowing the focus areas for the education. Caveat: do not try to throw too much education - too many objectives - at the user communities at once. Biggest goal: instituting a "data-driven" approach across the organization. "Data-driven" means using the right data for the right purpose - and "right" includes expectation of high quality.

Again - the learning content/method of delivery should be based on what they need - not based on what you know. Every time, any person spends quality time in learning is only when they know what is in it for them, and what will they benefit for the time spent. And if the governance program is going to make it easy for them to find the data, understand the data, trust the data then they will be all ears in listening and learning.

Learning needs and objectives should come from the organisation needs, data team's objectives and also the user's own learning needs. We would have balance and add value to the different agendas around the table.

Traditionally learning objectives are set by the educator and based on the module eg "Here is a course I designed and these are the learning objectives for this module". I prefer a more tailored set of objectives based on the individual with a bunch of learning materials brought together to achieve the student's objectives. The materials can include articles/books to read, ted talks, youtube videos to look at interviews to conduct and real-world business activities to complete. The training materials would include but not be limited to online or classroom training. If you can imagine a set of tailored objectives set with the individual with access to a library of materials that can be brought together to achieve the next maturity step.

As part of the data governance roadmap, the expectations from the data users should be clearly defined. The expectations include responsibilities for data users. The data users can be taught about the program, that the program tries to achieve or value-add to the organization, their role and responsibilities, policies that are planned to be established, data quality objectives and framework, etc.

Education can be delivered in a wide variety of formats and using different methods. Choose the ones that will enable your user communities to learn and apply that learning consistently. Workshops and case studies are often effective ways to teach adults, and using senior stakeholders and champions to help deliver the data governance message should be part of the efforts.

When you learn something new the best way to retain and hone that skill is to teach it to someone else. Creating a movement by facilitating peer training can be a self-propelling method.

Workshops, Webinars, and in-class or on-site sessions are most common methods. The materials used during the learning sessions should be made available to attendees for reference. A sample demonstration on scenarios like data quality improvement and standardization of addresses or other elements would definitely make users engaged in learnings.

A roadmap for success and a starting point: 1) Define data principles that resonate and reflect values across the organization. 2) Get those principles endorsed and supported by senior business leaders across the organization. 3) Rollout the principles to the organization by delivering the training in various mediums to adapt to diverse learning styles, connecting to the business priorities that have direct implications to that specific user group. 4) Collect feedback and use this as a starting point for further conversations to further connect business priorities and governance needs. Most important is consistency of data related conversations - it is a journey!

Well-organised and planned learning is a must. Learning can and should be fun!! Think about what's in it for the student. The monitoring and evaluation should also be assessed against business outcomes.

Lesson planning and execution are crucial to effective education - in the workplace as well as in the classroom. Communication of the plans and how the lessons will be delivered is important, so the learners know what to expect and when to expect it.

Learning is most valuable when actually applied into your day to day, but because human beings are animals of habit it’s usually easier said than done. It’s hard enough to remember things you read somewhere, and even harder to remember them in the right moments of your daily life. Providing external impulses - such as creating learning groups, practice buddies, best practices sharing can encourage practicing. Acknowledging and recognizing impact through applying what’s learned can further more create a healthy motivation for everyone.

Once the learning sessions are completed, it needs to be ensured on the results of lessons. Typically it is done by quizzes on the learning subjects with a passing score. Sessions with question and answer could demonstrate the knowledge learned. Support and feedback would be motivating factors after the sessions.

Delivering stale or outdated learning is always counter-productive. The education / training development team should include revisions to each lesson to incorporate the lessons learned from delivering each session and each course - to each user group. Different lessons will elicit different levels of learning and retention for a group. Capture metrics for each learning experience and review them regularly.

Based on my experience running a governance organization, I recommend three additional considerations: 1. Identifying consumers of data is only half of the equation, as you also have to identify (and educate) the creators of data as they will also need to have some idea of the benefits of data governance. 2. Any end user training needs to be rooted around the benefits that those users or creators realize from data governance. all users or creators need to understand the 'what's in it for me?'. 3. Avoid using 'data literacy' to describe your training efforts. Data literacy is a judgemental term that incorrectly assumes a user skill gap is why organizations fail to get value from data, when the problem is often the data itself.

There’s a lot of hype around data literacy. There are several ways to raise data literacy and most are listed above. I am a strong believer that by enabling any form of data governance within an org you’re raising literacy. A catalog = access and transparency to your data assets. Policy = what you can and can’t do with your company data. Metadata = understanding of terms… and the list goes on.

A lot of this insinuates one curriculum for all data users. It's important to develop guides by domains of data to lower the effort of end data users of the given domain of data so that they only need to be aware of what is necessary for their given domain. Generic data governance curriculums, the majority of folks checkout, don't find useful and struggle to apply the policies and standards in their context of work. This is where adopting the principle of data as a product with appropriate readmes/guides/training allows for data users to effectively use the data and also ensure security and compliance by default in the intended use of the data.

Before launching into theory, start with value-proposition, some case studies demonstrating the huge value (i.e. success stories) good governance can bring and the huge risk (i.e. horror stories) bad governance carries. This helps your stakeholders to know why they should care and listen to you.

Yes, as Elijah said, you have to start with the value. The value of the data itself must be explained in many instances to management and coworkers. If they don't value of their interaction with data, they'll never value data governance.