How To Ensure Data Governance Adoption
Today, it is widely acknowledged that in order to successfully operate a data-driven organization, it is necessary to:
Establish procedures, policies, and standards for the usage, development, and management of data;
Design an appropriate organizational structure; and
Implement the technology infrastructure to support data governance.
However, the successful deployment of data governance remains a challenge for the majority of organizations. Many have undertaken a pilot or successfully implemented the vast array of data governance platform features. Pilots generate modest financial rewards for the organization, and as features are materialized, excitement typically increases. However, months or years pass without delivering the predicted major victories. Why is that?
The appropriate technology can ease the way, but tools are only as useful as the information they contain and, more importantly, the degree to which it is utilized throughout the organization. People must actually enter, update, and utilize the data definitions, business rules, and KPIs in order for data governance expenditures to deliver business value. For example, in many Collibra Data Governance projects or Informatica Data Governance implementations (or any Data Governance program for that matter), little effort is set aside for adoption and change management.
The governance process must be a closed feedback loop in which data are defined, monitored, acted upon, and modified as necessary.
This blog post examines suggestions for maximizing the adoption of a data governance strategy. These concepts were inspired by collaboration with clients.
Planning for Data Governance Adoption
By making sure the scope incorporates adoption as part of the objectives, you can set the right conditions for success.
First, define and prioritize a list of important use cases and the advantages or value proposition they will offer to your organization. As mentioned earlier, the focus should be on demonstrating value. Begin by working on the use cases with the highest possible level of feasibility and business impact. From there, you can expand the scope of data governance and its impact across your organization.
Make sure to define success metrics for your data governance program. These measures can be used to build a baseline for evaluating what false data signifies and, perhaps, what accurate data signifies. As a result, they provide a basic framework for getting the entire company to recognize the need for improved data.
Furthermore, the relevant metrics can help in your organization's alignment on a set of common aims, which is vital to success. Stay updated on adoption rates, which reflect whether your data governance program is usable by people and whether they understand and care about it enough to adopt it. That's where Key Behavioral Indicators (KBIs), a set of specific and measurable behaviors, promote explicit ownership, eliminate friction between stakeholders, boost awareness of individual behavior, and enhance the value supplied by each stakeholder group. And this brings us to another aspect of planning wisely: make sure roles and responsibilities are crystal clear, but also that people get the support they need.
You don't just want to track development and adoption; you also want to make sure you're accounting for the value you bring to the company. Make sure you have well-defined impact metrics in addition to progress indicators (e.g. improvement in report quality).
A final point; when organizations expand their investment budgets for data governance, license, implementation, and maintenance costs are typically considered. But the cost of improperly fostering and implementing data governance is enormous. Unfortunately, when budgets and priorities are considered, data culture and adoption are frequently relegated to the bottom of the list. Why?
We have seen evidence that companies that have successfully scaled data governance allocated more than fifty percent of their analytics budgets to activities that promote data governance adoption. Our advice: budget the same amount for culture and data governance adoption as you do for technology.
Finally, before you even get started, make sure you have the sponsorship from the highest levels that you can. What is not important to the boss will not be important to her people. Data Governance is no longer a question of whether it is necessary or not, it is a question of how it will be done in a way that works for the organization.
Designing Data Governance Adoption
What about the deliverables themselves? During the architecture phase where decisions about the overall solution are made, there is a very real opportunity to prioritize adoption and architect it into the solution itself.
Define a flexible Operating Model. There is no one-size-fits-all approach to data governance. As a result, every data governance program begins with defining and developing the operating model that will influence the program's execution. The operating model might vary depending on the priorities, culture, organizational structure, and even internal dynamics of your organization. Part of the initial stage in setting up and establishing a successful data governance program is deciding on the operational model your organization will use.
One of the obstacles is that the value creation potential of data governance is often indirect and takes time to manifest, while individuals are more likely to be impatient to expect a significant return on that investment.
As a result, instead of striving to develop everything at once, emphasis should be on efficiently demonstrating value and delivering more gradually. The importance of data governance can be illustrated through a few carefully chosen use cases that are associated with a critical business situation.
Because of the historically high rates of failures, data governance is still a novel concept, and whether it's fear of change or the prospect of additional obligations, you have to meet business users and stakeholders where they are. Instead of asking people to come to you, go to them and offer ideas that are relevant to their work environment. Consider using an on-the-go application, which allows you to access data from anywhere at any time. We know from science that exerting a lot of effort diminishes the likelihood of new behaviors developing and enduring. As a result, consider designing a simple, engaging, and user-centric approach.
Data has an impact on every aspect of an organization, from IT to marketing, finance to supply chain, analytics to legal. You must have the right individuals in the room to help you encourage change and offer them incentives to do so in order to establish a successful adoption strategy. Data governance must be associated with performance management to expand adoption and build new practices (incentivize change). People tend to anticipate all potential exceptions in processes and attempt to develop a governance solution that can manage every unique element of their business. It frequently results in a highly complex system that is either ineffective or incomprehensible. We suggest keeping the approach as simple and clear as practically possible (over-engineering is not advisable). Exceptions can be handled on a case-by-case basis eventually.
People frequently forget to act on their objectives or intentions during their everyday routine. Furthermore, people may be unable to recognize or take advantage of the appropriate moment or opportunity to act in compliance with data governance requirements. When people are faced with tight deadlines or high-pressure demands, these issues become much more pronounced. People are more likely to revert to previous habits and defaults in these circumstances.
The formulation of implementation intentions is one self-regulatory method that has been advocated to assist both individuals and communities in closing the intention–behavior gap. These simple plans use an if-then framework to outline when, where, and how to execute on a specific goal. For example, day-in-the-life guides, training, and coaching are examples of ways to help people get value from data governance in their daily work processes.
Conclusion
The benefits of data governance are well-acknowledged in today's organizations. On the other hand, many of them cannot attain the intended outcomes outlined in the initial program charter. Focusing your attention on generating adoption for your data governance program is a critical step in overcoming the challenges that these programs encounter. Apply the design and planning principles outlined in this blog post at any point of your data governance journey, and you'll rapidly see the benefits and changes that occur with being able to find, understand, and trust your data.
