Companies Struggle Balancing Between Autonomy and Control of Data.
The Data Governance Puzzle: Even Good Plans Are Hard to Realize.
As the person responsible for the data quality and governance in your organization, have you been in a situation where you have decided to develop your organization’s data management?
You have identified strategic projects, identified your most important data assets, established a data governance organization and decision-making model, but… it doesn’t fly? Everything is done according to the textbook, but people have lost interest after the initial enthusiasm and various tool experiments.
In my consulting career, I have seen several such organizations, both large listed companies and smaller manufacturing companies as well as public sector organizations. The so-called “real work” has taken the interest away from the work of data management. By real work I mean the normal everyday work that people used to spend their working time, before somebody started talking about data assets and data products.
The common denominator for these different organizations has been a low maturity in data culture and a weak concretization of the data strategy related work as part of people’s current work. Change starts with people, as they say, and if people don’t have a clear idea why e.g. conceptual data modeling is needed, they won’t do it.
Make the Objectives Relevant for Data Owners
From the point of view of data governance, data owners play a key role. The data owner has overall responsibility for a certain data entity and that the data governance principles are implemented in practice for the data entity.
The list of responsibilities can be long, and the implementation of responsibilities requires a lot of work with data stewards, being in various work groups, and participating in data council meetings. And this in addition to all that other “real work”! It won’t happen, at least not for long.
Based on my experience, you have to serve data governance in small portions. Nominate people to the roles, train them to understand their roles, but don’t tell them in data governance jargon what is expected of them. Define the responsibilities with them, from a business perspective. Make sure the data management tasks are integrated into their current job description.
Don’t try to give them something that they don’t understand and that you can’t train them for in a reasonable amount of time.
Serve data governance in small portions to the organization.
Be sneaky and wrap the data governance agenda into the data use cases.
Be smart and connect the data governance KPI’s with business and personal objectives.
Tailoring Your Approach: Use Case Oriented Approach
People are not productive if they do not see the meaning of their work. In Data Design, our data strategy and governance services are designed around data use cases.
All data management development must support the realization of our business strategy. Otherwise, it doesn’t matter, or it can be done later when it matters. Data owners and other roles in a data governance organization are better motivated when data management is developed based on goals of the organization and the personal goals of the people.
Data Management as Part of Every Day Work
Data management must be embedded into everyday work. It should not be faceless emails and reports from the Data Management Office, but it’s part of the meetings held by the business anyway. The business is interested in when they receive their customer-specific demand or production forecasts, not the data quality report’s index of the correctness of customer data.
Monitoring and measuring is part of the implementation. We help our customers to inform about the progress of development of use cases and related data management development needs in a business-oriented manner. This also supports management of expectations, when there is a a clear connection between data management activities and business needs.
Implement Data Governance Only as Much as Necessary
In summary, it can be stated that data governance should be implemented only as much as is necessary and it must primarily serve the business needs. The organization should focus on implementing the data use cases, data management activities come along with the work. The head of data governance needs to orchestrate all of this, so it requires a good set of business and IT skills to perform well in the role.
At Data Design, we have packaged our experts experience into ready-made data strategy and data governance accelerators, which are easy to implement for different types of organizations. We have seen both successful and not so successful data programs run by our clients. That’s why we know that success is based on use case oriented data governance implementation.
