It is crucial to pay a particular attention to the use and adoption of Data Governance regarding any new initiative, whether it is an innovation or the deployment of a new product line. Data governance represents a major change among the companies, and we all know that "the more Data Governance is adopted, the more likely it will be used by a large number of people".
For any initiative, first key element of good adoption is to pass the right message to the right people in the organization, for that the best option to build a hybrid data team which will include a mix of experienced employees and freshly hired data experts. Doing this you will take full benefits from the network and the understanding of the company challenges; and on the other the data experts will translate your business pain points into tangible data topics. The below diagram depicts the keys steps to build a successful data culture.
All good adoption of data culture starts with a study phase with maturity level assessment and the building of an elevator pitch. The maturity level assessment on the key data pillars (data governance, data quality, data management, data privacy, data integration, data preparation, analytics), based on your current operational challenges and business pain points, will enable you to identify the priority data topics to be put on your roadmap.
During the elaboration of the roadmap, it is very important to define tangible objectives, deliverables and solutions that will speak to the business and will ring the bell in people’s mind with concrete operational benefits. From these priority topics, and from the overall corporate strategy, we will elaborate an elevator pitch that will be used for explaining the objectives and the high-level roadmap to the C-level first, then to heads of functions, and finally to operational teams. No sponsorship, no credibility!
There is a mandatory step of evangelization through the top-down levels of the organization pyramid to explain:
This step is very important to get the concerns and the feedback from the various levels and stakeholders. Along the evangelization journey, the pitch and the presentation can be adjusted in regards with the remarks of the business stakeholders.
Select a data governance framework based on the best fit for the main objectives, business priorities, organization structure, and overall culture and maturity level. Define mission and vision of the data governance program. Establish and define goals, governance metrics and success measures, and funding strategies. It is important to understand that these planning activities may be performed on a recurring basis.
This phase prioritizes on ensuring sustainability of data service implementation and operation and identifying transformational opportunities with efficient & cost-effective data services and optimized data quality management.
Some of the challenges of this phase might include the potential loss of agility and flexibility for uniformed enterprise-scope governance method. It is extremely important to balance the centralized control vs. distributed management to account for unique local and departmental requirements.
Most data projects are done following an agile or scale methodology. The data owners and operational teams must be fully involved in the project execution through regular reviews, the prioritization of the product backlog and through regular intermediate solution development demos.
This is also a very important step for supporting the adoption before the business go-live. Before conducting training and testing, I always suggest doing a final round with the business sponsors and the head of functions in order to answer their last questions and to get their "blessing" before the deployment.
During this phase, you will present them the final solution and the organizations or processes changes that will be deployed. From there, you can organize the User Acceptance Testing (UAT) and the training phase ensuring that everyone feels comfortable with the future solution.
Data governance is one of the most important components of Enterprise Information Management. It is interrelated with all other disciplines of data management functions. The level of dependency might vary from function to function. Master Data Management, as an example, would not succeed without effective data governance. Styles and different focuses of data governance require unique data governance activities to meet these specific requirements. However, as the term “Governance” indicates, the foundation of any governance involves certain levels of control. Practicing data governance is about finding the right amount and right level of control. Taking an iterative approach will mitigate implementation risks and help an organization focus on the right level of control to be effective and successful in managing their most important asset – data.