No single Path (Part 2)

In the first part of this article (https://www.josealmeidadc.com/articles/no-single-path-part-1) I highlighted the most common reasons for data governance initiatives to fail and some of the negative impacts that result from the absence of effective and efficient data governance within most organizations.

So, how can these challenges be overcome and how can the benefits of properly governed data can be turned into actionable insights, that will feed the corporate decision processes and turn into better decisions.

Data governance is about people, processes, and technology. It’s about combining these factors to create business value from data, and as any process that is introduced into an organization it will create some disruption of the status quo, it will generate resistance to any change, a success approach to data must be able to overcome these and the challenges mentioned before.

It’s easy to find in the market good data governance frameworks, that have been tested and refined overtime, unfortunately the value of these frameworks doesn’t make them easier to implement.

I believe this is the first problem “implementing” the framework. There are no two organizations alike. So instead of implementing a data governance framework, the drive should be on adopting and adapting a data governance framework. As my friend Sanjeev wisely says, “Set the framework in a frame that works”.

It is the uniqueness of each organization that challenges the framework implementation, that seen as a one size fits all solutions, will most likely fall short on its outcomes.

That is why it is so important for each organization to define its own path.

A data governance framework is not a blueprint, it must work as a roadmap that is constantly being reviewed and adapted.

Start by looking at data from a business, not a technical, perspective. Data’s purpose is to create business value, so the strategy towards effective data governance must be oriented towards the organization's strategic priorities and key business objectives.

These are the priorities and from here it is possible to identify how data may be used to deliver those priorities and objectives. These will be the business cases, not use cases, that will define a clear roadmap for data governance.

In an early stage, for effectiveness purposes, there should not be more than five cases, all with clear, achievable business objectives and close to stakeholders that are aware of the importance and impact of data.

Start with small, targeted initiatives, where the impact and value of data can be clearly identified and working with a business stakeholder that can passionately and effectively articulate the of data governance in their business processes and that will be eager to defend it. Apply an agile development mindset to all this process, start with a minimum viable solution and iterate, allow that visible results are presented in short time lapses.

As in any process that involves change within any organization, communication is paramount. Setting up a set of metrics that can be linked to data governance and communicating them across the organization, a success story, that even at a small scale will create the awareness and act as a motor to leverage the replication of that story in other business units.

The ultimate purpose of these processes is to leverage data and enable it to generate business value, so it should be the business driving and orienting this process. Data governance is not an IT function, it is a business function, it is the business who better knows what their problems and objectives are. The role of IT in this process is to find the right technology and support the business units in this journey.

Finally the technology, as data governance is only part of the process of managing the organization’s data assets, it must be integrated with other initiatives, as Master Data Management (MDM) , data quality, data stewardship workflows, data catalog, business glossary or metadata management.

Approaching data governance in an adaptative, incremental and compounding way will produce long-term benefits, creating traction and increasing the awareness across the organization and will end-up acting as the motor from within the organization for a Data Governance structure that will grow organically out of the initial iteration.

There are no predefined paths, and most likely the most travelled path is not the one that produces those best results in every context.