7 Data Governance Guiding Principles

Data governance is about people, processes, and technology. Is 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.

Successful data governance has always been a challenge for any organization, but it has never been so challenging as it is now, with increasing data needs and more complex business environments.

Starting and running a data governance program is an ambitious goal and often, the results are far from the expected in multiple levels.

When taken in a holistic perspective, these are expensive initiatives, they’re time and resource consuming and span through long time frames, they can be deeply intrusive and disruptive, creating the natural resistance to change within the organization, creating a very challenging ecosystem to work on, besides being the kind of initiative that might take years to break even and deliver ROI, making it hard, even with a strong sponsorship, to keep the necessary traction to complete all the necessary changes.

The alternative to this approach is a more focused, iterative, and business-centric approach where, it is possible to deliver value and targeted return within short time frames.

A sequence of these targeted initiatives will also leverage the awareness of the importance and impact of data governance across the organization, increasing the overall internal engagement, increasing the trust levels on data and, eventually, the turning critics into evangelists and paving the way to a more structured and strategic approach enterprise wide.

There are a few principles I believe to be fundamental for the success of a data governance framework implementation.

Guiding Principles

1. Data strategy is business strategy

It’s frequently said that data is a business or corporate asset, although rarely treated one. Data seems to be a world apart.

The fact is that, as any other asset in the organization data’s purpose is to create value, data exists to support business, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives.

It’s not possible to talk about data-driven businesses without a full commitment to business-driven data.

2. Business Cases

Business cases, not use cases. Clearly identified business problems, where it is possible to identify how data may be used to deliver those priorities and objectives.

A strong data strategy is grounded in these business cases, all with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.

3. Start small, think big

Starting with small, targeted initiatives, where the impact and value of data is identified and with business stakeholders that can effectively articulate the impacts of data in their business processes and are aware of the value being generated.

4. Measure and communicate

The biggest challenges in these processes are usually associated with lack of leadership buy-in and commitment from the top management or poor cross organization involvement.

Setting up a set of metrics that can be linked to data governance and communicating them across the organization, creating success stories, 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.

5. Business on the driver seat

Another frequent mistake is to approach data governance from a strictly technological perspective, overlooking that the ultimate purpose is to leverage data to generate business value.

If there’s a perspective, it’s the business perspective.

All the program and initiatives must be driven and oriented by the business units.

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.

6. Agile mindset

Deliver value and deliver it fast. 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.

7. Integrate

There are always multiple data initiatives running in any organization.

Either at a more foundational level, data management initiatives as Master Data Management (MDM), data quality, data catalog, business glossary or metadata management or in the other end analytical, risk or compliance initiatives.

Integrating data governance in each of these initiatives not only works as a guaranty of better results but also consistently grows the organization’s governance framework.

Take out

The purpose of data is to create business value, the data strategy, must be oriented towards the organization’s strategic priorities and key business objectives.

It must be the business prerogative to determine what are the priorities and objectives, all these initiatives should be driven and oriented by the business units and grounded on clear business use cases — aligned with strategical business objectives.

Start with small, targeted initiatives, where the business impact and value can be clearly identified — Success, is dependent on business success — create success stories and communicate them across the organization, they will act as a motor to leverage the replication of that story in other business units.

Assess the current situation, with special focus on these three vectors: people, process, and technology to clearly identify and timely address the existing gap in all the requirements for a successful initiative and iterate.



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