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7 Data Governance Guiding Principles

7 Data Governance Guiding Principles

Every successful data governance program follows these seven essential guiding principles

The secret for the success of a data governance program is in finding the sweet spot that successfully brings together people, process, and technology, backed by a strong executive sponsorship and a close relation with business strategy and objectives (supported by relevant business cases) and operationalized within an agile framework, starting with small initiatives, more focused and efficient, delivering faster returns and creating awareness across the organization, acting as the motor from within the organization, allowing data governance to gain traction and leverage long-term benefits.

Data governance is about people, processes, and technology. It 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, generating resistance to any change.

Successful data governance has always been a challenge for any organization and with increasing data needs and more complex business environments this challenge will grow.

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

When taken in a holistic perspective, these are expensive initiatives, they’re time and resource consuming over long periods. Therefore, they can be deeply intrusive and disruptive, creating the natural resistance to change within the organization, creating a very challenging ecosystem to work on.  Additionally, a data governance initiative 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.

Focused Data Governance Program Traits

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.

The application of these principles will allow to leverage more ambitious programs, starting with projects that have in common the following attributes:

  • Reasonable funding model.
  • Targeted program goals and milestones.
  • Focused effort.
  • Short timeframes.
  • Increased internal engagement.
  • Delivering targeted return on a short timeframe.

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.

Guiding Principles

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 from “real assets”.

The fact is that, as with 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.

Business Cases

Business cases are not use cases. A business case states clearly identified business problems, where it is possible to identify how data and data management may be used to deliver those priorities and meet business 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.

Start small, think big

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

Measure and communicate

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

Establish a set of metrics that can be linked to data governance and communicate them across the organization, creating success stories, that will raise awareness and act as a motor to leverage the replication of that story in other business units.  Even at a small scale, success stories must be part of communication.

Business in 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 truly 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.

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.


There are always multiple data initiatives running in any organization. Foundational data governance and related initiatives should be integrated with other data management initiatives, such as Master Data Management (MDM), data quality, data catalog, business glossary or metadata management or 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.  Data governance program training is essential for effective integration.


The purpose of data is to create business value, so 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.

The critical point is to understand that it’s not about data, it’s about business. Business is the driver, data and what it produces is the enabler.

The success of data governance, as of any data related initiative, is measured on how it impacts business performance. How it impacts customer and experience loyalty, offer and innovation, or operation efficiency, minimizing inefficiencies and reducing costs. The true measure of success is the quality of the organization’s decision processes; the organizations best able to make the best insight-driven decisions faster will gain the competitive edge.


Jose Almeida

Jose Almeida is a principal-level international consultant in the areas of Data Governance, Data Quality and Master Data Management, ETL, Data Migration and Data Integration, with consolidated experience in projects across Europe, Middle East, and Africa. Currently, Jose works with local partners across Africa and the Middle East enabling organizations to proactively manage their data assets and to address their data-related challenges, thereby realizing more value from their data.

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