Towards organic data governance

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.

It’s irrelevant to discuss the need or importance of having a solid data governance framework in place within any organization.

Data governance rapidly evolved from a need to a requirement.

What started as a tool to manage the ever-growing data assets within the organization, become a priority, but also a critical challenge, as failure to deliver can develop into a disaster for decision making and implementing business strategies, but also to regulatory fines and even reputational costs.

Regulatory constraint or opportunity?

In highly regulated industries, such as financial services, we can see that most of the regulations are inherently data centric.

BCBS 239 is perhaps the best example for this, with strict focus on risk data aggregation, reporting, data quality, lineage, aggregation, and infrastructure, where organizations are not only required to report on their risk data but also on their data governance processes.

But we can find all sort of examples, from CCAR, MiFID II, Basel III or IFRS, to anti-money laundering (AML) and Know your customer (KYC) regulations, all related with data quality and overall data management.

Even on less regulated industries, there is still data privacy, with the EU Data Protection Regulation (GDPR) in the European Union, and similar versions in other countries.

All these regulatory frameworks present a challenge, but also an opportunity to grow the scope and benefits of data governance controls and processes, and for allocating funding to develop data governance processes.

Beyond regulation

Data is a critical and strategic asset for any organization, making essential that the right information is available at the right time to the right people to enable the organization to gain the insights for better business decisions, increase efficiency and productivity and promote a consistent and effective use of data across the organization.

All these necessary to create the necessary competitive edge when facing incredibly competitive markets.

Having a data governance framework in place will not have an immediate impact on the business, but will improve trust, transparency, and reliability when meeting customer and stakeholder expectations.

Unfortunately, although the awareness of the strategic importance of data exists, most organizations are slow adopters of data governance frameworks, risking poor strategic decision making and misallocation of critical resources.

The reasons behind this slow adoption are easy to understand as the implementation of a data governance framework in an organization, can sometimes be an overwhelming challenge, highly disruptive and prone to failure.

Most of times the reasons for failure can be associated with lack of leadership buy-in and commitment from the top management and poor cross organization involvement, lack of alignment with business goals and benefits or lack of focus on strategic data, but also for frequently being approached from a technological perspective.

These are all structural problems faced by almost every data governance program during their development stages, some are unavoidable, but these risks and their effects can be somehow minimized.

Organic Data Governance

Data governance is about people, processes, and technology. Is about combining these factors to create business value from data.

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 has to be able to overcome these and the challenges mentioned above.

Data strategy is business strategy. As any other asset in the organization data’s purpose is to create value, so any data strategy must be oriented towards the organization's strategic priorities and key business objectives.

Use Cases. From here it is possible to identify how data may be used to deliver those priorities and objectives. These will be the use cases for the data strategy. In an early stage, for effectiveness purposes, there should not be more than five use cases, all with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.

Start small, think big. Always aligned with the data strategy start with a small, targeted initiative, where the impact and value of data can be clearly identified and working with a business stakeholder that can passionately and effectively articulate the impacts of data in their business processes and that will be eager to defend the project.

Measure and communicate. 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.

Business on the driver seat. 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.

Agile mindset. 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.

Integrate. 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 and metadata management.

Growing from within

And effective data governance program must start bottom-up, its success depends on finding the efficient combination of people, process, and technology.

This approach 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.