Sweet Spot

The secret for the success of a data governance program is in finding the sweet spot that successfully brings together people, process, and technology.

What’s waiting when you reach that place? Business value.

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

Data governance may not seem to be the highest priority but looking at the kind of issues that can be addressed, problems that are not exclusive to any industry, or market, that touch organizations of every type and size.

  • The gap between business and IT, leading to often contradictory strategies.
  • Difficulty in managing a siloed data ecosystem.
  • Difficulty to identify and define data across sources.
  • Lack of standard business and data management rules and data protection policies.
  • Rising data-security concerns around providing employees with remote access to data.
  • Difficulty to identify, cleanse, standardize, and curate data for sharing.
  • Existence of duplicate, erroneous, inaccurate, and incomplete data.

It’s almost impossible to find any organization that doesn’t struggle with most of these issues, or anyone working with data that doesn’t relate with most of the issues listed above.

Now more than ever organizations are investing heavily in leveraging new technologies, artificial intelligence, machine learning the internet of things, augmented and predictive analytics, and data is at the core of each of these initiatives.

They are fully dependent on data; all are aimed at providing quality data that is essential to improving insights and driving substantiated business decisions.

The solution is found in the implementation of data governance frameworks, with the purpose of assuring that timely, consistent, and trusted data is provided business to support critical decisions.

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.

  • Lack of leadership buy-in and commitment
  • Lack alignment with business goals and benefits
  • Lack of empowerment for the data governance team
  • Lack of focus on strategic data
  • Lack of cross organization involvement
  • Focus on a technological approach
  • Time to deliver results

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.

As any process that is introduced into an organization it will create disruption of the status quo, it will generate resistance to any change, a success approach to data must overcome this as all the challenges mentioned above.

Forget data strategy — develop a business strategy for data — 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.

Forget use cases — define business cases supported by data — identify how data may be used to deliver business priorities and objectives. Business cases, with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.

Forget data — focus on business — business is the driver, data and what it produces is the enabler, the success of any data related initiative is measured on how it impacts business performance.

Deliver business value — start with an end in mind and a clear business objective, with small but business relevant initiatives, gathering business metrics to that can be linked to the data governance initiative and replicate these success stories across the organization.

The transformation process that leads to a data driven organization must be wholeheartedly supported on the business strategy and objectives. 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.



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