No single Path (Part 1)

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

Having a data governance framework in place, assures that timely, consistent, and trusted data is provided business to support critical decisions, improving trust, transparency, and reliability when meeting customer and stakeholder expectations.

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.

What are the causes of failures in data governance?

• Lack of leadership buy-in and commitment - Data governance is a process that needs buy-in from every level of an organization, and it starts with strong executive sponsorship but also from every other stakeholder in the organization, which need to be aligned and committed to the program. Otherwise, the data governance program will constantly stumble into resistance pouches within the organization.

• Lack alignment with business goals and benefits - In any kind of data management initiative there is a principle that needs to be respected and seen as a goal: “Data exists to serve the business”. This means that any data governance process has to be supported on a strong business case, their objectives need to be anchored on business objectives, otherwise it will be viewed as another siloed IT project with no perceived value from the business side.

• Lack of empowerment for the data governance team - Data governance will affect every area of the organization, often it will affect the balance of power within those areas, added to the introduction of a new element, the data governance team. The lack of executive support and weak change management practices will result in the data governance team that is unable to perform any of its work.

• Lack of focus on strategic data - At an operational level, most of the organizations rely on dozens of different systems, which handle massive volumes of data of every kind of typology daily. Approaching data governance in a global perspective will inevitably lead to a lack of focus, resulting on a misalignment with the business objectives and incapability to deliver value. Again, being supported on a strong business case that identifies and prioritizes business critical and strategical data is paramount for success.

• Lack of cross organization involvement - As mentioned above, data governance is a process that needs buy-in from every level and area of an organization, and failing to clearly transmit the objectives and benefits of data governance, while inevitably lead to a lack of commitment and involvement.

• Focus on a technological approach - Technology itself won’t govern data, technology is but one of the components that supports a data governance program, and often proves useless if seen as an end in itself or not properly leveraged on the remaining components of this transformation process.

• Time to deliver results - When we look at the characteristics of a data governance program implementation strategy in an organization some characteristics are easily identified, these are expensive, time and resource consuming and span through long time frames, take a sometime to deliver ROI, making it hard, even with a strong sponsorship, to keep the necessary traction to complete all the necessary changes.

What can be some of the negative impacts of failures in data governance?

• Manage and use the available data – Organizations have been accumulating larger and larger volumes of data, from numerous sources and formats. Most of the data being produced and gathered results from ad-hoc initiatives, without any alignment with business goals and strategy.

• Data availability – Making the right data available to the right person at the right time is more than ever a competitive advantage, impossible without the alignment of the needs of all data stakeholders.

• Poor quality data – The existence of multiple systems, each with different rules and policies, with different quality assurance mechanisms, data that is used in multiple analytical systems, distributing data of poor or unknown quality across the organization, impacting both operation and the decision processes.

• Bad decisions - An organization’s decision process depend on the quality and reliability of the data. With it will impact the business negatively, with financial repercussions.

• Reduced productivity – Data is useless if it can't be found in a timely and efficient manner, and this impacts directly the productivity of workers that need to access multiple systems and spend huge part of their time consolidating information, when they find it.

• Data breaches – Being unable to control what data exists within the organization, where is located, increases the security risks.

• Compliance costs - In highly regulated industries, most of the regulations are inherently data centric and even looking beyond data and into the governance processes (ex: BCBS 239)

Avoiding the most travelled path

On the second part of this article, I’ll highlight why each organization needs to find its own path in this process. Staying faithful to its uniqueness.

Avoiding the implementation of one size fits all solutions, that will most likely fall short on their outcomes.