Your data governance program (or lack thereof) could be the cause of the problem.

A data governance framework is essential to ensure that an organization’s data assets are accessible when needed, up-to-date, and secured in an environment in accordance with an organization’s internal data standards and regulations. However, the reality of everyday business activities and complexities mutes this ideal.

No single organization interfaces and applies data in exactly the same way. In the Asia Pacific region (APAC), some businesses find the lack of alignment between IT and business outcomes to be one of the biggest challenges in executing software strategy. 

Moreover, organizations today pool more volumes of data/data assets, and confront more complexity in data security and compliance. The roadblock is clear: in today’s data-driven business environment, there is no one-size-fits-all data governance program, and data quality and usability remain a challenge due to inconsistent data taxonomies.

David Chan, Managing Director, Singapore, AdNovum

So, what are the three time-tested data governance principles can leadership teams apply to build data governance programs?

    1. Ask these critical questions to set a clear direction
      What are the goals of the business unit or team? What direction is the organization heading towards in terms of the general business? These big-picture organization questions play a fundamental role in determining the context to apply data-driven work in everyday activities. Data governance challenges are dependent upon the context it is operating within.

      To determine which sources of data to incorporate into work, it is helpful to have a clear definition of what is the desired business outcome. Is this data supposed to impact top-line revenue, bottom-line profitability, or possibly both? The desired business outcome will drive the fundamental decisions being made on which data to collect, and why this data is being collected.

      Answering these questions will bring clarity by materializing data challenges into the realms of everyday concerns.

    2. Establish your data governance framework
      Today, the momentum behind data-driven operations has led to numerous side effects. These include data sprawl, inadequate data quality during analysis transformation, and cases of circumventing compliance regulations locally and internationally. The lack of a standardized approach to data governance can impede the transforming of data use cases into clear business value. Generating and collecting more data alone does not create the forward momentum for insights and business value to be unlocked.

      Each organization identifies and applies data in its own unique way to derive the best business value it deems profitable. Therefore, when defining and designing a data governance program, ensure transparency and accountability. The framework must address fundamental questions: Who owns each data asset? What roles and responsibilities must the data asset owner fulfil? How do we ensure data is collected, curated, and qualified for use across the business? While these questions may seem elementary, the ramifications of not having good answers can add up at an accelerated pace in today’s accelerated digital climate.

      When data is correctly collected, curated, and formatted, it can be used for analytics and as training datasets for machine-learning algorithms in predictive analytics. The insights generated from quality data can play crucial roles in decision-making and action plans.

    3. Follow the data trail
      A comprehensive data governance program also ensures data integrity across touchpoints and parties interacting with the data.

      Establishing common data quality metrics solidifies data integrity, a key component of your organization’s data governance program. Are there mechanisms in place to intervene when error rates in datasets exceed acceptable levels? What are the quality levels in data definition, metadata and data catalogs? What are the possible quantitative measures to define data completeness and data consistency internally as an organization?

      Likewise, an effective data governance program adapts and complies with local and international rules and regulations. Safeguarding data and guaranteeing that it is used in line with all applicable external standards cannot be overstated.

      Also note that, as more of everyday business activities and professional work involve integrating tools, external data assets, and connecting multiple stakeholders and users, data governance may be placed at a lower priority. However, data breaches and inappropriate business usage of data assets can result in more lasting consequences than poor decisions and wasted resources.

A quality data governance framework is built upon an organization’s specific context in relation to how it serves other businesses and the broader industry to create value. Without frameworks, there is a tendency to be overly protective of data, or to isolate data within the organization. This could result in a reduction of possible use cases to explore across the value chain — out of fear rather than trust.

Recognize that the true arbitraging power of data comes from implementing use cases that cut across different business units or organizations that impact entire value chains.