The data governance framework (2019) was developed in response to common opportunities and challenges that sustainability standards seek to address in order to make the most effective use of the data they collect and manage.
Opportunities include providing greater value and impact from their certification activities in the form of data services and product offerings for stakeholders, from certified enterprises to company value chain actors. Challenges include inconsistent data quality and unclear permissions to share and use data in an agile and responsible way in light of emerging data privacy regulation.
How can my organization use the framework?
The framework below shows sequential steps to achieving good data management through data governance best practice.
Data governance is less about governing data and more about governing people’s behavior in how data is handled and used.
The concepts, processes, and tools used to apply the framework build deeper understanding about the nature of your data and how data stewards interact with it. Data processes can then be documented and improved iteratively. These activities are valuable because they allow organizations to augment data value by knowing their data, how it is handled, by whom, and where opportunities exist to create value with it according to objectives.
Ideally, an organization will begin the journey to good data management by first understanding their data and their data-related objectives. Step 1 is understanding how data currently adds value in daily operations and where there may be barriers or opportunities to value creation. Once data value chains are better understood, the organization can then use Step 2 to articulate its objectives in using data. This step is essential to hone in on the most valuable data processes to be understood and documented in subsequent steps of the framework.
Steps 3 to 7 are where deeper exploration and documentation of data processes take place, including identification of key roles and responsibilities to resource data management activities within the organization (e.g. data decision-making, cleaning, analysis, reporting).
Steps 8 and 9 help organizations harness tangible value from data governance activities. With clarity from previous steps on which data is most important for meeting objectives and how that data is managed and transformed to add value throughout data processes, organizations can license the data they wish to share and develop clear data policies to enable data to flow outside the organization.
Finally, a core element of the framework is the center circle which represents the soft skills, awareness, and data literacy skills that enable data stewards to contribute to the organization’s digital transformation. This dimension of data governance is the intangible value created from exploring each of the previous steps. It captures the collective effort and contribution that individual data stewards offer towards building a data culture within the organization. The data culture enabled by implementing good data governance is resilient and effective in responding to the evolving digital landscape that brings benefits and opportunities for augmenting sustainability impacts.
I developed this framework as part of my work with the ISEAL Alliance in support of voluntary sustainability standards.
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