When organizations have data quality or data flow challenges, the last thing they want to hear about is the plumbing or infrastructure gaps behind the challenges. However, if you explore the root causes of data problems, they are often multi-faceted in nature. They are only partly about infrastructure such as databases and technology solutions. Those are easy culprits, and are visible and actionable. In the absence of deeper understanding about the state of our data, many organizations rely on fixes using technology for their data problems.
This is where data governance comes in. Much like any governance mechanism, data governance can provide structure, order, and balance for your organization’s collaborators in their interactions with and use of data.
Data governance is all about greasing the wheels in your data management machine, however it may currently look. That system includes people, their data-related activities (and time allocations), data storage or technology tools, and policy or procedures.
Data governance helps you understand how data flows throughout your organization by helping you identify and document the resources that create and move high-quality data. Good data governance policy goes further and brings data governance tools and activities together into a cohesive program. This is because a good policy is not just a document. It is rather a set of active tools that connect data stewards and their activities. A data governance program will serve to empower your data stewards to effectively use and create value with data.
The elements of data governance such as strategy, data inventory, and data process mapping, and data licensing are gaining increasing value in the digital age. Although data management practice at the enterprise level has been implemented and rapidly evolving for decades, even large companies still struggle with achieving consistent and meaningful value from their data. Large companies, organizations, and governments are looking for ways to get the most from data in a way that is cost-effective and systematic. This is why data governance is emerging as a way to achieve integration of data-related resources and solutions.
Sustainability organizations at the forefront of work on pressing social and environmental problems are also faced with similar needs to reap the most value from data, in part because their stakeholders are asking for data to back up their sustainability impacts stories. Many also have an advantage in that they are relatively small in size and complexity of operations (compared to large enterprises) and can implement data governance practice more easily.
So, how can sustainability organizations get started on this journey to better value from data by looking underneath visible data challenges and understanding what makes good data work for them?
A data governance framework has been developed for sustainability standards organizations and can be applied generally to NGOs and social sector organizations looking for long-lasting solutions and systems to manage data effectively. This framework includes the following elements:
- Where are you going? Defining an objective for use of your data
- Roles and responsibilities: Who are the key supporters and budget holders within your organization responsible for resourcing and supporting data value creation?
- Data inventory: Taking stock of your most valuable data assets
- What does your data trail look like?: How does data flow within and outside of your organization? Who handles the data and influences its quality?
- Map your data trail: Use tools and templates to document how data flows and to communicate effectively with your data stewards
- Share data responsibly: License your data according to a data sharing policy that enables your stakeholders to use data effectively while respecting data privacy regulation.
These elements of data governance can be applied incrementally. They do not need to be exhaustive and include all of the data your organization manages.
The most important step to take in implementing data governance practice is bringing awareness within your organization about the interconnected nature of people, processes, and activities in the use of data.
Contact me to discuss how this framework can support your organization’s data-related challenges or opportunities.