We all know about disaster projects like the Panama Canal and the Big Dig. It’s not that they didn’t get built, but they probably could have been built more efficiently and for way less money. Both of these projects had managers, plans, and goals, but they seemed to lack any governance plan. In her 2010 article for NASA, Virginia Greiman said, “If there is a single cause for the massive cost escalation on the Big Dig, it probably involves the management of the project’s complex integration.”
There was no single point of failure in either project; instead, lots of smaller mistakes — cost overruns, not having the right people in the right positions, etc. — led to significant financial excesses and their place in history as examples of how not to run a project.
It wasn’t just project planning issues that led to failures.The data that were used to make decisions about how to run the project and the lack of management of relationships between stakeholders played significant roles
Data governance is not data management.
Even if you have the best, most complete data, and all the right elements of a project in place, data management isn’t going to get you to the end goal by itself. Data governance, the framework that puts all of the policies and procedures in place to support data management, is critical to ensure consistent and proper handling of data and understanding of information.
Tableau, the business intelligence giant, uses a great analogy to explain how these two concepts work together:
“…data governance designs and creates the blueprint for new construction on a building, and data management is the act of constructing the building… while you can construct a building without a blueprint… it will be less efficient and less effective, with a greater likelihood of a failure [in your data structure] down the line.”
You could replace “building” with “canal” or “tunnel” and the analogy holds true.
Data governance ensures the most important data in an organization are easy to access, understand, and use. It ensures that expectations are set at an enterprise level regarding data use and how to address data quality issues. It’s also about understanding and documenting who is responsible for what, and who the stakeholders are.
The goals of data governance are to:
- Ensure data meet the needs of the business.
- Protect, manage, and develop data as a valued enterprise asset.
- Ensure that data are used properly, both to avoid introducing data errors into systems and to block potential misuse of personal data and other sensitive information.
- Improve data quality, lower data management costs, and increase access to needed data for data scientists, other analysts, and business users.
- Break down data silos in an organization.
Why do we need governance for spatial data?
Maybe it’s as simple as because it’s data. In order to make our data more valuable in an organization, we have to think of data, spatial or otherwise, as an asset. How can we receive the most benefit from this asset? How do we manage this asset effectively and efficiently? Like any asset, we want to ensure that we build standards, policies, and processes for the usage, development, and management of data, create the right organizational structure, and develop the supporting technology infrastructure. (Panian, 2010)
Data governance is a key factor — and driver — of an organization’s approach to data management. A governance plan clarifies roles, responsibilities, accountabilities, behaviors, processes, and structures, which in turn enables:
- Appropriate, efficient, and effective data management.
- Better project outcomes.
- Better understanding of trends and processes.And ultimately, a more effective and satisfied workforce.
What is the result of implementing and embracing a data governance framework? For the organization, it means the ability to create spatial data of high quality that are standardized, well-understood, well-utilized, and well-governed; fFor the workforce, access to the right information in the right formats by the right people at the right time. Data governance is what will enable organizations to plan wisely and be prepared for unexpected situations, use data more efficiently, and increase their ability to share data with confidence.
References
Panian, Zeljko. (2010) Some Practical Experiences in Data Governance. World Academy of Science, Engineering and Technology, 62. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=173626431ed114b232c1dab8ae60a005a0d593dc
A book and blog about where to find geospatial data, how to know if the data is any GOOD, and societal issues surrounding data such as location privacy, copyright, ethics, and AI: https://spatialreserves.wordpress.com/