More and more corporations both large and small are realizing that their critical data is a key corporate asset, with just as much value — and just as much need to be governed and managed — as their physical assets. Poorly or non-governed data can have massive impact on a corporation, even to the point of jeopardising its existence. Properly used, such data can also reveal, and enable the enterprise to take advantage of, new business opportunities or greatly increase the profitability of existing ones.
As a consequence of realizing that data is a key corporate asset, more and more enterprises are putting in place people, organizations, and procedures for data governance, all with the objective of ensuring that the data assets are understood, of high quality, and are fit for the purpose to which the data is put by the enterprise or its customers. And, as a result, more and more enterprises are investing in tools for data governance and data management.
Data management tools support:
- the capture and persistence of the data itself
- the specification of the data models and business vocabularies to which the data is to conform
- mechanisms for data validation and quality assurance, and
- the assignment of life cycle states as the data moves through its life cycle within the organization
Data management tools also support the definition and association of metadata to the data, providing a maintenance capability, as well as, a means to add value to an evolving corporate resource.
Data governance tools:
- support the assignment of persons and organizations to particular roles in relation to the data (such as the capture of metadata, and the execution of data validation), and
- track whether these persons and procedures have been carried out as required
- track who is using the data and how often it is accessed.
Data governance provides the facilities to track the employment of data governance procedures to ensure data is of high quality, and that the associated metadata enables the data to be understood, and assessed as to fitness for purpose.
These principles apply equally to spatial data, such as GIS and Engineering Drawings, as they do to conventional tabular data, like customers or products. In many cases, spatial properties are simply part of the description of conventional data objects such as a person’s location, the “route” of a pipeline, or the extent of a building or mineral claim. In this context “spatial” is most definitely not special, and the tools for Data Management and data governance are the requirement. Just as such tools may work in conjunction with conventional database management systems (e.g. customer database), so too can we expect them to work in conjunction with conventional GIS.
Data governance is essential to manage data as a corporate asset, regardless of the types of data that are being managed. Data governance is a set of capabilities in its own right, often building on the governance capabilities of data management systems.
Enterprises should start thinking about their data as an asset that is just as valuable as their material goods. If they manage and govern their data in this manner, good things will happen.