Data: Your most overlooked asset

If asked to think of a community’s most important assets, many would likely start by naming road networks, bridges, buildings, or possibly even transit and sanitary systems. But would data ever come to mind?

As the foundation of everything, data is a vital asset—especially for public sector organizations. In order to have confidence in the wide array of operational and strategic initiatives undertaken in any given community, it’s crucial to think of data like you would other assets and consider implementing department standards for data review, quality assurance and quality control practices. Every organization should aim to have access to accurate, reliable and timely incoming data, resulting in data confidence—which should be understood as a dynamic and continuous process.

Establishing a strong data management system guarantees better creation, collection and preparation processes, allowing for optimal data extraction and therefore better-informed decision-making skills. Though this process may seem impossible for those already experiencing time management and capacity issues, attaining data confidence can become a reality for all types of organizations with the proper implementation (and maintenance) of a data management system.

The creation of a data management system
As an integral part of a data management system, implementing a data management strategy is the process of creating a plan to accept the data acquired and how it will be catalogued, managed and processed by an organization.

Identifying the purpose behind your organization’s data collection process will be the first step in developing a data management strategy. Some examples of purposes include those related to asset management, such as wanting to gain maximum value from data assets, while others might be based around goals for financial reporting, work orders, or land planning initiatives. To successfully extract the purpose behind your data, additional supportive data, including static and dynamic data, can help your organization complete the picture of a well-operating data management system.

However, it is essential for an organization to establish parameters for acquiring and structuring all accumulated data well before it is stored. Consolidating data requires a collaborative effort between everyone involved in the data collection process, which is why a centralized data management strategy should allow for the connection of information and data silos, rather than break them down. Once your purpose is clearly identified and the process of acquiring data has been defined, organizations should be equipped with consolidated datasets that are conveniently housed in one system and easily accessible. With a strong data management strategy in place, organizations will not be left to sort through and disentangle scattered datasets that rely on different collection standards and span multiple departments.

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