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For Analysts And Managers, These Are The Top Data Management Practices Your Team May Be Overlooking

Every company managing data – especially data associated with Geographic Information System (GIS) analysis and technology – needs to have a strategy in place for consistently maintaining its assets. This is true no matter the size of the data stores. What practices might you be overlooking in your own data management strategy? Let’s explore below.

1. Simplify access to traditional and emerging data.

Being able to quickly and easily access files is a key component of every data management strategy whether we recognize this or not. Why else would companies take the same to organize and clean an enterprise network of outdated and erroneous data? Having files spread across a network can slow a workflow down and complicate decision-making in cases where a user may not be familiar with where necessary is stored.

Simplifying access to spatial data is an important point in every data management strategy we don’t want you to overlook. Companies can accomplish this by keeping their network as condensed as possible, limiting the number of drives across which data is stored. They can also employ specialized filing systems that seek to categorize data based on ways users are most likely to use it, such as by region, project, etc. Our own team has also found success is managing data across networks by employing customized data registries that allow file paths for spatial data like Layer Files to be defined in a single configuration file or spreadsheet and pulled directly into ArcMap with the click of a button.

2. Don’t underestimate the value of categorizing your data.

Organization is not just a buzzword in lifestyle magazines. It is also a key component in properly managing enterprise data. Organizing data can involve any number of aspects, like storing data based on region, owner, date, or relevancy to a project. What all of these systems have in common though is that categorization is a catalyst for successful organization.

Incorporating categorization in a data management strategy allows you to better analyze the context of the data as well as better assign responsibility for maintenance of its quality.

You do not necessarily have to physically categorize these files – simply attributing classifications to files can help to streamline data management practices and increase comprehension of available resources.

3. Make note of metadata and make it a priority.

We will be the first to admit that metadata is not the most glamorous aspect of data management or data analysis. With that said, it is one of the most useful aspects of data outside of the actual content it represents. Metadata makes it easier for professionals, teams, and companies in general to identify and reuse data correctly. By noting the when, where, why, and by whom it was created – as well as any other pertinent details – the process for both using data as well as maintaining its quality becomes much simpler than if it were lacking that information.