![]() Getting the business process owners involved is a good start however, utilizing data discovery software is an important next step. In today’s digital world, data resides in different applications, devices and formats. One of the challenges in building or maintaining a data inventory is identifying all data sources that an organization works with, especially as data sources and dataset sizes change over time. Common challenges with data inventoryīuilding a data inventory is more than just a one-off process: It requires alignment across different departments, an investment of resources and technical expertise. The data inventory process can help an organization dispose of unneeded data while implementing data retention policies for sensitive or valuable data. With a data inventory, organizations can get better visibility into dark data, allowing them to identify where it came from and how they can utilize or dispose of it.įor example, an organization can identify and sort data into different categories, including unused data that needs to be retained for legal or compliance reasons, intellectual property, duplicate data, data under legal hold or other business data. Dark data is a byproduct of applications, data interactions and devices that add to the costs of maintaining IT infrastructure. Several research studies over the years have identified that most organizational data is “dark,” which means either it is unknown or unexploited. SEE: Top data quality tools (TechRepublic) Another important way data inventory helps improve operational efficiency is by identifying obsolete, redundant or trivial data that is unnecessarily using organizational resources. Operational efficiency is improved by having better access to data, which can help with decision-making, analysis and productivity. What Is Data Literacy, and Why Is It Important? Top 7 Power BI Alternatives and Competitors in 2023 Why the Database Market Keeps Growing Bigger and Stronger ![]() ![]() The clearer view of data that a data inventory enables is a major advantage for organizations looking to identify high-risk or high-value data and develop strategies to manage that data through its lifecycle. Why is a data inventory necessary?Ĭreating a data inventory allows organizations to improve operational efficiency, meet compliance obligations, mitigate risk and achieve data-driven business outcomes. This helps the organization understand how data flows through the business, how to protect it and how to derive the most value from it. The data inventory illustrates key information about data and provides invaluable information about how each data point interacts with other data. The purpose of a data inventory is to help an organization understand its data or metadata and derive valuable insights from it. Each listing within the data inventory often includes additional details about the data, such as its owner, name, source, format, access permissions, frequency of use and other properties. A typical data inventory has a comprehensive and up-to-date record of all data assets and where they reside in enterprise systems. For companies that are having trouble keeping track of all of their data asset types, locations and use cases, a data inventory becomes extremely useful.Ī data inventory is the catalog or map of an organization’s data assets. However, with the vast quantities of data that are now available and in operational use in the business world, it can be challenging for organizations to understand all of their data fully. SEE: Data governance checklist for your organization (TechRepublic Premium) Data is often referred to as the most important asset of an organization, as it can be used for purposes ranging from improving revenue and optimizing customer experiences to mitigating security risks. ![]() Over the last couple of decades, the role of data in the business world has become increasingly vital. Learn what it is and why it's helpful for regulatory compliance here. Data inventory: What is it and why does it matter?Ī data inventory is an important tool for identifying personal data in the data mapping process.
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