Data governance, though often overlooked, offers businesses a host of benefits. Keeping data up-to-date, accurate and complete often poses challenges for many business leaders. Thankfully, with the proper knowledge, tools, and patience, data and analytics leaders can build a team and utilize various available support systems to overcome these barriers and master data governance within their organization. What Is Data Governance and Why Is It Important?At its core, data governance focuses on the following:
Therefore, it's obvious why data governance is an essential part of most workplace operations. Many businesses heavily rely on storing and retrieving information for future use. For this reason, duplicate information, customer profiles, and disorganized data tracking can lead to significant issues. Without correctly managed data, numerous departments can struggle to perform their jobs correctly. These issues can result in a loss of productivity, increased costs, and even impact long-term customer retention. Finally, it's also important to note that storing data correctly and carefully monitoring how, when, where, and who uses stored data is also essential. Several regulatory agencies require companies to report on how they store and use consumer data. Others monitor data use and enforce transparency regarding certain types of information. Though, monitoring and governing data then become fundamentals to remaining in compliance with these regulatory agencies. How Can Companies Master Data Governance?Mastering data governance is no easy task, but it is critical to most businesses, no matter the size. Thankfully, through the help of available tools and the assistance of data and analytics professionals, data governance becomes a manageable task. Here are a few key strategies organizations use to organize, analyze and maintain data integrity successfully. Determine the needs of the organization and align them with data governance solutions. This step serves as the stepping stone for all data governance plans. Many companies find themselves frustrated with the way data is managed across the departments, as governance practices are often mistakenly data-based rather than business-based. Determining how employees use data, how often it is retrieved and accessed, and who can make permanent changes to records allows organizations to manage their information effectively. Determine key performance indicators. During this phase, data and analytics leaders should also consider outlining and implementing key performance indicators, or KPIs, for managing their data. KPIs allow businesses to use measurable metrics to determine the overall success of their data governance practices. Over time, organizations can use these KPIs to make adjustments to their data governance plans. By measuring KPIs, data governance becomes a practice of using data to align with business needs and moves away from the traditional expectations of data storage. Develop risk management and security measures for stored data. Finally, many governing agencies require companies who store data to remain accountable and transparent regarding data security. Therefore, modern data governance plans include a variety of layers of protection. Companies should consider the following when developing their risk management programs:
This step often involves working alongside your cyber security and legal teams to determine the appropriate action steps for data security. Who Should Understand Data Governance?Ultimately, any individual within an organization who may access, store or update data used by a company should receive training on data governance. Once you've developed a high-quality governance plan, ensuring each individual within your company who interacts with stored data understands the organization's data governance practices is essential. Furthermore, ensuring data integrity and accuracy may involve revisiting certain practices, changing methodologies, updating information, and providing additional company-wide training. Therefore, mastering modern data governance requires organization-wide cooperation and consistent monitoring to keep data consistent and error-free.