7 data cleansing best practices
Organizations rely on data for analytics and decision-making, but if that data is flawed, inconsistent or otherwise unreliable, it's not as valuable.
One of the most important steps that data teams can take to ensure data quality is to launch a data cleansing initiative.
Data cleansing refers to the process of scrubbing data to identify and correct data quality issues, such as errors, duplicates, outliers and missing data. It provides a formalized process to validate, standardize and enrich data based on the organization's goals and objectives. The purpose of data cleansing is to make sure that the people who rely on the data can trust that it is accurate, consistent and complete.
Data cleansing can play a significant role in achieving the level of quality necessary for an organization to thrive in today's data-driven culture. Substandard data can affect analytics and decision-making, reduce productivity, increase operational costs, impede marketing efforts, affect customer service, and lead to missed opportunities.
Despite its advantages, data cleansing can be a significant undertaking, especially with large volumes of distributed data. To be carried out efficiently and effectively, a cleansing initiative requires careful planning and execution. Data teams should follow some best practices that highlight important considerations for launching a data cleansing effort.
1. Define data quality standards
Before data teams can cleanse data, they must develop data quality standards that align with their organization's goals and objectives. Then they can assess the data's condition and potential problems. The standards provide guideposts for measuring the data's quality and identifying issues. Without these guidelines, evaluating the data is harder, which increases the risk for inaccurate decision-making, unexpected costs and a lack of trust in the data.
Data quality standards provide rules and guidelines for validating and formatting data and ensuring its consistency during the cleansing process. They also define key metrics for measuring the data's accuracy and provide a methodology for categorizing the data so teams can manage, track and understand it easily. The standards must be documented carefully, communicated clearly, and reviewed and updated regularly to meet business requirements.
2. Identify data quality issues
Next, data teams should assess their data to identify where quality issues might exist based on the rules and guidelines specified in the standards. The assessment should include existing data -- whether on-premises or in the cloud -- as well as newly generated and collected data. Depending on the amount of data and how it's used, an organization might prioritize certain data stores over others, but the goal should be a complete analysis of all relevant data.
Data teams should begin the assessment process with a comprehensive audit of their data so they can fully understand the scope of their cleansing efforts. This includes profiling the data to understand its structure, content and relationships between data. Then, admins should validate the data to determine what aligns with the quality standards and what is inaccurate, inconsistent or incomplete. The goal is to have a complete understanding of the data's quality before moving on to the next step.
3. Develop a data cleansing plan
After completing their quality assessments, data teams and key stakeholders should use that information to start planning their data cleansing efforts. The plan should describe the steps required to facilitate the most efficient and safest data cleaning possible. Without a comprehensive plan, data teams might fail to scrub their data properly, miss critical issues or incur unnecessary costs.
A data team's cleansing plan should consider the types of data that the organization stores, manages and processes, as well as the organization's overall business and data requirements. The plan should also include the steps necessary to address data quality issues -- such as duplicate, missing, inconsistent or inaccurate data, and data outliers -- and how those steps should be carried out. In addition, the plan should assign clearly defined roles to those participating in the cleansing process.
4. Educate teams on data quality and cleansing
One of the most important steps in carrying out a data cleansing effort is to provide the people participating in the cleansing process, as well as those handling the data, with the training and education they need to properly address data quality issues and ensure the quality of that data going forward. Everyone who works with the data, no matter their role, should understand the value of high-quality data and its importance in meeting the organization's objectives and goals.
Training and education are especially important for those cleaning the data. They should be well-versed in the data quality standards and cleansing plan, and be fully informed about current data quality issues. They should also be trained in data quality techniques and tools, as well as how to protect the data and comply with applicable regulations. Workflows and individual roles should be clearly defined, emphasizing collaboration and open communication.
5. Deploy tools that automate data cleansing
Today's organizations must often contend with massive volumes of distributed, heterogeneous data. Cleansing this data requires tools that can streamline operations, automate repetitive tasks and monitor the data throughout its lifecycle. These tools help lower the costs of managing the data, and they can help foster more effective analytics and decision-making because the data is reliable and trustworthy. Without such tools, the data is more susceptible to errors, duplications and inconsistencies.
Today's tools include comprehensive functionality for cleaning, managing and protecting data. Some incorporate AI and other advanced technologies to deliver greater efficiency and accuracy. Many tools can automate routine data cleansing tasks. They can also validate data as it's being ingested into the organization's internal system. In addition, the tools are often customizable to accommodate the organization's specific workflows and business requirements, and they can integrate with other data management tools.
6. Monitor, document and assess cleansing operations
The ability to cleanse data and maintain its quality on an ongoing basis relies on data teams tracking and documenting all aspects of the data cleansing process, while continuously monitoring their data for quality issues. By carefully tracking their operations and data, businesses can improve processes, troubleshoot issues as they arise and provide new team members with detailed information about how to approach data cleansing within the organization.
Data teams should maintain complete records of the steps they take when cleansing data. This includes information about how data quality issues are being addressed and any problems that arise during the cleansing process. They should then use this information to identify how to improve their operations and, where applicable, help find tools to better streamline and automate their efforts. In addition, they should continuously audit their data for quality issues, looking for patterns that might point to holes in their data management processes. They should also keep stakeholders regularly informed on their operations and findings.
7. Implement a data governance strategy
Although there is no universal definition of data governance, data quality is typically a key component in governance strategies. Governance provides a structure to ensure that data quality can be achieved and sustained over the long term. Without a comprehensive governance strategy, data teams can have a difficult time achieving high data standards and carrying out their cleansing efforts, resulting in data that is incomplete, inconsistent and untrustworthy.
Data governance ensures the security, integrity, usability and availability of an organization's data, based on its current business requirements and internal standards. A governance strategy defines the policies and procedures needed to properly manage the organization's data throughout its lifecycle. In addition to data quality, governance addresses issues such as master and metadata management, data security and compliance, and document and content management. It also defines the roles and responsibilities of those handling the data. When launching a data cleansing initiative, data teams should be working within the larger governance framework to ensure the best outcome.
Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.
