What is data cleaning and why is it important?

Today’s businesses should treasure data as one of their most valuable assets. At the same time, poorly maintained data can turn from an asset into a liability that increases costs and risks. Problems faced by companies that need to manage data better can include making ill-advised decisions, spending too much on storage space, slow processes, and worst of all, not engaging customers.

Entrepreneur cited research that found low-quality data cost organizations an average of 30 percent of annual revenues. Many companies don’t know how much bad data costs them because they have yet to investigate the problem. On the positive side, organizations can take time to understand the issues that poor-quality data causes and develop a data-cleaning strategy to remediate them.

How bad data leads to bad decisions

Poor data management can damage businesses in various ways. Important examples include:

• Compliance issues: New compliance standards, like HIPAA, GDPR, and CPAA, have forced businesses to revisit their data management tools and processes. For instance, some standards explicitly state that companies must keep personal information accurate and timely. Businesses with well-managed data will encounter fewer regulatory problems and can face audits confidently.
• Poor audience segmentation and targeting: Many businesses improve their advertising ROI by delivering targeted messages to segments of their audience. For audience segmentation to work, data must accurately reflect the demographics of each audience member. Think of all the sales calls to renters to sell new roofs or to 79-year-olds to offer non-Medicare health insurance. Better data quality ensures relevant messaging, higher customer satisfaction, and greater returns.
• Trouble predicting processing costs: Businesses that need help understanding the status of their stored information may underestimate the costs of processing or migrating data. For instance, unstructured data, like letters and messages, takes more sophisticated tools and effort to process than structured data. With an understanding of the data, project planners can base their projects upon accurate estimates.
• Problems measuring outcomes and making decisions: Companies should set targets to meet business goals based on measurable results. However, bad data can skew metrics, making it impossible to measure the true ROI of investments. In turn, choices made upon inaccurate measurements can lead to customer dissatisfaction, wasted money, and worse ROIs in the future.

Making sound business decisions, offering an excellent customer experience, and even remaining compliant to avoid potential penalties rely on superior analytics derived from high-quality data. Many organizations haven’t taken the time to gauge their data quality. Thus, decision makers may not realize that this problem devalues their entire company. On the positive side, data cleansing can help businesses understand what they have and how they can improve.

What is data cleaning?

Sometimes called data hygiene, data cleaning describes processes that organizations schedule periodically. These tasks can identify, remove, and remediate corrupted, incorrect, poorly formatted, incomplete, and duplicate records. At the basic level, these tasks remove irrelevant and outdated information. Manual or machine processes may also tag and categorize files to make them easier to process and access.

Data cleaning techniques

Data cleaning techniques may vary because of the unique needs of an organization. Consider these basic steps when developing a data cleansing plan:

• Identify and correct errors: Common issues may occur due to outdated data or input errors. Ensure the files contain up-to-date, validated information.
• Remove unnecessary duplicates: Duplicate records can use up storage space, increase processing time, and produce erroneous reports. Some processing techniques may create subsets of master files for greater processing efficiency, which include duplicates. Still, organizations should keep track of these to remove them after use.
• Remove irrelevant data: Sometimes, irrelevant data enters the system because it’s included in purchased datasets or as a remnant of older computer systems. As with duplicates, deleting this information can free storage space and speed processing.
• Standardized data formats: Problems may arise because of inconsistent capitalization or naming conventions. For example, “Not Applicable” and “N/A” may both appear, and the system needs to recognize them each as the same category.

Cleansing data ensures people and computer systems can recognize and trust their input. Another data cleaning technique may include data transformation, which changes information from one format into another. In addition, data aggregation involves summarizing raw data into a more helpful summary format that machines and people can read and understand, such as spreadsheets, charts, or reports.

Develop a clear strategy to clean data

Almost all established businesses can benefit from planning and implementing a data cleansing strategy. Over time, system changes, low-quality purchased data, human errors, and lack of proper governance inevitably lead to issues. Poor data quality makes processes inefficient and uses up storage, leading to bad decisions and a poor customer experience.

An investment in improving data quality can yield substantial returns. At the same time, businesses should plan to maintain their data’s value with a holistic data management strategy. This strategy future-proofs information and leads to higher returns, greater trust, and a better experience for employees and customers.

Highlights of a holistic data management strategy include:

• Technology: Asking people to comb through massive data stores manually would not work because of the time and effort required. Organizations can invest in intelligent software to help them investigate and remediate issues in large batches. This software can also help ensure the business manages data well in the future.
• Strategy: A strategy to ensure collected data adheres to rules for tagging, structure, and organization. Plus, the process should include data governance rules to prioritize, tag, organize, and segment information in the best way for that company.
• Deployment and sharing: Understand the best ways to use the collected information. For instance, marketing may employ targeting data that might prove helpful in offering insights for customer service and sales.
• Future-proof data: Plan for the future with built-in resiliency. Businesses can only sometimes predict the next disruption. However, they can develop the resiliency they need to respond quickly to changes in the economy, business model, or government regulations.

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