What is Data Cleansing?
Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.
Over 50 percent of businesses spend more time cleaning their data than actually using it, according to research from TDWI.
So, the adage “garbage in, garbage out” is more relevant than ever, especially in this age of data-driven marketing.Marketers may crave access to more and more of the reams of data available to them. But that data won’t help deliver customer value, accelerate marketing performance, or increase marketing efficiency if it’s incorrect, inconsistent, or incomplete.
Ensuring data quality is crucial to marketing success.
There’s no quick fix to achieving optimal data quality, nor is there one for maintaining it. Both must be enduring efforts. As such, they require ongoing attention and adjustments as people, processes, and technologies change.
There are, however, six ways that marketers can act now to improve their data quality enough to allow them to transform their customer engagement and improve their marketing outcomes.
Data Cleansing Considerations:
- Data Auditing – Nearly 90 percent of data management professionals say they’ve added bad data to their data stores, according to Health Data Management. The best way marketers can ensure that the data they use and store is high quality is to create processes that block bad data from getting into their systems in the first place. Audit data sources to determine which have issues with quality, and then set the best courses of action to resolve those issues and eliminate bad data. If, for example, the first-party data salespeople are entering into a CRM system fraught with errors, consider training, incentives, or penalties based on data quality, or implementing technology that helps to reduce input errors.
- Build Bridges – Having multiple siloed systems creates fragmentation and inconsistencies. Most companies need these varied systems to maintain customer data specific to various functions or business areas. In fact, according to Dzone, 92 percent of organizations report having 16 to 20 data sources, and that data is spread across multiple locations in multiple formats. But it’s still possible to bridge silos using technologies that link those multiple data sources and create a high-quality, holistic view of customer data by using customer identifiers, such as a unique customer ID or email address. It’s also of increasing importance to link online and offline customer data sources to build more complete customer profiles.
- Mend Faulty Data – Repair existing data by cleansing, appending, and deduping it. When cleansing data, ensure that data elements are consistent; for instance, that information such as addresses, company names, and dates are all written in the same format. Simple examples include Corp. versus Corporation, and 12 November 2017 versus November 12, 2017. When appending data, use only the most reputable second- and third-party data sources to augment and enrich customer profiles. Doing so will avoid adding bad data into the data sources being cleansed. For deduping, look for technologies that use approaches such as semantics and machine learning, which can help to improve the results of matching records and profiles.
- Assemble a Smart Martech Stack – Systems that don’t integrate exacerbate the data silo situation. Look for open systems that integrate well with technologies the marketing organization is already using, or for platforms that have multiple capabilities necessary to ensure data quality, as well as the connectivity needed for that all-important holistic customer view. Partner with the IT team to ensure that the systems will actually integrate and will meet the expected requirements.
- Data Governance – There may be many “right ways” to maintain quality data, but there needs to be only one optimal way for each company. Data governance is a must-have. Use it to set parameters for data types, fields, quality, usage, and “ownership,” as well as communicating who’s responsible for data governance. Delegate the responsibility and authority for data governance to the person or team with the most vested interest in ensuring data quality or the best understanding of how to maintain it. And make sure metrics and measurable goals are in place to monitor progress and avoid back-sliding quality.
- Close the Skills Gap – Marketers rarely have the experience and training that data analysts and data scientists do in terms of setting up processes and systems that can help to ensure data quality. Provide training where necessary, so marketers can do more to help determine and maintain data quality. But also foster collaboration between marketers and data experts that allows for learning, creates efficiency, and enhances results.
Marketers yearn for insight that will help them engage customers and inspire them to action. Poor data quality leads to poor inferences and decisions. The only way marketers can get the insight they need to transform customer engagement is by ensuring the quality of their data.