Data scrubbing is a process that is performed to ensure the quality of the data in a database, which will then be used for analytics. Data scrubbing is a fundamental step to reduce the risk of basing the decision-making process on inaccurate, erroneous, corrupt or incomplete information.
Data Scrubbing Phases
To carry out an exhaustive data scrubbing process, it is necessary to follow the following phases:
Data analysis: its mission is to determine what type of errors and inconsistencies should be eliminated. In addition to manual inspection of data samples, it is necessary to incorporate applications that act on metadata to detect quality problems.
Transformation flow and mapping rules: Depending on the number of data source sources, their heterogeneity and the prediction of data quality problems, it will be necessary to act at two levels: one that corrects the problems related with data from a single source and prepare them for a good integration; and another that addresses data problems from a variety of sources.
Validation: As a general rule, validation is applied through multiple iterations of the analysis, design and verification steps; since some errors only appear after a certain number of transformations are applied to the data.
Transformation: It consists of executing the ETL flow to load and refresh the data warehouse, or during the response to queries.
Clean data flow: Once quality errors have been eliminated, “clean” data should replace data that is not in the original sources, so that legacy applications can also benefit from them.
Benefits of Data Scrubbing
Improves The Efficiency Of Customer Acquisition Activities
Thanks to data scrubbing, the problem of incorrect data is effectively eliminated, which allows increasing customer acquisition efforts dramatically. A lack of clear and up to date information has the potential to jeopardize those customer and supplier relationships your company has worked hard to maintain.
Enhances Decision-Making Process
Accurate information and data quality are essential for decision making. With clean data, the analytics that complete business intelligence can offer your company can be better supported.
Streamlines Business Practices
The elimination of data duplicates from the database can optimize business practices and save a lot of money.
Having a clean and well-maintained database can help companies ensure that their employees are making the best possible use of their work hours.
Companies that work to improve consistency and increase the veracity of their data through data scrubbing can dramatically improve their response rates, which translates into higher revenues.
Since data is a major asset in many businesses, incorrect data can be risky. Inaccurate data can reduce marketing effectiveness, thereby bringing down sales and efficiency. If the company has clean data, then such situations can be avoided. Data scrubbing is the path your business should go. It eliminates significant errors and inconsistencies that are inevitable. Using tools to clean up data will make everyone more efficient.