BI Insights

The Data Cleanse: 5 Rules For Scrubbing Your Data

16 January, 2015

The Data Cleanse: 5 Rules For Scrubbing Your Data

Big data is big for business, and in an ever-increasing world of information, it might seem as if any kind of data can be an asset to your organization. However, hoarding incomplete or invalid information can lead to erroneous conclusions, resulting in poor business decisions.

At the time of year when juice cleanses are pervasive, try rethinking how your organization is gleaning insights from its data. Cleanse your data to ensure its uniformity and usefulness to your company.

What is data cleansing?

A normal step of the cycle by which data is extracted, transformed and loaded, data cleansing aims to remove errors and inconsistencies from data. There are a number of reasons so-called “bad” data exists in your database. For instance, misspellings, missing information, or invalid entries are errors that would be targeted in a data cleanse.

Data cleansing is even more critical when multiple data sources need to be integrated because there’s a higher chance that the sources contain redundancies. In this situation, your company is likely consolidating different data sources and it becomes even more important that you eliminate duplicate information.

For instance, Kellogg recently undertook a massive IT transformation to improve the way it did business while creating more efficient operations. First, all data was converted into a new system and a considerable amount of time was spent cleansing, mapping, and redefining their standardized data definitions. Then, different functional teams met together to come up with standardized definitions and values for the data, so that terminology about brands such as Special K were consistent across the board.

“We are able to ensure high data quality, and we will be able to retain the pristine data that we put into the system. Standardized data provides a foundation for better information management and decision making,” said Diana Karklins, VP of Application Solutions at Kellogg.

Five rules for data scrubbing

  1. Define and determine errors in your data set.

Errors can include misspellings, multiple names for the same information, or incomplete data.

  1. Correct data errors by standardizing information.

Organize and store data according to data type. Improve your data quality, processes and procedures by getting rid of the unnecessary and incomplete data kept in storage.

  1. Verify that your new data values are correct and uniform.

Ensure that there are no discrepancies in name for the same variable you are trying to clean and that the integrity of the data is still intact.

  1. Modify data entry to avoid future errors.

Implement data filtering, but use cautiously so you don’t end up with incomplete information.

  1. Assign a team to govern your data.

Effective data governance can play a vital role in driving new business opportunities and retaining existing customers by improving overall data quality and business intelligence.

So, start the New Year off right. Ensure that your organization has a process in place by which it scrubs your database of bad information so that you can be confident you’re making sound business decisions.

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