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Data Cleansing

The Value of Data Accuracy

By February 28, 2020December 28th, 2020No Comments

There is an axiom that has existed in data science for years.  “Garbage in, garbage out.”  Despite the existence of such sound advice, too often it is forgotten.  Organizations routinely use information that, when tested, does not meet the minimum acceptable range of data accuracy, per the Harvard Business Review.  However, creating information that is acceptable has a cost in dollars.  Is it worth the investment?  What are the benefits of having good data?

1. Importance of Data Quality

In the modern business landscape, data feeds directly into revenue.  When you are able to rely on your information because it is clean of duplicates, utilizes standard formatting, and has correct values in the critical fields, you can make confident decisions that lead to increased revenue.

2. Cost of Bad Data

Data feeds into many business processes.  Bad data means those processes run less efficiently.  Direct mail campaigns are sent to incorrect addresses.  Email campaigns send messages to non-existent addresses.  Even the best designed campaign will fail to meet expectations without the clean information to feed into them.  Good data means saving money on wasted efforts.  Well managed data means campaigns are efficient and targeted, helping to maximize return on a lower investment.

3. Customer Satisfaction

Customer satisfaction is imperative to any business.  By assuring that records are appropriately clean and accurate, your organization can be sure to match different elements together, you can be sure to deliver what your customers expect at each and every touchpoint.  Your customers will be happier, feeling that your organization is meeting their needs.

4. Impact of Data Accuracy (in time)

One of the sometimes hidden issues of poor data quality is the time spent manually fixing bad data.  Simple steps like standardizing “st” versus “street” done manually can be time consuming and error prone.  Often times the actions that are taken to fix information are done on output records, not the actual source, which means that the same steps will have to be repeated every time the source is used.  Departments will codify manual data correction and normalization as part of standard procedure, instead of expecting clean, correct information!  Applying principles of good data management saves time.

5. Good Data vs Bad Data

Organizations spend money on systems to maximize the data accuracy and the value they can get from their information.  But the underlying records doesn’t always allow these systems to operate as best possible.  But if you can be confident that your records are clean, you can be confident in the results of processing that data through other systems.  The investment in systems that work from your organization’s data will have a greater ROI.

Data is an invaluable resource, but only if you can rely on it.  The examples above are just some of the ways that clean data provides value to organizations.  In order to achieve this goal, it sometimes takes using a third party resource to perform data hygiene tasks.  DataGroomr is a data cleansing solution that is simple to use, while leveraging advanced machine learning technology to apply best practices in data cleansing.  Questions?  Reach out to us at or signup for a free demo at

Steven Pogrebivsky

Steve Pogrebivsky has founded multiple successful startups and is an expert in data and content management systems with over 25 years of experience. Previously, he co-founded and was the CEO of MetaVis Technologies, which built tools for Microsoft Office 365, Salesforce and other cloud-based information systems. MetaVis was acquired by Metalogix in 2015. Before MetaVis, Steve founded several other technology companies, including Stelex Corporation which provided compliance and technical solutions to FDA-regulated organizations.