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4 Tips for Data Quality Maintenance

Ashly Arndt | May 8, 2024

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Retailers mining customer data for targeted marketing

Retail data mining no longer relies on loyalty programs.

Big data strategies beg the question: Speed or accuracy?

These capabilities have led many companies to rush forward with their plans, passing over important data quality measures.

The benefits of data cleansing

Cleaning your house is always a good idea. I especially love to clean on rainy days, like the one we’re having in Boston today. It gives you the chance to get rid of and organize all that clutter. Taking the opportunity to enhance your space by getting rid of the junk and allowing the important details to shine will bring a sense of clarity. The same goes for your data! Every data set could use a little sprucing up. Rather than waiting for a rainy day, it’s even better to give your data a regular dose of cleansing. After cleaning up the data in your system, you are open to a whole new world of cost savings and enhanced customer experience.

Codified governance policies necessary

Issues impacting data quality have grown more serious recently. The increase in scale of data reserves has left companies in need of solutions.

Retailers clamoring for big data may first need to firm-up grasp

This will likely change in the near future, since companies across industries are anticipating big data benefits, such as data-driven decision making and greater insight into data.

Healthcare could suffer from poor data quality

A growing number of healthcare providers are investing in, and using, electronic health records (EHR) systems to streamline documentation.

SEM and SEO produce best marketing results, expert says

Online retailers should use SEO and SEM in combination to get the best results from their marketing budgets, it is suggested.

A good data cleanse can clean up some healthcare practices

Having good data quality is important across a number of industries, but especially when it comes to healthcare.

PCI SSC reveals tokenization guidance

Tokenization is not sufficient technology for compliance alone, it has been claimed.

DIA concerned about data quality safeguards in clinical research

Further measures need to be taken to insure data quality is protected during clinical research, it is claimed.
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