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Measuring timeliness is vital to understanding data quality

Rachel Wheeler

March 7, 2014

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One of the most important things that companies can do to gain a better understanding of their respective markets is focus on data quality. If they have detailed, accurate records of the people around them and their patterns of spending, they can not only comprehend the economy today, but they can also predict what might happen in the future.

Of course, data quality is about more than asking the most basic questions - who, what and where. It's also about the fourth W - when?

Timeliness is a key factor when it comes to getting good data. If a company looks at information that's outdated - even if only for a few weeks or months - it can make all the difference. Data scientists in all lines of work need to think about the timeliness of the data they're working with. If they don't, their companies may end up making foolish decisions.

The meaning of "currency"
According to Information Management, the key value here is "currency." Companies shouldn't just look for data to be accurate - it needs to be current as well. On this subject, the news source cited David Loshin, author of the book, "The Practitioner's Guide to Data Quality Improvement."

"Currency refers to the degree to which data is current with the world that it models," Loshin explained. "Currency can measure how up-to-date data is, and whether it is correct despite the possibility of modifications or changes that impact time and date values."

Companies need to constantly assess and reassess the currency of their data. The last thing they want to do is make decisions based on information that's seen as "old news."

Looking at real-world examples
Consider, for example, the case of a coffee shop that wants to hand out loyalty cards for customers to use in a rewards program. The owner of the shop spends weeks counting how many times people use their cards and how much value the program adds. For months to come, he uses that data to argue his point about the rewards program.

But what if his data isn't good anymore? What if the rewards cards have become less popular, and people are buying their morning coffee somewhere else? The cafe owner shouldn't be basing his opinions on data he collected months ago. He should be constantly looking for new data to support - or challenge - his opinions.

This logic applies to any organization. Data quality is always important, and currency is equally vital.

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