Here is an excerpt from an article written by Eric Haller and Greg Satell for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.
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A big part of the problem is that numbers that show up on a computer screen take on a special air of authority. Once data are pulled in through massive databases and analyzed through complex analytics software, we rarely ask where it came from, how it’s been modified, or whether it’s fit for the purpose intended.
The truth is that to get useful answers from data, we can’t just take it at face value. We need to learn how to ask thoughtful questions. In particular, we need to know how it was sourced, what models were used to analyze it, and what was left out. Most of all, we need to go beyond using data simply to optimize operations and leverage it to imagine new possibilities.
We can start by asking:
How was the data sourced?
Data, it’s been said, is the plural of anecdote. Real-world events, such as transactions, diagnostics, and other relevant information, are recorded and stored in massive server farms. Yet few bother to ask where the data came from, and unfortunately, the quality and care with which data is gathered can vary widely. In fact, a Gartner study recently found that firms lose an average of $15 million per year due to poor data quality.
Often data is subject to human error, such as when poorly paid and unmotivated retail clerks perform inventory checks. However, even when the data collection process is automated, there are significant sources of error, such as intermittent power outages in cellphone towers or mistakes in the clearing process for financial transactions.
Data that is of poor quality or used in the wrong context can be worse than no data at all. In fact, one study found that 65% of a retailer’s inventory data was inaccurate. Another concern, which has become increasingly important since the EU passed stringent GDPR data standards is whether there was proper consent when the data was collected.
So don’t just assume the data you have is accurate and of good quality. You have to ask where it was sourced from and how it’s been maintained. Increasingly, we need to audit our data transactions with as much care as we do our financial transactions.
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Here is a direct link to the complete article.
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