Accurate and reliable data can bring context to research studies, help people understand trends, aid executives in knowing what’s working well for achieving organizational goals and much more.
However, data discernment is crucial. Bad data can completely negate all the positive factors of trustworthy information.
Some glaring imperfections in data can be spotted right away. For example, those working closely with healthcare data might find various misspelled names or cases in which an entry appears two or more times in a list but should only be there once or date-related inaccuracies.
However, other indications of bad data aren’t obvious.
People who work with data may unintentionally or purposefully look for data that supports their findings or theories. When it happens automatically, it’s a phenomenon called confirmation bias, where people search for and notice information that aligns with their views while ignoring material that does not. That’s been typically found in medical research, where those looking to support a theory may be selective in the data they include, and that which they ignore.
Using data responsibly and being mindful of potential bad data means working hard to neutralize bias. In the medical arena and elsewhere, that might mean taking the step of depending on someone who has a neutral stance to fact-check material and hunt out these kinds of practices.
Spotting bad data also may mean verifying the sources of data, because that can indicate specific, externally gathered statistics. Verification of such provided information can have profound implications for healthcare organizations.
For example, if a headline declares 95 percent of healthcare organizations were attacked by hackers during a given year, readers might accept it as fact without checking the source of that claim. But what if those hacking statistics came from a security firm that only got established in a community last month and had a history of trying misleading tactics in the United Kingdom before transferring to the United States? Then, it’s possible that the company is lying or using strategies to make providers afraid about getting hacked and feel compelled to use the business’s services.
Scientists have also manipulated data in research studies to make it highlight certain conclusions. Then, other investigators waste time and money to replicate the findings and later realize they were chasing a questionable finding.
Evaluating the worthiness of data involves determining whether it’s possible that the people or company responsible for its publication might be trying to enhance their reputation, bolster profits or enjoy another advantage that goes beyond keeping people informed.
Data scientists, organization executives and other people working with data aren’t always honest about the limitations of data—and there may be gaps in the way it’s managed that cause inaccuracy. If decision-makers put too much emphasis on flawed data, they may make mistakes and feel less confident about using data to educate their conclusions in the future.
A 2016 survey of CEOs found 84 percent of them felt concerned about the quality of data they used while making decisions. And they have valid reasons for feeling wary—bad data could cause financial repercussions if leaders put too much trust in material that’s ultimately found to be spurious.
It’s also crucial to consider the wasted time from bad data. When professionals engage in data-driven decision making, they may be relying on content filled with non-human influences such as bots or malware. If that happens, they could get false perceptions of customers’ journeys at websites or the factors that cause them to linger on certain pages instead of others.
There are reputational risks, too. If an organization releases public research that later gets proven inaccurate, it’ll be difficult for that entity to encourage trust in future material.
When leaders of healthcare organizations and companies blindly trust data—especially when making decisions—they inevitably set the stage for problems. Staying aware of the characteristics of bad data is only a first step. People who deal with data must be mindful of its limitations and demonstrate honesty when disclosing those shortcomings to others.
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