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What defines good and bad data in the world of analytics?
If you’ve been following the Permea blog for a while, you’ll already be aware of the fact that some data are considered to be better than others. But how exactly do we define “good” and “bad” data, and what difference does this make to the analytics process?
With more companies harnessing the power of data analytics than ever before, we’re seeing an explosion in the amount of data available for use. That being said, there are many ways in which the quality of data can be compromised, and it’s crucial for any organization with a data analytics solution in place to monitor this closely.
When collecting data for any reason, it’s a sad inevitability that some of these data won’t be of the best quality. This might be down to a variety of reasons, such as human error, poor collection methods or implicit bias – but regardless of the issue, “bad” data, or data that are inaccurate, incomplete or inconsistent ultimately can’t be used for their intended purpose. “Good” data, on the other hand, will be free from typos, spelling mistakes, and character issues, and will have been collected in an ethically responsible manner with the full consent of the individuals involved. But why does all of this matter?
Essentially, the insights derived from bad data can’t be relied upon – making the analytics process something of a waste of time. All data will need to be cleaned and structured as part of the analysis process, but only to a certain extent, any data that seem unreliable will need to be struck from the record. A number of setbacks can be caused by “bad” data, ranging from mildly inconvenient to serious problems.
First and foremost, it creates a bottleneck, preventing your company from embracing all of the possibilities that digitalization and data analytics have to offer and, as a result, slows down the whole process. Not only does this cause a sense of organizational inefficiency, but it also creates a certain level of demoralization and disappointment amongst your team. It’s understandably frustrating to have to destroy data that, had they been collected and processed properly, could have been used to generate innovative new insights. It's a waste of resources, both financially speaking and in terms of time and effort.
Secondly, if these data are used without the analyst realizing that they’re “bad”, the result is a series of flawed insights. Conclusions made from these insights might be slightly inaccurate or completely wrong depending on the severity of the situation, which can seriously affect a company’s reputation and ability to operate successfully.
Although this sounds quite negative, the good news is that there are a handful of ways in which these “bad” data can be saved. Some data solutions - such as Permea - are able to clean and organize this data in order to fix errors and make sense of the chaos that can stem from this. By salvaging and making use of data that otherwise would have been discarded, data analysts are able to continue the process and uncover valuable insights that might have been missed. Ensuring completeness of data – which essentially refers to the comprehensiveness of data – is also very important. By minimizing the amount of missing data to the smallest possible number, organizations can reap the benefits of this valuable information, boosting everything from market access strategies to use case scenarios.
Data analytics offers a multitude of exciting opportunities for growing organizations, and incorporating solutions into your company is a big step for any organization to take. With the right guidance, advice and expert know-how, it’s possible to avoid the pitfalls of “bad” data, generating hugely valuable insights that provide enormous benefits to your team.