4 Common Causes of Bad Data: How you can deal with them

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4 mins read

“Great vision without great people is irrelevant”. “The principle is not getting the right people on the bus, it’s first getting the right people on the bus.

Jim Collins, Leadership and management coach

To keep up with challenges and opportunities in your sector, you need a strong, reliable workforce that manages data well.  Misinformation is costly for any organization. Misinformation is terrible for the military, the government, hospitals, farmers, etc. Bad data is bad business!

You probably have a digital transformation strategy that is improving the performance of your workforce. While that is great, a key question that tests the viability of the solution is “are you getting quality data?”

Getting your workforce right minimizes loss due to poor data entry. Poor (dirty) data can cost you more than sales, it can damage your reputation as an organization.

For this reason, dealing with terrible technology adoption (or poor data) in the workplace should be a priority for organizations. Workers inputting wrong data that invalidates the system is an unavoidable aspect of running a business that needs to be checked.

According to Gartner research, the average financial impact of poor data quality on organizations is $9.7 million per year. IBM also discovered that in the US alone, businesses lose $3.1 trillion annually due to poor data quality.

Can you imagine how much we lose monthly in Africa due to poor data quality?

Research has shown that bad data is on average costing businesses 30% of their revenue. The cost of poor data quality for banking firms, on average, is around $15 million per year. If you begin to assess the life and financial cost of poor data in the food industry, governance, healthcare, and education you would start today to prioritize “clean data”.

Organizations that use spreadsheets for recordkeeping understand this pain so well.

Inaccurate or incomplete data will missinform your organization.

Irrespective of how super your formula is, one wrong input in a cell gives the wrong final result; making the spreadsheet invalid. You know how misleading misinformation is, right?

But how does data get bad in the first place?

Let’s look at few common reasons for poor, dirty, bad data.

The System Design

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Proper digital transformation avoids stories that touch the heart. When evaluating the realities of your organization it’s important to consider the ease (or simplicity) of the proposed solution. Tedious data entry easily leads to poor data quality. Making people stress over inputting data is never good.  The software you use has to be designed with simplicity and a good user experience in mind. There are good design principles to follow.

Make it simple.

Reliability of the Source

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It’s very important to guide your workforce to get data from a trusted and reliable source. Some organizations like to have a single source of truth, that is, a central database that has vetted data where other applications or users can feed on. However, when creating your database it’s very important to check the realism of the data. We highly recommend frequent evaluations of the realism of data. One of the selling propositions of Blockchain technology is in how data is vetted and stored.

The Literacy Level of Your Workforce

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Good execution relies on people, operation, and strategy. A good strategy will fail with the wrong team. People with poor literacy will most likely input poor data, and wouldn’t be able to spot poor data nor correct them when they see one. In one of our posts, Digital Transformation Journey :: Generating Data From Your Business , we emphasized the importance of getting the right people. People who are teachable, fast learners, and smart will help tame poor data quality.

Get smarter people.

Feedback Mechanism

It’s prudent to build a system that considers the green (success) and red (error) paths. Following good design principles reduce the bottleneck of figuring out what went wrong. A good design will help you swiftly understand that you have a poor result, sometimes by automatically matching the data received with reality. A good system can spot invalid data. Good software gives good feedback that guides the user to input proper data.  Listening to your business is critical.

Conclusion

There are so many ways you can get this right. However, we have highlighted common causes of poor data.

Get your team right. Keep entries simple. Evaluate the results of your system and match them with reality. There are guided practices for improving data quality. If you need further assistance, kindly get in touch with us.

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