We start with the basics: if people do not trust the quality of the data, they will not base decisions on it. This lack of trust can be due to various factors: the data may be inconsistent across departments, it may take too long to collect and analyse, or it may be inaccurate due to a faulty system or human error. The headache associated with trying to reconcile incompatible datasets also often leads people to stop using data altogether. And that is exactly what you want to avoid.

If people do not trust their data, they will likely choose for themselves which data they will use for any insights. In doing so, they may ignore potentially useful insights because they do not want to feel that they are making decisions based on poor information. This lack of trust paralyses decision-making, with managers choosing to rely on their intuition rather than on data-driven insights.

Poor data can create the perfect environment for fraud within your company and lead to significant financial losses. Fraud generally involves people manipulating data in order to ‘misappropriate’ money or other assets from a company. This may be an individual employee making false claims about working hours or payroll data that do not correspond to reality. Fraudulent data can even be caused by the desire to achieve organisational goals at the expense of reliability or quality. In such cases, data are often presented more favourably than they are in reality.

To combat the risk of fraud, companies must have systems in place to identify and investigate unusual activities. Poor data can, however, make this much more difficult. If you cannot rely on your systems to provide accurate data about employees' working hours or pay scales, it becomes much harder to identify false claims.

Incorrect data can lead employees to lose trust in your company in various ways. Poor data quality and a lack of transparency in data processing can lead to privacy-related issues. It can also cause your company to make errors or miscalculations that directly affect internal and external stakeholders, such as sending incorrect payslips to employees or promoting employees unfairly. In systems that do not distinguish between similar names or contain typographical errors in ID numbers, such situations can occur quickly.

If employees feel that you do not have control over your data, they are also likely to lose confidence in your overall ability to run a reliable and efficient business. In addition, poor data can lead to people being treated unfairly, with systems accidentally blocking them or systematically excluding them on the basis of data. A CV screening process based solely on information from historical CVs could inadvertently result in only white men without a history of work limitations being invited for interviews. This clearly leads to poor PR and can seriously damage your brand.

When you have poor data in your systems, it can be a more difficult task to find and remove them. In this case as well, it is better to prevent poor data quality in the first place and pay the necessary attention to data input. Data quality problems can be caused by a wide variety of reasons: inaccurate data entry due to human error, errors introduced by automated systems during data entry, outdated data or missing elements, and data transferred from one system to another in a format that cannot be properly read.

You also cannot clean up these data with the push of a button once they have caused a problem. It takes time and money to determine where the issue originated, manually adjust the raw data and rerun all subsequent analyses.

Sometimes this even means redesigning the entire data collection process or reversing or containing the negative consequences. You may then need to minimise delays in business-critical processes and even restore the correct access to systems for the people who need it. A doctor suddenly losing access to the operating theatre because their badge is no longer recognised is just one of the consequences of poor data that you would rather not have on your conscience.

Data are an incredibly valuable resource that can make or break your company if not managed properly. It is easy to see the enormous costs associated with resolving data issues, but it is much more difficult to quantify the additional losses that companies suffer as a result of poor data quality. The good news is that all of these problems can be addressed at the source – by ensuring that you collect high-quality data.

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