Chief executives are much better able to manage their organisation when that data is up to standard, particularly when they are confronted with major unexpected events such as the COVID-19 pandemic. Moreover, owing to the fact that it saves a lot of money, it may, therefore, be well worth the investment in a 'tedious job'.
You could say: one push of the button and an organisation knows how many people it employs. However, it’s just possible that such a simple question will yield several answers, for example because different definitions of what constitutes an employee circulate within the organisation: are you talking about people or Full-Time Equivalents (FTEs)? Do you take the average during a period or the headcount at the end of that period?
Organisations are therefore not always able to answer relatively simple but important questions on the basis of their own data. This is because clear definitions are lacking, or because data is being recorded inaccurately at source, in the wrong format or not at all. Furthermore, systems that have been integrated together don't communicate well with each other in a timely and accurate manner.
The agility of response of organisations to unexpected events is almost always about information. How quickly are you, as a business, able to obtain information from which you distill insights and on which you ultimately base decisions and actions?
During the pandemic, we have seen that many organisations did not start looking for information or know where to start looking for information until the crisis had really hit. This is due to the fact that they didn't have the access to the right information in a timely and well managed manner which, in turn, did not enable them to be agile in their decision making to help them navigate at the beginning of the pandemic.
Flexible and resilient data structures with corresponding processes and systems are not yet widespread in many organisations. Many departments within organisations are therefore involved in a lot of time-consuming and manual work trying to resolve data quality issues in order to generate the right insights to help guide decision making.
Improving the quality data is time-consuming, intensive and often results in laborious debates between many people within an organisation, because it affects almost every department. Moreover, it is typically one of those investments whose usefulness and return are not immediately apparent. In many cases, it is not clear where the responsibility lies for solving this complex problem.
It's a job almost no one feels like doing, so nothing gets done. Or the focus is on improving processes and systems which, while also important, will not solve the problem of data quality: a new system will not deliver on its promise if it runs on incomplete, invalid, untimely and ambiguous data.
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