Poor Data Quality – Impediment or Opportunity?
It seems that many business owners and managers believe that to be useful and valuable, data must be perfect. The reality is that all data is imperfect. The world is filled with imperfect data, just as it is filled with imperfect people. So let’s get real about data quality. If the imperfections are small and incidental, then they won’t materially impact on value and utility. If the imperfections are significant and the data is of such poor quality as to be completely useless then why is the business wasting time and money to capture, store and manage it at all? Why would you knowingly continue to create and store rubbish data in your business?
Imperfect data is not useless data, rather it presents a significant opportunity. In many cases the root cause of poor data quality is manual data capture or process elements performed by humans. This will be costing your business in terms of additional resources and lost productivity, not to mention the significant consequences from impaired strategic and operational decision-making.
A Big Opportunity
Rather than viewing poor data quality as the impediment that is holding your business back, it is a very powerful indicator of a big opportunity to create value in the business. In understanding the source of the data quality issue, we’ll identify redundancy and inefficiency in certain processes that can be improved, automated or removed in order to both resolve the data quality issue, but more importantly to make real improvements to assist the business operate more effectively and profitably.
In today’s challenging and competitive business environment, your data can be a source of significant competitive advantage. Do you see the full opportunity in your data or is your data glass half-empty?
Speak to your PKF Business Adviser today to see how we can help you achieve your objectives by leveraging your data opportunities.
How to ensure you can access the value available in your data:
- Develop a simple and effective data strategy
- Implement strong data governance protocols
- Automate data capture seamlessly into the core process and remove manual human inputs
- Ensure input controls exist in any manual human inputs
- Train system users well and continuously to ensure consistency
- Undertake periodic reviews of key data sets to identify improvement opportunities.