Data quality is often one of the most neglected topics in organizations. It becomes part of the culture of the organization to make statements such as The data for that report comes from our CRM system – but be warned: the data quality isn’t great or Sorry, I can’t answer that question because our data just isn’t good enough to support it. How often do you hear these statements repeated month after month – and even year after year?
When data quality is neglected in this way, it impacts the following:
- The effectiveness of business and compliance processes
- The ability to make high-quality decisions from reporting
- The ability to differentiate your organization from the competition
- The reputation of the organization with customers, suppliers, and employees
Organizations cannot leverage new technologies, such as AI and ML, to get the most out of their data. Those lofty ambitions to monetize data as a product all too often must be shelved.
Poor data quality is also an invisible drain on productivity. Every employee in an organization is impacted by poor data quality in some way – whether it is a report that doesn’t include all the information they need or a business process that they can’t complete because key data is missing. Eventually, people stop reporting the issues and create new (often highly complex) processes to deliver the required outcome despite the data quality problems. The problem of data quality is often considered to be too complex and too costly to resolve – leading to people searching for ways around the problems.
Take the example of a manufacturing organization with a highly automated product master data creation process. The products needed to be extended to the various manufacturing plants and sales organizations. This was done using tables containing rules (for example, field X should contain value Y for Italy and value Z for Germany). The process of creating products took just seconds but the underlying tables of rules had not been kept up to date, so this systematically created incorrect data for three products in one country. The incorrect data was carried over into sales invoices that reached customers. The product master data had a flag that, if ticked, meant an additional charge needed to be made for packaging. This flag was incorrectly left blank for the three products. A total of more than ten thousand invoices were distributed in six weeks without the additional packaging fee. A small issue had a substantial impact!
After reporting the product data issue consistently for many weeks – with no action taken to resolve the issue – the sales team established a process to manually correct each invoice before it reached the customer. This work was so repetitive that employee attrition became an issue. This was one of a raft of similar issues within this organization that was invisibly draining away its potential.
Does this sound like a familiar story in your organization? If so, I hope that this book helps you find a path forward.
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