By Ken Drazin, Consultant
One question every organization should ask is, “Do we have reliable data?” Your initial answer may be, “Well that’s IT’s issue not mine.” Bad data can affect every division of an organization, from Sales all the way down to HR. Several studies have estimated that bad data quality costs U.S. businesses over $600 billion per year due to inefficiency and lost customers. In fact, a recent Gartner survey revealed that:
- 140 companies surveyed lost an average of $8.2M annually due to bad data
- 30 companies surveyed estimated their losses at $20M
- 6 companies surveyed estimated their losses to be more than $100M annually
So how could bad data create such a huge cost?
- Bad data quality quickly results in inadequacies in business processes that rely heavily upon data—reports, inventory systems, etc. Large companies often have more than 400 applications in their information technology portfolio. Up to 200 of the applications read or update product data. Ultimately, bad data will result in the rework of all data to ensure they are meeting requirements of all your organizations source systems.
- Bad data quality gives leads to incorrect decisions. As they say, GIGO (Garbage In Garbage Out), decisions based on poor data are poor decisions and critical decisions based on poor-quality data can have very serious consequences.
- Poor data quality can have external effects on your organization as well. Bad data can lead to mistrust with your clients/customers who will quickly lose confidence in your organization’s abilities once your bad data becomes exposed.
Whether bad data is projecting your sales/costs as a million more, or a million less, you can clearly see that data either helps you earn money or waste it. Over the next few weeks, I will detail the five key standards of data quality and what each exactly means to your organization – Completeness, Accuracy, Timeliness, Uniqueness, and Consistency.
