How Data Quality Impacts Your Bottom Line (And How to Fix It)

Poor inventory data = missed orders.
Slow updates = lost revenue.
Disconnected systems = frustrated suppliers and customers.

Data quality issues disrupt supply chains and create costly inefficiencies.

Bad data quality in business can often feel like the blind leading the blind. Without accurate data, decisions are based on guesswork. One of the most infamous data quality disasters happened to Nike in the early 2000s, when bad inventory forecasting data led to a $100 million loss in sales—and some very frustrated customers.

Nike had implemented a new demand-planning system to optimise inventory, but poor data accuracy and system errors caused two major issues:

1. Massive overproduction of slow-moving products, leading to excess inventory and markdowns.
2. Severe shortages of in-demand sneakers, meaning popular styles sold out instantly, frustrating customers and retailers.

Nike lost $100 million in sales, suffered brand reputation damage, and had to manually intervene to clean up the inventory mess.

Had Nike used a middleware solution ensuring real-time, accurate data synchronisation, they could have prevented these errors before they became multi-million-dollar problems. Instead, bad data led to a supply chain nightmare that took years to recover from. Middleware prevents these disasters by ensuring data flows accurately across all systems.