The Hidden Cost of Bad Data: How Retailers Can Safeguard Their Bottom Line

In today's fast-paced retail environment, data is the lifeblood of successful operations. Key functions like competitive analysis, pricing strategies, product decisions, and understanding customer trends all depend on having lots of relevant data that’s clean and current. However, not all data is created equal, and processing bad data is often worse than processing no data at all. Doing so leads to misguided decisions and costly mistakes. After all, if you put garbage data in, you get garbage insights out, and that leads to garbage decisions that produce garbage results.

The price of bad data may be higher than you expect. According to a MIT Sloan study, bad data costs most companies anywhere from 15 percent to 25 percent of their revenue. This staggering figure underscores the critical importance of data quality in retail operations because the consequences of processing bad data extend well beyond immediate financial losses. Processing bad data wastes bandwidth, storage, and compute, spending tons of money on IT resources for no gain at all.

Consider, for example, the implications of inaccurate pricing data. Retailers may inadvertently leave money on the table by underpricing products or, conversely, they may dissuade shoppers from purchasing products by pricing them too high. That sweet spot in the middle is elusive; identifying it accurately requires timely, relevant data. Similarly, flawed data can lead to the selection of products that don't appeal to consumers, resulting in unsold inventory and missed opportunities for better-performing items.

The Crucial Role of Timeliness

For retailers, fresh data is as important to the bottom line as fresh produce is to a grocer. Just as lettuce looks unappetizing after a week in the cooler, old data — even just week-old data — can often be synonymous with bad data, especially in dynamic markets where prices can change daily or even hourly. Additionally, retailers combating fraud, such as pop-up stores on e-commerce platforms that violate manufacturers' pricing agreements, need timely data to catch offenders before they disappear and resurface under new identities.

While batched data can suffice if it's very recent (daily or hourly), streamed data offers the most up-to-date picture of the market. Retailers must be wary of third-party data collectors that may hold data and send it in batches to reduce their own costs. To ensure they're working with truly current information, retailers need to verify that their data partners are providing the most recent data available.

The Devil is in the Details: Metadata Matters

Beyond the data itself, retailers must pay close attention to metadata — i.e., the information that provides context and structure to raw data. Incorrect data associations can transform good data into bad data, leading to misinterpretations and flawed decisions.

Geographic data serves as a good example of the importance of accurate metadata. Retailers shouldn't rely on shortcuts or assume that geotagged data is always correct. Instead, they should ensure that data is associated with specific ZIP codes to ensure accuracy. Even artificial intelligence systems can make mistakes, such as tagging Astoria, Oregon as Astoria, New York. In extreme cases, such errors could lead to pricing and product specifications being applied to areas where those products aren't even available.

Safeguarding Your Bottom Line: Best Practices for Data Management

To protect against the pitfalls of bad data, retailers should implement two key strategies:

1. Partner with a trusted data provider that is transparent about its sources. This transparency allows retailers to assess the quality and reliability of the data they're receiving.
2. Process data with a dedicated internal team. This team should understand the data model thoroughly, analyze data quality consistently, and identify issues promptly so they can be addressed.

By processing data internally, retailers maintain control over their data quality and can quickly adapt to any issues that arise. This approach also enables a deeper understanding of the data's nuances and limitations, leading to more informed decision-making.

Turning Data Into Dollars

In an era when data drives retail success, the quality of that data can make or break a company's bottom line. Bad data is more than just a nuisance; it's a silent profit killer, costing retailers money, time and missed opportunities.

By prioritizing data quality, working with transparent data providers, and processing data internally with a dedicated team, retailers can turn the tide. These practices not only safeguard against the pitfalls of bad data but also unlock the true potential of data-driven decision-making. In doing so, retailers can increase revenues, boost profits, and enhance customer satisfaction, transforming data from a potential liability into a powerful asset for growth and success in today's competitive retail landscape.

Rochelle Thielen is CEO of Traject Data, a company that provides clean and comprehensive API data to enterprises that helps them make informed, critical decisions.