Has your company ever shipped a product to the 99999 zip code?
Of course not. It doesn't exist. In fact, the highest real zip code is 99950, for Ketchikan, Alaska. Still, if you scour your database, there's a good chance you'll find more than a few 99999 zip codes.
Most organizations find that it's been entered as a placeholder in their shipment database. If shipments go as planned, what's the problem? If your strategy calls for automating your processes, you'll encounter serious challenges created by a lack of data accuracy. Let's talk about how data becomes inaccurate and what you can do about it.
Dirty Data Drives Supply Chain Inefficiency
Depending on the solutions in use, a database may fill in 99999 if no zip code is entered, or 99999 may have been entered rather than taking the time to look up the correct number. While a placeholder zip code may not be a fatal problem, it's likely an indicator of deeper issues. That's one reason industry experts estimate that data is faulty in 35 to 40 percent of supply chain systems.
While individual data problems are not good, they are also a symptom of the more significant challenge of potentially suspect data. Without the right numbers as a baseline, it's impossible to make accurate strategic decisions. If you're looking at adding or repositioning distribution centers, rationalizing your product lines, or myriad other initiatives, clean data makes all the difference.
Clean data is also essential for implementing automation, artificial intelligence and other emerging technologies. Poor data quality can lead to problems with carrier compliance, shipment tracking and predictive and prescriptive analytics. As shipments generate more and more data in real-time, quality data is essential. It's also vital for decision-making and sharing with strategic partners to drive benefits across your shipping eco-system.
Solving the Dirty Data Problem
How do you correct the zip code 99999 problem in your company?
The key is to evaluate the integrity of data collection and management programs continuously, not only against your internal requirements but also in relation to external demands. Does your organization have the capability to dig deep into your data collection and management programs, identify challenges and fix them with internal resources? Or will the organizational structures and culture prevent you from making the necessary changes? Third-party analysis may be required to identify the data issues that will derail your competitiveness.
To find out more about ensuring your organization is prepared for next-generation technologies, read our resource guide, AI, Blockchain, Machine Learning: Is Your Data Ready?