Wiland Blog

Joe, the EVP of Customer Engagement at Acme Entertainment, Inc.—ranked #19 on the Fortune 500—was riding high. His SVP of customer data had just delivered some exciting news: they had gained a record number of new customers last month. Visions of yachts, luxury cars, and bonus-funded vacations filled his mind.

Then, reality hit.

A revenue report landed on his desk, and something was very wrong—despite the surge in customers, revenue had taken a nosedive. Joe’s eyes darted between the glowing customer acquisition numbers and the bleak revenue figures. Finally, he admitted the painful truth: his data was lying to him.

While Joe’s crisis may be hypothetical, the problem is all too real for businesses everywhere. Companies collect mountains of data to drive key decisions in marketing, customer service, inventory management, and beyond. But what if that data is flawed? What if key details are missing, outdated, or worse—just plain wrong? Extracting insights from data is only valuable if the data itself is reliable.

Bad data leads to bad decisions, and bad decisions cost businesses real money. Like Joe, you could be misled by inaccurate reports, causing you to misallocate resources, miss growth opportunities, or send marketing messages to the wrong audience. This is why deriving insights from data isn’t just about analysis—it’s about ensuring data integrity from the start.

The Key to Good Decisions: Good Data

Poor data quality can manifest in many ways, creating phantom business problems that don’t exist. Whether mismatched customer records, incorrect sales attributions, or inconsistent touchpoints, unreliable data can wreak havoc on strategy. But fear not! You don’t need a massive overhaul to clean up your data. Implementing a strong data strategy ensures that how to derive insights from data doesn’t become an exercise in futility.

Here are five simple yet powerful best practices to keep your data squeaky clean and your insights sharp:

1. Collect Data from as Many Touchpoints as Possible

Cutting down on data isn’t the solution—better data collection is. Engage customers across multiple channels like blogs, mobile apps, loyalty programs, and in-store experiences. The more well-rounded your data sources, the clearer your understanding of your customers will be.

 2. Standardize and Enrich Your Data

When customer data is collected from web forms, call centers, mobile apps, and point-of-sale systems, inconsistencies are inevitable. Implement standardization protocols to ensure uniform formatting, reduce missing data, and validate entries. Off-the-shelf data standardization software can be a lifesaver here—think of it as a grammar checker, but for your data.

3. Validate Data at Every Touchpoint

Standardization is great, but validation is your second line of defense. Even simple measures—like requiring valid email formats or correct zip codes—can prevent a flood of bad data from entering your system. Your future self will thank you.

4. Audit Before You Hash

Many businesses rely on hashed emails, mobile advertising IDs (MAIDs), or customer IDs to protect privacy. While that’s great for security, you can’t audit hashed data. Before encrypting anything, make sure it’s valid—you don’t want a database full of junk you can’t even verify.

5. Consolidate and Integrate

Once your data is clean, bring it all together. Consolidating customer records ensures you aren’t treating the same person as five different leads. Integration connects these records across all platforms, so every department operates from a single, accurate source of truth. This is where extracting insights from data turns into meaningful action.

Take the First Step Toward Better Insights

Before fixing a problem, you need to know where the weak spots are. Audit your current data collection process—where are gaps occurring? Where do inconsistencies creep in? Identify those loose boards in your data fence before they become costly cracks.

The good news? Cleaning up your data doesn’t require a massive budget or a team of data scientists. By tightening up your data strategy, you’ll not only prevent Joe’s unfortunate predicament but also ensure that how to derive insights from data becomes a competitive advantage—not a cautionary tale.