Data Gone Bad: How to Minimize Its Risks

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A skull and crossbones is never a good sign—unless you're a pirate.

 

Today's post is inspired by a video I saw about a customer (let's call her Sally) who purchased a beef pie from a bakery that went bad.

Sally took a bite and, to her dismay, discovered creepy crawlies inside.

She was understandably upset, but I think this is a great metaphor for what it is like using outdated data. 

In both cases, you consume something (be it the pie or the dataset) expecting good results, only to find out later that there's a problem.  

Now, just one bad experience could make Sally stop buying from that bakery and lose trust in all store-bought meat pastries.

Similarly, you might stop using a dataset and start relying solely on your gut instinct for decisions.

Wouldn't it be great if pastries could indicate when they're nearing their expiration date, allowing bakery owners to sell them faster or remove them from the shelves altogether?

While that might be ideal, mistakes happen in reality, so the best we can do is minimize the error rate.

With that in mind, here are some proactive steps you can take to deal with outdated data:


Regularly Audit Your Data

 Think of this as checking the bakery's hygiene standards. Regularly review your data to identify and remove anything outdated or irrelevant, ensuring your dataset is fresh and reliable.


Set Data Expiration Policies

Similar to "sell-by" dates on food, establish clear guidelines for how long data should be retained before it's considered expired. This helps you archive or delete outdated information before it leads to bad decisions.


Automate Data Cleaning Processes

Imagine having a team constantly cleaning the bakery kitchen. Automate data cleaning tasks to remove duplicates, fix errors, and update old information, keeping your dataset clean and usable.


Monitor Data Usage

Just like keeping an eye on what ingredients go into your pastries, monitor how data is used within your organization. This ensures you're not basing critical decisions on outdated information.



Resources

Data Wrangling with Trifacta

Data Lineage Tools

Book Rec: Data Quality : The Accuracy Dimension



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