Before you get started with a data-cleansing project, you have to be sure of what you want to accomplish and how you will accomplish it. Keeping these best practices in mind will give you a good conceptual footing.
Take a holistic view of your data. Think about who will be using the results from the data, not just the person doing the analysis.
Increase controls on inputs. Make sure that only the cleanest data is used in the system.
Identify and resolve bad data. Stop bad data before it becomes problematic by choosing software that gives you that capability.
Limit the sample size. With large datasets, large samples unnecessarily increase prep time and slow performance.
Run spot checks. Catch errors before they can be replicated throughout the data.
This is another list of best practices, but this one is a step-by-step checklist for your cleansing project.
- Set up a quality plan before you begin
- Fill out missing values
- Remove rows with missing values
- Fix errors in the structure
- Reduce data for proper data handling
Let AI Do the Hard Work
Most large data cleansing projects are now performed with Artificial Intelligence — or, to be more precise, Machine Learning. Here’s why.
The amount of data that organizations collect increases significantly every year. When cleansing data without the aid of Machine Learning, whenever bad data is found the data scientist or data analyst has to write a new rule to tell the system what to do with it. Because new data (including bad data) enters the system at an increasingly rapid rate, the data scientist is constantly playing catch up. Consider the scope of the problem. Data creates patterns. Bad data is found by recognizing anomalies in the patterns. The more data there is, the more complex the patterns become, making them more difficult for humans to analyze. That means some anomalies are not found unless the data scientist spends a massive amount of time looking for them among increasingly complex patterns.
Every minute the data scientist spends cleaning up data is a minute they are not using the data to make the organization more productive. And if they can’t catch up with the bad data, they are effectively caught in a trap like a hamster on a wheel. This is not as uncommon a problem as you might think.
Machine Learning frees the data scientist from the trap. The data scientist creates a learning model to predict matches. As more data enters the system, the more Machine Learning fine tunes the model.
With manual data cleaning using standard computer programs, as more data is added, the worse the problem becomes. With Machine Learning, as more data is added, the more the problem is eliminated. The key is to focus machine learning on systematically improving how it analyzes, rates and utilizes data so that it becomes the expert at correcting, updating, repairing, and improving data.
That puts the data scientist back to doing what they were hired to do.