The importance of data cleaning

- Intro

- Benefits of clean data

- Creating high quality data

- Conclusion

One of the most important initiatives for creating a successful artificial intelligence/machine learning (AI/ML) model is ensuring the data you’re using is high quality and clean. That is complete, correct, and relevant to the problem you’re trying to solve. Despite the importance of clean data, it can often be overlooked in model creation due to how tedious and time-consuming it can be to review. According to

, the lack of clean data, or poor quality data, cost US companies $3.1 trillion in 2016. 

Accurate models are only built when using clean data. Using inaccurate data results in models not being built at all, or inaccurate models and predictions being created. Ensuring that data is clean the first time it goes into the model can save time and money. 

Clean data benefits any model it’s being used in. Applying accurate, relevant, and complete data into a model is the first step in ensuring that the model creation is effective. While specific benefits for clean data vary based on the model it’s being used for, there are many universal benefits for most integrations of clean data. 

• More informed decision-making

  : Models using poor quality data either don’t run at all, or produce inaccurate information. The latter is the worst of the two, as faulty models are less likely to be identified and thus will be used for deployment and decision-making. 

Taking the steps to ensure all data being entered is clean gives managers at the company the most accurate information to use when making decisions. 

• Increase productivity

  : Not being diligent when first uploading and using data often means time and money wasted on going back and identifying and fixing errors that otherwise would be prevented. 

Ensuring that data is clean before uploading it to build your model allows developers to focus on the other intricacies of model creation and deployment. Creating a system that demands clean data in models, and identifies how to accomplish that, leads to higher quality and greater quantity of models being produced. 

• Increase revenue

  : Businesses have historically lost time and money when building models that use poor quality data. Not only do data scientists spend more time cleaning up the data after the incorrect model is created, but incorrect models can give false and misleading information to other departments. 

Making sure all models are being used are equipped with clean data results in positive effects across the business. Sales is better able to identify potential leads, marketing can more accurately target prospective customers, and production can better see where the pain points are and make improvements. All these efforts can increase sales and decrease unnecessary spending. 

Poor quality data can be caused by a number of factors. Duplicate and incomplete data both misrepresent the populations or grouping of things being represented by the data. Additionally, invalid data points can lead to a number of problems when creating a model.  

There are a number of elements that contribute to clean data, here are ones we found to be most common to success when ensuring data quality. 

• Validity

  : How valid is the data to the business needs and real world applications. One way to ensure validity is to apply constraints. Here are some examples: 

  • Not Null: Making sure that no columns are empty. 

  • Unique: All values are different from one another. 

  • Primary key: Unique identifier for each row.

  • Foregin key: Links two datasets together, defines the relationship between two dataset tables.

• Accuracy

  : How correct and consistent is the data

• Completeness

  : How close is the data to representing the population, group, or thing it’s trying to define. 

• Consistency

  : Containing only the same type of grouping or numerical values. 

• Uniformity

  : The data has a few of the same characteristics throughout. Such as being measured in the same unit or operating at the same time zone. 

While the process of data cleaning can initially be time-consuming, it can save time in the long run as the data team will spend less time fixing errors. Additionally, using clean data to run your models will make for more informed decision making, saving a great deal of money. Clean data sets the foundation for the success model to be built. 

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