If you want your AI or machine learning project to lead to accurate and adaptable algorithms, it’s imperative to spend time planning and preparing the data at the outset. 

This is not a quick exercise; a recent TechTarget article reported that data preparation takes up 22 percent of data scientists’ time, which is more than they spend on tasks like deploying models, model training, and creating data visualizations. However, the resources allocated to this time-intensive process will quickly prove to have been well worth it once the project has reached completion.. 

With that in mind, the following are six critical steps of the data preparation process that you cannot afford to disregard:

  1. Problem Formation: Before you get to the “data” component of data preparation, take a step back to consider the underlying problem you’re attempting to solve. As part of this, it might be beneficial to connect with stakeholders within the domain that understand the nature of the issue, as that can help inform what data to capture. 
  1. Data Collection and Discovery: Once a team has determined the issue, the next step is to inventory potential data sources—both those within the company and from external third parties. It’s also crucial to be mindful of any factors that may have biased the data, as that can lead to issues with model accuracy. 
  1. Data Exploration: At this stage, it’s important to review the type and distribution of data contained within each variable, the relationships between variables, and how they vary relative to the outcome you’re predicting or hope to achieve. 
  1. Data Cleansing and Validation: Data cleansing and validation procedures can identify and address inconsistencies, outliers, anomalies, missing data, and other issues that could negatively impact model performance and output. 
  1. Data Structuring: Once a data science team has followed the above steps to ensure that their data is in good shape, they must shift gears to focus on the algorithms. Most algorithms work best when data is put into categories rather than left in raw form. Data binning and smoothing continuous features can help structure the model for optimal performance, as they prevent it from being misled by minor statistical fluctuations in the data set. 
  1. Feature Engineering and Selection: Feature engineering comprises numerous activities, for example extracting variables from a data set, decomposing variables into separate features, aggregating variables, and transforming features based on probability distributions. Feature selection is also important, as a feature could look promising but lead to problems that might ultimately prevent the model from accurately analyzing new data.

Take a look at the complete TechTarget article for more on these considerations and why they’re essential for obtaining trustworthy analytics.