Data Preparation

Raw data is generally not up to the standard required for data analysis—meaning that it has to be prepared before entering the processing stage. 

For example, one may have to standardize the data formats, enrich the existing source data by combining datasets, or remove outliers (such as results that are so far detached from the rest of the data points that they are not considered to be useful/relevant). 

Data preparation can be a long process, but it is valuable, and important to get it right. Effective preparation powers successful analytics projects. It helps to spot any potential errors before the processing stage, and ensures the data is high-quality and correct. Using high-quality data means that models will produce more accurate results which fuel better business decisions. 

The data preparation process differs from organization to organization, but in general, it might include the following steps:

• Gather the data: Identify the data needed and the sources to acquire it, and then gather the relevant data points accordingly. 

• Discovery: Analyze the data to detect relevant patterns that might be of interest to the specific use case/business problem. 

• Clean and validate: Begin to remove any outliers, fill in missing values, make sure that the data conforms to a standardized pattern, or remove any data points that contain sensitive/private information. 

• Store: Store the data in a relevant third-party application, such as your company’s business intelligence tool, before conducting the necessary processing and analysis. 

Prioritizing data preparation means minimizing the risks of encountering issues further down the line. It will speed up your analytics projects and increase ROI. 

Additional Resources:

• Rethinking Data Acquisition with Explorium
• Explorium 2021 State of External Data Acquisition (Report) 
• 10 Questions to Ask Before Buying External Data
• How to approach data preparation using python
• 4 ways to know if you are using the right data preparation tools