Data Integration Buyer's Guide

Three High-Impact Data Preparation Best Practices

Three High-Impact Data Preparation Best Practices

Three High-Impact Data Preparation Best Practices

Data preparation involves sorting, cleaning and consolidating data into one store for analysis. The process for doing this generally involves correcting errors, filling in incomplete data, and uniting data from multiple source locations. Data preparation is a pre-processing step that allows for the transformation of data before analysis to ensure quality and consistency, providing enterprises with maximum potential for business intelligence. Given the growing volumes and velocity of big data, data integration acts as a significant barrier to the overall data preparation scheme. From a tactical perspective, generating data quality too remains a challenge.

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Here are three high-value best practices to help your organization fine-tune its data preparation techniques:

Understand your data types and formats

Data comes in an infinite number of shapes and sizes these days, so facing what seems to be an overwhelming amount of data is the new norm. Data that comes from disparate sources must first be analyzed before data preparation can be done. This is so the data worker can ensure the data can be read, an especially important factor when working with unstructured data sources.

Include your outliers

Outliers are data files that don’t match up with the majority of the data. These can throw data models out of whack if not dealt with properly. When running reports, an outlier can mean the difference between generating insight and nothing at all. Most data analysts simply delete these files. However, we recommend utilizing them in a more wide-angle methodology. Running analysis on data twice can yield more actionable results, once with the outliers included and once without them. Once data preparation is complete, this allows you to evaluate which analysis moved the needle.

Verify accuracy

Verifying the accuracy of the data does several key things. First, it allows the data worker to predict what properties the prepared data should exhibit to see if the process was run correctly. Second, it provides a concrete explanation as to whether or not the data is what it originally represented. If the properties of the data hold up, then there is a high likelihood that the data is quality. If not, then it’s time to go back to the drawing board. It’s best to have someone other than the data analyst run through the accuracy check, as someone with knowledge of the subject area should be able to verify the results.

Bottom Line

Data preparation tools can be used to harmonize, enrich and standardize data in scenarios where multiple values are used in a data set. Proper formatting is essential for analysis, so preparation is needed during the integration phase of a project. This is especially important if data is being integrated from unstructured sources, such as a data lake. High data quality is essential for impactful analysis. No matter the use case, turning bulk data into an actionable business asset is a critical step in generating knowledge.

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