Before building a model, an investigation of the data at hand is not only an ideal practice but a crucial phase of the data science lifecycle. A thorough, comprehensive understanding of your dataset(s) from the get-go is an absolute must for ensuring accurate analysis and insights further down the line.
This initial data inspection, which is a part of the data preparation process, reveals where clean-up is needed and sets the key foundation for the next steps, including gathering business insights from the dataset(s).
What's So Important About Exploratory Data Analysis?
Exploratory data analysis (also known as EDA) is dedicated to uncovering patterns and features within your dataset, identifying issues, forming hypotheses surrounding data questions, and creating an analytics demonstration of the findings.
The best way to understand exploratory data analysis is to jump right into why it’s necessary. One of the primary purposes of exploratory data analysis is to identify issues early on to ensure that the data going into machine learning (ML) models is both accurate and consistent and will provide valid, unbiased results.
Exploratory data analysis is very important for ML models. Picking up on affectations within the data is critical so that you can improve data quality prior to model training and deployment. Remember: data quality directly impacts model accuracy and robustness.
By instilling an efficient and repeatable exploratory analysis during data preparation, organizations also enable analysts to accelerate the time-consuming and repetitive components of the data lifecycle for future models. This frees up time for more in-depth, goal-specific analytics which, in turn, drives more efficient decision-making processes.
Mitigating Issues and Introducing Transparency
A key principle behind data preparation, in general, is “garbage in, garbage out” — if the data is flawed going into an ML process it is bound to generate flawed results, algorithms and, worse, business decisions. Spending enough time analyzing, exploring, and cleaning data at this stage does not just mean better results, it also helps organizations avoid serious issues (e.g., using inherently biased or problematic data that can easily lead to biased or otherwise problematic models).
Error is only natural, so catching it sooner rather than later is the best way to mitigate it, and having a systematic way to explore data on the front end makes it easier to pinpoint the risk-prone, sensitive areas. Analysts (and data scientists for that matter) should always keep tabs for explainability. By checking in on the data at the beginning, transparency throughout the entire data process becomes possible.
Essentially, organizations cannot effectively make decisions, meet important business needs, or survive without exploratory data analysis and adequate data preparation. Key parts of exploratory data analysis like discovering important patterns and features within your dataset and forming hypotheses about your defined problem through visual analysis or statistical modeling of the data (also known as data mining) do not need to be siloed projects either.
Thanks to collaborative platforms like Dataiku, non-coders can explore and transform their data using visual tools and easily share insights with technical teams who will build out the models. The centralized platform makes each stage of the process, including the notably important exploratory data analysis, easily accessible and shareable.