Why You Sometimes Get More Value from Preparing Data than from the Analytics Itself
Often, the insights gained from preparing data for analysis can rival or even exceed the insights generated by the models themselves. As data scientists and data engineers delve into cleaning, organizing, and transforming data, they uncover information that informs not only the analytics process but broader business processes as well. This post explores the lessons learned during data preparation and why this step is invaluable.
What Data Preparation Teaches Us
Here are nine areas where we can learn things from the data we are preparing before we even begin the modeling. The data scientists on your team can surely list more, and provide specific examples relative to your context.
- Understanding Data Flaws and Gaps
Data preparation is like shining a spotlight on the imperfections in a dataset. Flaws such as inconsistent formats, duplications, and errors come to light, prompting questions about the processes generating the data. Gaps in data—such as missing records—often reveal weaknesses in data collection or storage systems that need to be addressed before analytics can be trusted. - Detecting Anomalies and Outliers
Anomalies can tell fascinating stories. While some outliers signal data-entry errors, others may uncover rare but important events. For example, a spike in sales data could indicate an unrecorded promotion, while an outlier in sensor data might point to a potential malfunction. - Discovering Patterns of Missingness
Missing data often is not random—it often reflects systemic issues. Patterns of missingness can point to breakdowns in data collection workflows or highlight areas where external factors influence data availability, such as supply chain disruptions or customer behavior shifts. - Gaining Insights from Data Content
By deeply examining the content of data, analysts often identify hidden relationships or surprising trends. For example, looking at the text of customer reviews might reveal unanticipated product preferences, or studying transactional data could uncover hidden customer segments. - Understanding the Context of Data
Data preparation forces analysts to ask fundamental questions: Where does the data come from? What processes generated it? Why does it matter? Answering these questions builds a richer understanding of the data’s context, making the subsequent analytics more meaningful and actionable. - Spotting Opportunities for Data Enrichment
Gaps and inconsistencies often highlight opportunities to enhance datasets. Data preparation can inspire decisions to integrate external data sources, such as demographic information, economic indicators, or competitive benchmarks, to create a fuller picture. - Revealing Operational Challenges
Data issues often reflect operational inefficiencies. For instance, delays in data availability might signal bottlenecks in workflows, while discrepancies in financial data might uncover issues in billing or accounting processes. - Sharpening Domain Knowledge
Preparing data requires collaboration with domain experts, which deepens the analyst’s understanding of the industry or business context. This knowledge is helpful for designing better models and interpreting results accurately. - Highlighting Business Opportunities
Through the process of cleaning and exploring data, analysts often stumble upon unexpected opportunities. For example, tracking data quality over time could reveal trends that point to new operational improvements.
Why These Lessons Matter
These insights don’t just make for cleaner data—they inform business strategies, drive operational improvements, and enhance the overall quality of analytics projects. When organizations invest in rigorous data preparation, they’re not only paving the way for better analytics but also unlocking value that exists in the raw data itself.
Conclusion
Preparing data isn’t just a precursor to analytics; it’s a powerful exercise in discovery. The lessons learned during this phase—from uncovering flaws to understanding the broader context—create a foundation for informed decision-making. By embracing the insights gained from data preparation, organizations can unlock hidden opportunities and set the stage for transformative analytics.
It is said that data preparation is the menial labor of knowledge workers. We know now that it is the essential labor.
Disclosure: nearly all of this post was written by an AI, with human prompting end editing. Check out our generative AI disclaimer.