Beyond the Shiny Object: Optimizing Your AI Analytics Pipeline with EDA

Feb 13th, 2024

Incorporating systematic exploratory data analysis (EDA) into the upstream data pipeline means weaving data exploration and understanding seamlessly into the early stages of your data processing workflow. It’s not just a one-time analysis but a continuous process embedded within your data infrastructure that lays the groundwork for successful analytics initiatives.

Data science teams often get laser-focused on the “shiny object”: building and optimizing machine learning and AI models. While pushing technical boundaries is essential, it’s only one piece of the analytics puzzle.

As Alexandre Jacquillat, Assistant Professor at MIT, states, “Most analytics projects in practice are focused on the development of deep learning and artificial intelligence tools… This represents only a narrow subset of the full analytics pipeline.”

This narrow focus means that many analytics projects take costly shortcuts in building their data pipelines, shortcuts that may limit the value of the entire analytics program.

Upstream Issues: Garbage In, Garbage Out

Obsessing over model accuracy while ignoring data quality is akin to polishing a rusty car – it may look good on the surface, but it won’t perform well. As Jacquillat emphasizes, “many analytics teams forgo critical steps to ensure the quality of their data, the representativeness of their data, and their own understanding of their data.”

Skipping these upstream steps leads to:

  • Data quality issues: Are you using reliable, accurate data? Without EDA, you might miss biases, inconsistencies, and missing values that skew your entire analysis.
  • Representativeness concerns: Does your data truly reflect the population you’re studying? EDA helps uncover potential biases and ensures your models generalize properly.
  • Lack of data understanding: You built a 90% accurate model, but do you even understand what’s driving those predictions? EDA provides crucial insights into the “why” behind the numbers.

The solution? Implement Systemic EDA

Integrate systematic exploratory data analysis into your pipeline to identify and address data issues before they derail your project.

Systemic Exploratory Data Analysis (EDA) goes beyond traditional, one-off EDA approaches. It’s a structured and continuous process embedded within your data pipeline, ensuring proactive exploration and understanding of your data right from the start.

Follow four steps to systemise EDA:

  • Define goals and needs: What data issues do you want to identify? What insights do you seek?
  • Choose appropriate tools: Explore data profiling, observability, visualization, and automation tools like Precisely’s Data Integrity suite. The Data Integrity suite is a set of interoperable data integrity services that allow customers to swiftly build new data pipelines while identifying, tracking and managing data issues before they negatively impact downstream analytics projects

  • Integrate into your pipeline: Make EDA a seamless part of your data processing workflow with Precisely’s seamless connectivity to both legacy and modern cloud systems
  • Collaborate and iterate: Work with stakeholders to refine your approach and optimize your data understanding.

By incorporating systemic EDA, you gain a deeper understanding of your data, improve data quality, and ultimately build more robust and impactful analytics projects.

Downstream Challenges: From Insights to Impact

Even the most accurate model is useless if its insights don’t translate into actionable decisions. Jacquillat also highlights the need to address “the challenges associated with complex, large-scale decision-making in complex systems.”

Simply presenting a prediction isn’t enough. You need to embed your models into prescriptive analytics pipelines and decision-support systems that guide users towards optimal choices.

Optimizing Your Pipeline for Success

Remember, data science is a marathon, not a sprint. To truly unlock the power of analytics, you need to invest in every stage of the pipeline. Embracing a holistic approach focusing on both upstream and downstream aspects transforms your data science projects from “shiny objects” into engines of real-world impact.

Remember, as Jacquillat concludes, “analytics projects could gain an additional edge by systematically embedding predictive tools into prescriptive analytics pipelines and decision-support systems.”

Contact Master Data Management to move beyond the hype and optimize your entire pipeline for success