Best Practices for Applying ML in Data Analysis

While you take a cloister look at the above considerations, there are also certain best practices that guide you at the time of applying ML in data analysis. Let’s dive deep and look at these best practices in detail:

1. Start with a Relevant ML Proof-of-Concept: Rather than overhauling entire analytics processes with ML, identify a well-defined business problem and run an 8-12 week proof-of-concept project to test capabilities. Focus the project on a single use case with a committed business team rather than a technical exercise. Learnings from this project can inform your broader ML adoption.
2. Build Hybrid Analytic Approaches: The most valuable insights often come from blending different analytic techniques, e.g., using machine learning for predictions while applying business rules to eliminate specific recommendation scenarios. As there is rarely a single source of truth, aim to leverage both ML and non-ML-based techniques.
3. Apply Guardrails to ML Outputs: Monitoring machine learning models to catch unexpected errors or bias issues stemming from insufficient data is essential. Establish human review processes, test ML outputs versus benchmarks, document model limitations, build cross-checks with other models, implement fairness constraints, and continually enhance the model’s accuracy.
4. Focus on “Good Enough” Over Perfection: There is no perfect machine learning model, as new data introduces entropy over time. Rather than over-optimizing for theoretical accuracy or wasting resources on incremental gains, focus on reaching the model effectiveness needed to address the business problem. Then, monitor and update as necessary.
5. Build Trust & Understanding: Adoption will be negatively impacted regardless of accuracy levels if business leaders do not adequately understand ML outputs or lack trust in the technology. Fostering stakeholder education, openly discussing risks, and encouraging feedback loops are critical for driving engagement. Leadership buy-in stems from their comfort with deploying analytics.
6. Embed ML Engineering: To properly monitor models and ensure optimal integration with applications, have machine learning engineers partner closely with software engineering teams rather than operating in a silo. DevOps approaches applying version control, collaboration rituals, and continuous delivery pipelines lead to better ML applications.
7. Enrich Data Over Time: Each model is equal to the data used to train it. Even with advanced machine learning, more data can be needed to improve model accuracy. Develop a data governance strategy for capturing, connecting, cleansing, and enhancing varied datasets over time to drive improved insights.

Data analysis has become a critical component of decision-making across industries. With the exponential growth of data, businesses are increasingly looking for valuable insights to stay competitive. Here’s where machine learning comes in. Machine learning provides advanced analytical capabilities to uncover patterns, make predictions, and optimize processes based on historical data. However, an essential question organizations face is: how much machine learning capability is needed for impactful data analysis?

In this article we will explore the considerations around finding the right balance of machine learning for optimizing data analysis.