Considerations for Applying Machine Learning

Here are five key considerations when evaluating the extent of machine learning to apply for data analysis within an organization:

1. Identify Critical Business Decisions: The first step is identifying high-value business decisions that can benefit from advanced analytics. These include predicting customer churn, forecasting regional demand, optimizing marketing spend, or personalizing recommendations. The business priorities and use cases should drive the data analysis approach rather than a general desire to incorporate machine learning.
2. Assess Data Quality and Volume: Machine learning algorithms perform better with large volumes of high-quality, relevant data. Before training ML models, assess if your data depth is adequate. For example, predicting rare events like fraud requires collecting niche datasets. A rules-based approach may suffice if data volume is low rather than struggling to train ML models.
3. Map Analytics Maturity: Every organization has a different analytics maturity level. Before advanced ML initiatives, those still doing basic reporting may first focus on getting the proper data pipelines, descriptive analytics, data visualization, and business intelligence capabilities. Being realistic about existing infrastructure, skills, and culture will inform the analytics expansion roadmap.
4. Develop In-House ML Expertise: While it is tempting to skip to fancy machine learning capabilities, having the right in-house skills is essential first. Recruiting data scientists and ML engineers and supporting their continual training is vital, even if they eventually rely on cloud services or ML automation platforms for deployment. Without expertise, organizations will not be equipped to frame problems, preprocess data correctly, interpret model outputs, and ensure model fairness.
5. Leverage Cloud and Automation Options: Organizations can accelerate the integration of machine learning without intensive coding and infrastructure investments thanks to mature cloud platforms and ML automation tools like Azure ML Studio, DataRobot, and H2O Driverless AI. Pre-built and automated machine learning solutions have democratized access to advanced ML capabilities across industries. However, they still require foundational data understanding to train, validate the output, and provide adequate guardrails properly.

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.