Machine Learning vs. Traditional Analytics: When to Use Which?

This article focuses on demystifying the difference between traditional data analytics methods vs. machine-learning-driven ones, not without providing firstly a clear understanding of what is — and what is not — data analytics compared to other data terms often used interchangeably. After gaining such understanding, the post provides clear and succinct guidelines on when to use data analytics approaches guided by machine learning modeling vs. using more traditional approaches inherited from statistics.

What Exactly is Data Analytics vs. Other “Data” Fields?

Let’s be clear about one thing: there is a lot of confusion between data analytics and other related data fields like data science, big data, business intelligence, and even data analysis (yes, data analytics ≠ data analysis!). So, before jumping into the core question raised in this post, it is convenient to clarify what data analytics is compared to other data terms.

• Data Analysis: it focuses on examining data to identify trends, patterns, and relationships, typically using a blend of statistical and visualization methods. Traditional hypothesis tests, regression analysis, variance analysis, and time series analysis techniques fall under the umbrella of data analysis.
• Data Analytics: it concentrates on predictive, descriptive, and prescriptive analysis, i.e. discovering future patterns and trends to support business decision-making. In essence, data analytics is a subtle contextualization and more domain-specific conceptualization of data analysis: the difference is purely semantic and application context-oriented, but underlying methods are nearly identical in both fields. Customer segmentation and customer retention analysis in marketing and retail can be categorized within the scope of data analytics processes.
• Data Science: it focuses on the study and engineering processes of complex data to gain deep insights and knowledge. It often entails the use of advanced algorithms and modeling techniques under the machine learning umbrella, as well as cleaning, warehousing, and presenting data.
• Big Data: this term refers to the process of managing and analyzing huge and complex datasets that cannot be effectively dealt with by using traditional data processing approaches. It largely focuses on the use of hardware and distributing computing technologies (thanks to tools like Hadoop and Spark) for efficient, high-performance data handling at scale.
• Business Intelligence: it entails the triple process of collecting, analyzing — usually from a descriptive vantage point — and visually presenting data in organizations for supporting decisions. Whilst very similar to data analytics, business intelligence puts more focus on the creation of reports and interactive dashboards to provide relevant information to decision-makers. Power BI, Tableau, and QlikView are well-known BI tools on the market.

Hopefully, the above list dispelled part of the doubts you might have encountered about these closely interrelated areas. If not, let the below diagram do the job

Data Analytics and Machine Learning: When to Use Which?

Now we are in a better position for comparing data analytics and machine learning. Machine learning (ML) is a subarea of artificial intelligence, whereby software models fueled by data and capable of learning by themselves to perform a task are built. ML models perform tasks like classifications, regression, clustering, and so on, by being exposed to data used for learning, a.k.a. training data.

ML can sometimes be used as a data analytics tool by businesses in certain use cases, typically of a forecasting nature: predicting sales trends, customer churn, or detecting fraud. In these scenarios, ML models for classification, regression, and anomaly detection, among others, can constitute powerful data analytic tools. Remember, the notion of data analytics is not determined by the techniques used -whether ML or not- but by the conjunction of data analysis approaches plus an application contextualization of business decision-making support.

This means many modern data analytics processes and methodologies make use of ML techniques as part of them. But not all. ML is not always the go-to approach for data analytics in business settings. And this is where the initial question that gave a title to this article arises: when to use which? Now that we have a solid understanding of these two terms and other very interrelated ones, we are in the ideal position to answer the question and wrap up the article.

 

When to Use Machine Learning

The use of ML for analytics purposes is encouraged when:

• There is a need to make predictions or automate decisions from large and complex datasets: scenarios include customer segmentation upon complex customer behavior data, recommender systems that predict and suggest potentially needed or liked products to a customer based on analyzing their shopping habits, and so on.
• Patterns to be found underneath the data are too intricate for manual analysis: this is especially the case in tasks involving unstructured data, like image recognition and classification, and natural language processing e.g. to categorizing the sentiment in customer reviews.

When to Use Traditional Analytics

On the other hand, the use of traditional analytics methods like statistics-based ones is a better option when:

• There is the goal of understanding historical data, identifying trends, or testing hypotheses using approaches like variance analysis and regression analysis.
• Working with smaller and simpler datasets where the focus is on explaining relationships and correlations between data variables clearly and interpretably, e.g. correlations between products sold, sales trends, etc.