By Asha Deshpande, Vice President of Data Science, Data Axle

Artificial intelligence and machine learning have fundamentally changed the landscape of how data is analyzed and delivered. Through these techniques, we’re able to build scalable, highly predictive models by mining and leveraging an array of data sets simultaneously. In most situations, today’s AI models outperform traditional regression-based models, both in terms of predictive power and speed.

In the marketing world, cutting-edge AI and ML techniques are being used to drive strategic decisions on a daily basis. The deeper and accurate view of data assets that they provide has the power to drive growth and profitability for organizations of all sizes.

However, with this great power comes great responsibility—and that’s a responsibility that’s being neglected within a lot of today’s data-saturated enterprises.

Regardless of the popularity and buzz around any given data science technique du jour, one must resist the urge to use a sword to cut an apple. In other words, not every business problem calls for an AI-driven solution.

A prudent data science leader is one who wields the power of cutting-edge technology judiciously and guides their team on when to use AI and ML and when to instead use other analytical techniques, such as profiles, customer insight analyses, or recency, frequency, and monetary value (RFM) analyses. Ultimately, a company’s business strategy, resources, and goals should dictate the solutions applied to any given problem.

In many organizations, AI and ML are solutions in search of a problem worthy of their talents. Let’s look at two complex data scenarios where data scientists can appropriately wield their swords for the true advancement of their organizations and their business goals.

( Also Read: Difference Between AI and ML )

Parsing Complex Behavioral Data to Reveal Motivations

The analysis of behavioral data and the application of behavioral insights to business problems represent underexplored areas where AI and ML will prove to be game-changers. Today’s marketers struggle with a number of questions revolving around consumer motivations.

For example, why did someone buy a luxury automobile? Why is one consumer an adopter of green technologies while another is not? What motivated a given consumer to switch to a new soda brand after two decades of loyalty to its competitor?

For the most part, consumer action is triggered by underlying motivations rather than surface-level reasons. However, many significant consumer traits—such as buying attitudes, personal values, emotions, and past engagements—represent hidden constructs that often go unnoticed by organizations and lead to a fundamental misunderstanding of why people make the purchases they do.

Data scientists and marketers need to prioritize the measurement and understanding of these factors in order to build more predictive models around lead generation, acquisition, and retention.

Drawing Insights from an Influx of Data

Today’s marketers are awash in untapped behavioral data—but that’s not all. Over the past decade, as more than 27 billion internet of things (IoT) devices have come online, the influx of online and offline data has been staggering.

More than 2.5 quintillion bytes of data are created every single day, and organizations face the formidable challenge of collecting, aggregating, processing, analyzing, and visualizing it in a way that enables them to derive insights and make strategic decisions.

AI and ML can be harnessed to create marketing applications built on platforms with the ability to process volumes of data nearly instantaneously, extract information and actionable insights, and enable companies to refine and plan campaigns around new known dimensions of consumer behavior.

Technology has undergone a number of revolutions over the ages, including the Industrial Revolution and the Digital Revolution. Today, we are witnesses to the next great wave of advancement: the Data Revolution.

In this revolution, the companies that discover the most efficient means of drawing insights from vast data sets—bringing actionability, scale, and visualization to complex scenarios—will be the ones that survive and thrive in this new data-driven reality. AI and ML offer the solutions they need. The key is to apply them to the proper problems.