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3 Ways Big Data Interpreted by Machine Learning Can Provide Invaluable Insights for More Intelligent Business Strategies

Since the time of the Ancient Greeks, man has yearned for an oracle from which to divine the answers held captive by the future in order to make better decisions in the present. Modern advances in computer science and data analytics, however, have revealed that the key to companies unlocking the best decisions for the future lies embedded in the imprints of the past.

“Big Data” is a term that first entered the fore in the early 2000’s, and it refers to large sets of data that companies acquire through social media, business transactions, purchases, and other avenues. This data can come pre-groomed and organized, or it can manifest in the form of raw, undistilled numbers. Regardless of how it’s presented, trapped within it lie insights, trends, and patterns that can be leveraged for strategic, calculated business decisions that can provide a competitive edge.

While in the past, storing and decrypting the messages entombed within massive troves of data proved to be an insurmountable hurdle, present-day breakthroughs in data storage (Hadoop is one notable example of these) and innovations in the field of machine learning have finally made big data tameable.

Machine learning is a subset of artificial intelligence that focuses on developing softwares that are able to improve their understanding of a target behavior through the integration of acquired data instead of direct programming efforts by a person. In essence, the software is able to “learn,” and get progressively better at accomplishing a given task. This ability to improve results with the more data the software is fed makes for a natural marriage between machine learning softwares and big data.

Corporations can leverage the analytical capabilities of machine learning to for a variety of efficacy-enhancing purposes. Three of the most generative ways in which companies can apply the analytical capabilities of machine learning in order to extrapolate keen business insights from a matrix of numbers are as follows:

A company’s customer base is frequently made up of a plurality of demographics, and for a company to effectively connect with a patchwork of consumers, it must figure out a way to cater to all of their respective needs.

While in the past, companies were forced to lump people into the most relevant demographic cluster and hope that what worked for the goose would work for the gander, the capacities of the partnership between machine learning and big data have evolved to a point of nuanced specificity where they can interpret the data trail left by an individual and devise a marketing plan accordingly. As they, “statistics mean nothing to the individual,” unless, of course, they’re statistics about the individual.

One of the most successful usages of machine learning-driven interpretation of big data to create custom-tailored, micro-marketing campaigns is Google’s algorithm, which creates a user-specific experience driven off of the data gleaned in the wake of one’s past browsing sessions.

By digesting the data trail of customers, machine learning can learn to recognize their habits and provide guidance on how to preempt their behaviors. One example of this was demonstrated by American Express, which used machine learning to predict which behavioral signatures indicated that customers would be closing their accounts within the next four months. By learning to map out behavioral trajectories, American Express was able to better hone its retention efforts, and strengthen its connection with its customer base.

Machine learning can learn to identify the chromosomal makeup of authentic customer behavior, and, like a shark detecting blood in the water, sniff out which numerical anomalies are indicative of fraudulent actions.

Using artificial intelligence, companies can not only automate fraud detection efforts, but rest assured that as more data passes through the pipeline, a more effective model for detecting fraud will naturally evolve. The dynamic, ever-advancing nature of algorithms born from machine learning is what distinguishes them from their manually-created counterparts, and what allows them to morph in lockstep with the kaleidoscopic face of fraud.

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