Advanced analytics isn’t optional anymore. It’s a necessity for your business to be able to gain those deep insights that drive better decisions. And, as is true with any technology, advancements are coming at a rapid pace. However, not all of those advancements will be right for your business. One safe bet, though, is an investment in machine learning technology.
 
Machine learning (ML) is broadly defined as the use of statistical algorithms to enable information to mimic human learning. With ML, computers can examine their past experience, parse the data from it, and learn from the experience, on their own. If you make or sell products or services, or anything in between, you need ML technology to augment your analytics.

Customer Segmentation and Event Analysis

 Whether they’re consumers or other businesses, you have customers, and you need to know more about their behavior. You can use clustering algorithms to group data set members with common traits together. For example, you can segment customers with similar buying habits or demographics.
 
Insurance companies can use ML to analyze population and epidemiological data and build a model of people who are likely to get cancer, heart disease, or diabetes. ML can also be used to build predictive models of customer segments that are likely to churn, demand forecasts, project outcomes, financial performance—the list goes on and on.

Data Integration

It’s been estimated that 65% of all the data you’ll need to integrate in any current IT project you’re undertaking has already been dealt with in your last four or five projects. The catch is finding out which data that is. Machine learning can help with this hunt. Project teams can apply ML algorithms to analyze descriptive, structural, and administrative metadata to gain a clear picture of how data is used and organized throughout the various organizational systems it inhabits.
 
Using that information, ML algorithms learn about data structures, flows, and needs and can use that information to automate data integration tasks such as:

  • Deducing appropriate data schemas and structures
  • Cataloging data used across applications—both repetitive and unique elements
  • Recommending transformation tasks
  • Mapping metadata elements between applications

By automating all these tasks and learning from the process, you can shorten the time it takes to integrate your data into future new systems—saving both money and valuable resource time. That’s a big win.

One safe bet is an investment in machine learning technology.

Anomaly Detection

This is where you look for the weird. This type of ML, called anomaly detection, can help you ferret out strange events that occur in large, complex data sets—especially transaction data sets. You use data sets with specific parameters and known outcomes to train the ML algorithm to recognize the way things should be, then feed it large, controlled data sets to analyze. The algorithm marks any events that violate the parameters or that are outside known outcomes.
 
A few of the areas in which this type of ML can really make a difference include cyber-security, banking security and fraud management, medicine (think cancer clusters and epidemiology), and marketing. In short, anomaly detection can be used in any application where you need to track abnormal events and figure out which of those matter and which won’t really affect outcomes. What’s more, the algorithms get smarter over time, so they can discover more and more anomalies and fine-tune your monitoring initiatives.

Meeting your Goals

You might have heard of this ML application if you’ve read about computers that have trained to beat opponents at games like go, Atari, and chess. With these algorithms, you apply observations and measurements to a prescribed set of actions in the process of trying to achieve and optimize a goal. The computer interacts with its environment in an attempt to learn how to master it.
 
All the outcomes aren’t known in advance, but desired ones are rewarded. This use of ML can be applied to all sorts of business activities such as risk management, inventory management, logistics, and product design. The list is huge. The bottom line is that using ML for these activities can help you discover optimal outcomes that you seek, and it can reveal outcomes that you didn’t seek, but that you can leverage to optimize your operations.

Just Do It

I don’t doubt that you’ve spent good money on—and gotten good results with—your analytics initiatives. Why not make them better? Use ML to augment the good things you already do and get one step closer to realizing the promise and transformative value of analytics that all those pundits talk about. It’s within your grasp; just do it.

Anu Jain
Anu Jain, Vice President, Americas, is at the forefront of the analytics, machine learning, and workflow orchestration revolution. Anu is a leader in Teradata’s transformation from a perpetual license model to a service organization that will drive innovation in open source, business solutions adoption, analytics, and workflow. He has deep technology and domain-specific thought-leadership and expertise in ad tech, media, front-office effectiveness, digital media and analytics-powered industry solutions. His expertise in technology-driven business transformation includes big data, cognitive analytics, predictive analytics, data mining, data warehousing, and business intelligence. Before coming to Teradata, Anu worked for IBM and Deloitte Consulting.

Anu also frequently blogs on his personal site: https://thinking-analytics.com/
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