In this special guest feature, Shawn Masters of Novetta Solutions provides an overview of how machine learning is being used by enterprises to achieve competitive advantage. Shawn Masters is Vice President, Solutions Engineering for Novetta Solutions. He is responsible for driving new ideas and innovation across Novetta’s technical landscape so customers get solutions that meet their needs and grow over time.
The amount of data corporations have to confront is so immense we have yet to settle on an environment-wide euphemism to describe it. Whether it’s big data, data lakes, data tsunamis or even the datapocalypse, the message is clear – we must modify our business practices to navigate and survive the data flood. The bad news is data complexity will only continue to grow. The good news is that technology has provided us with a tool we need to keep our head above water – machine learning.
Today, almost all businesses leverage some type of automation practices – workflows following a set of strict rules. These rules map applications that the machine can perform without the need for human interaction. For example, computers easily perform actions like adding, subtracting, meeting a threshold or matching obvious pairings. All of this automation has increased efficiency and the total volume of data. Simple automation though is no replacement for human thought in finding patterns or making decisions.
Until recently, most enterprises have gotten away with processing a very limited data set and hiring humans to provide intelligent processing and decisions. However, there were many missed opportunities for optimization when using this approach. As data continues to build, the potential savings and increased revenue from processing the remaining data sets, or dark data, has grown exponentially. In fact, the underutilized portion of these remaining data sets is now so large that tapping into its insights could change the landscape of an entire industry. Machine learning is the science that can emulate and augment human judgement and help people achieve new insights into dark data.
Machine learning is the science that creates and utilizes algorithms that can “learn” from data. These algorithms are programmed to draw conclusions from data without strict rules encoded by a human. This simple concept is key – machine learning algorithms take concepts from human interactions and attempt to find the meaning using historical data. Machine learning can replace a person for routine, low skill decision-making, allowing the network analyst to concentrate on bigger concepts or and more complex data sets.
Organizations can realize the benefits from even the simplest implementations of machine learning workflows. Something as easy as recognizing the account holder or the action required allows systems to route, prioritize and pull potential hot data for greater efficiency. Simple techniques like this would allow companies to drive through massive amounts of data in shorter time periods. To add to the value, machine learning algorithms can even conduct triage and handle many tasks almost immediately – allowing employees to focus on more complex and important interactions.
A great example of this can be found in the internal workings of your organization. If you have a fraud department, machine learning could be applied to identify the top 100 potential cases in a regular report for your investigators. Your marketing department could have machine learning systems that follow social media and news feeds to alert your staff to important information before it starts trending. Engineering departments can train systems to look for failures or defects that might reduce yields or reliability, and flag those items for further QA. Logistics planning can use systems that predict shortfalls and excess items in the supply chain based on complex factors outside your control.
As organizations adopt machine-learning techniques, they will see immediate competitive advantages in automation, workflow efficiency and human augmentation. Now is the golden time to consider how machine learning could help your organization and implement this science to improve overall efficiency.
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