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AI and ML in 2017

This article was written by Carlos Mendoza.


Advancements in machine learning and artificial intelligence (AI) opens new doors for businesses to make data-informed decisions, and if your business does not take advantage of the growing industry of data analytics, your competitors will steamroll ahead.

The advancement of machine learning and artificial intelligence is opening new doors for businesses to make more data-driven decisions at higher accuracy rates. Data and analytics are also changing the way that businesses compete.

As leading companies take advantage of the power of big data, and laggards stay behind, the disparity between the two groups will continue to widen in the future. Recently, Forbes has predicted that by 2020, we will have 1.7 megabytes of new information created every second for every human everywhere on the planet. So what does this mean?

This means that if your business does not take advantage of this growing industry, your competitors may drive ahead as you’re left meandering in not seeing the true value of big data and how it can provide efficiencies for your business.

Machine Learning Defined and Why It Matters:

Machine learning is a method of data analysis that automates analytic model building. With the use of algorithms that iteratively learn from the data it’s being fed, machine learning allows computers to find hidden insights without being programmed to look somewhere specifically. A key strength of machine learning is the seamless ability to recognize patterns in data as well as spotting an inconsistency among that data.

It is advanced statistics, predictive modeling, algorithms, and computer programming combined to make a bot learn from any data that it is fed and make decisions instantly. The key statement here is that machine learning allows computers to find hidden insights without needing a human to point it out (albeit not necessarily with a human removed). With this level of deep insight, trust can be ensured among all levels of decision-makers in a timely manner.

Trusting your Data:

A recent Fast Company article explained how there are a number of executives that aren’t trusting the data that their analytics teams are currently providing. The question lies in where the distrust presents itself, and how an organization is able to overcome this apparent problem. According to the article, within the U.S., the issue may reside in the mismatch of “old-school instincts, which guided executives previously, and new technology.” As executives are looking to make better data-driven and trustworthy decision without the variables of human error, machine learning may be the place to start focusing on.

Hurdles and Breaking Down Barriers:

There are two big hurdles companies face in extracting value from data and analytics. The first is organizational; many companies struggle with implementing data-driven decision making in daily processes. Just a 10% increase in data accessibility will result in more than $65 million additional net income for a typical Fortune 1000 company alone.

The second is talent; data scientists are just the start–having a business expert who can combine data savvy industry with expertise is key. Once these barriers have been broken, companies can expect to see an increase in revenue as a result of their efforts.

Don’t Bite off More Than You Can Chew:

Machine learning and artificial intelligence capabilities may sound like the cure-all to businesses, but there’s far more than meets the eye. Companies looking to leverage the power of machine learning, but don’t have an advanced data analytics process in place, should be careful to bite off more than they can chew. In order for machine learning programs to function successfully, they’ll need a large amount of data and the proper insights to know what information they should be using to build their models. A great way to tackle uncertainties is to examine how other companies are using industry specific variables as a type of strategic benchmark. Right now, there are many companies implementing processes across various industries, with Centric Digital being one of them.

For example, financial services use machine learning technology to identify important insights on data and prevent fraud. The two major use cases in this sector are predicting and preventing banking customers to churn to another bank and fraud prevention. Customer churn is when customers or subscribers stop doing business with a company or service. This is an important KPI within the industry as it is far less expensive to retain existing customers than it is to acquire new ones. NGDATA has found a solution to this problem through the help of big data technologies, “Solve the data management challenges by storing, analyzing and retrieving the massive volume and variety of structured and unstructured data economically on scale elastically as the data grows.” This is important as data creation and analysis across the globe grows at an exponential rate.

Customer churn is not the only problem being reduced by machine learning in the financial services sector. Another large focus area where leaders are breaking down barriers is in fraud prevention. Ravelin is a company showing just how effective machine learning can be in the space. The basic idea lies in spotting irregularities across patterns, as fake transactions have different attributes than legitimate ones. This technology also gives a company like Ravelin the ability to predict such fraudulent transactions to occur before they do. It’s important to note that machine learning is again not a cure-all to detecting fraud. When utilizing such technology in any industry to maintain an accurate reading, the computing model requires a large amount of data to initiate interpretation. If a company does not have the required amount of data, they may not be able to get things off the ground and running in the desired amount of time. As one can see, there are many facets to consider when looking to machine learning to solve data-driven situations and due diligence is a must.

Industries Leveraging Machine Learning:

1. Government

The government has taken interest in machine learning and artificial intelligence in areas such as public safety, utilities, and even within certain military applications. However, most investment has been in using sensor data to identify ways to increase efficiency and save money. In addition, machine learning technology can help prevent identity theft and identify hacks in government systems prematurely. As cyber security has been in the limelight recently, the topic as a whole has been steadily increasing in visibility both among various government agencies as well as companies worldwide. Government focus and investment on cyber security will continue well into 2017, Nextgov predicts, “Machine learning built into core platforms is the next evolution, allowing greater flexibility and better efficiency in threat investigations, risk management, and incident response.”

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