In this article we would like to discuss some of the business significance of artificial intelligence or how complex analytics tools help data scientists make sense of large amounts of information whether it is historical, transactional or machine-generated.
When used properly, these algorithms help detect previously undetectable patterns for example customer buying behavior or similarities between allegedly different cyber attacks.
One big problem related to algorithms is their short life span. Besides great math, one also needs to be constantly aware which algorithms are becoming obsolete and replace them with new better ones. This needs to be done continuously and quickly as in today’s complex business environment only the most high-yielding algorithms will survive.
In a hacker attack the defense system is updated to neutralize the threat. But hackers always come back with different approach. In order to stay secure, companies have to develop algorithms at least as quickly as the cybercriminals. On Wall Street even the best algorithms are profitable for 6 weeks at the most. In this period competitors are able to reverse-engineer and exploit it. Algorithmic efficiency is what’s most important in cybersecurity. One well-known case is with a company named Target – it’s systems were able to detect the hack but they had no algorithm for separating the real hacking from unimportant errors as the spewed information was simply too voluminous.
In the financial services stakes are extremely high so algorithmic fraud detection is also highly developed. Knight Capital, an American global financial services company, lost approximately $440 in 45 minutes back in 2012. Needless to say, this company is non-existent, whereas algorithm innovation only becomes larger.
It’s essential to come up with a way of easily compiling data with a sieve for enormous amounts of irrelevant information. As systems present new patterns of failure, one can improve on what was already done with an innovative approach.
Another field that shows rapid algorithmic development is predictive maintenance. Internet of Things is expected to be part of our household items by 2020, but is already seeing industrial use. Algorithms are developed to identify initial signs of system failure and alert the command center.
There are a number of tools that help in the development of algorithms:
- visual analytics – relates to pattern recognition being used in real time to explore enormous amounts of historical data and connect usable models
- streaming analytics – inserting algorithms directly into streaming data in order to monitor it live and isolate patterns or detect potential threats
- predictive analytics networks – a place where data scientists help each other and polish their algorithms with a degree of reciprocity
- continuous streaming data marts – used to monitor an algorithm in real time with the possibility of calibrating it
Top companies are continuously improving on their algorithms. In an environment they created, only the fittest algorithms survive and they will drive smarter and sounder business choices.
