Data analytics has become a hugely powerful tool for businesses. Here we look at some of the terms that are being thrown around boardrooms the world over.
The rise of big data has combined with advances in computer technology to create a perfect storm in the world of data science. It is impossible to spend more than five minutes in any strategy meeting without phrases such as predictive analytics, machine learning, and artificial intelligence being thrown around like confetti and touted as the “must haves” to gain a strategic advantage over the competition.
Inevitably, there is also a great deal of misinformation, and it is useful to take a step back to understand exactly what some of these terms mean, and just how the tools they describe can add value to your business.
Data analytics refers to the broad-brush range of tools and techniques that are available for the collection and analysis of a business’s data. Today, there is more information available to a business than ever before, and whoever can make the best use of that data is likely to gain a competitive advantage.
For example, social network sites accumulate vast amounts of data relating to user preferences, community interests, and more. They can segment this data according to certain criteria, such as age, gender, or demographics, and use this information to inform their overall strategy in terms of content, layout, and other factors.
This is an advanced branch of data analytics that uses a combination of historical and contemporary data to make predictions about the future, using advanced mathematical modeling and statistical tools.
The applications for this type of information are numerous. From fraud detection in the financial services industry to help doctors identify high-risk patients, it can bring enormous benefits to businesses and to lives.
Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed” – while it sounds very much like a 21st-century discipline, the phrase was originally coined back in 1959 by Arthur Samuel.
A simple example of machine learning is the way a search engine might say “Did you mean…..” if you mistype a word in your search query. The algorithm has seen this mistake before and has learned what you probably meant.
This basic concept of “learning from experience” can be advanced to allow computers to learn almost anything. With all the data that is now available, machine learning makes it possible to perform analysis with a level of speed and complexity that was beyond our imagination just a few years ago.
AI is inextricably linked to machine learning. It can be described as the intelligence that actually allows machine learning to take place. Nidhi Chappell is head of machine learning at Intel. He remarked: “AI is the science and machine learning is the algorithms that make the machines smarter.”
Applications for artificial intelligence are obvious, and examples are around us every day, from Facebook recognizing your mother’s face in a photo and suggesting that you tag her, to your Amazon echo suggesting a nice restaurant for dinner.
It is also only at the very beginning what it can do. There has been a lot of media discussion on self-driving cars, and while they are still seen as a novelty at the moment, their time will come. Today’s science fiction turns into science facts faster than ever before.