Data analysis has become an extremely powerful tool for companies. Here we look at some of the terms that are being launched around meeting rooms all over the world.
The increase in important 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 a strategy meeting without sentences such as predictive analysis, machine learning and artificial intelligence thrown like confetti and claimed as “must haves” to have a strategic advantage over Competition.
Inevitably, there is also a great misinformation, and it is helpful to step back to understand exactly what some of these terms mean, and how the tools they describe can add value to your business.
Data analysis refers to the wide range of tools and techniques available for collecting and analyzing company data. Today there is more information available to a company than ever before, and anyone who can make the most of this data is likely to gain a competitive edge.
For example, social networking sites accumulate vast amounts of data about user preferences, community interests and more. They can segment these 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 data analysis branch that uses a combination of historical and contemporary data to make predictions about the future, using advanced mathematical models and statistical tools.
Applications for this type of information are numerous. From fraud detection in the financial services sector to help physicians identify high-risk patients, this can bring enormous benefits to businesses and lives.
Stanford University defines machine-based learning as “the science of operating computers without being programmed explicitly” – while it is very much like a discipline of the 21st century, the phrase was originally invented in 1959 By Arthur Samuel.
A simple example of machine learning is how a search engine could say “Did you mean … ..” if you have a password in your search query. The algorithm has already seen this error and learned what you probably meant.
This basic concept of “learning the experience” can be advanced to allow computers to learn almost anything. With all the data available, machine learning allows us to carry out analyzes with a level of speed and complexity beyond our imagination a few years ago.
AI is inextricably linked to machine learning. It can be described as the intelligence that actually allows machine learning. Nidhi Chappell is responsible for machine learning at Intel. He remarked: “AI is science and machine learning, it is the algorithms that make machines more intelligent.”
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 label it, to your Amazonian echo suggesting a good restaurant for the having dinner.
It is only at the beginning of what he can do. There has been a lot of talk about self-driving cars, and while they are still considered a novelty at the moment, their time will come. Today’s science fiction is turning into a scientific reality faster than ever before.