Whether you acknowledge it or not, analytics has changed your life in every way there is. When you wake up in the morning and start scrolling down on your Facebook homepage, it is an analytics technology that analyses your past behaviours to predict what you may be interested in today.
When you want to buy something online, an analytics technology has helped the store determine what kind of products you would be interested in, how they need to be placed on the website to look attractive, and maybe even the kind of font that is needed for the description of the product.
There’s almost no part of one’s life that has not been touched by analytics in one way or another – so it is important to know how analytics are used by various companies and how you can consciously embrace it as part of your career and, ultimately, as part of your everyday life.
How to do that?
Read on and find out more.
What Is Analytics Technology?
Analytics, as a technology, is not as new as many would be tempted to believe. Some may associate it with the post-90s wave of tech (mostly intertwined with the rise of the internet). However, the first analytics software applications were created much earlier.
You might be shocked to believe that analytics, as a science, has been used in business starting with the 19th century. However, it was only in the 1970s that the first analytics software was created.
Today, analytics is used in a very wide range of industries: marketing, business, movies, and even medicine.
Put in very simple terms, analytics is the collection and analysis of data to provide meaningful insight into the field the data pertains to.
For instance, Google Analytics is a free tool created by Google to allow webmasters, developers, and marketers to have access to a treasure trove of data that will allow them to make better decisions for the business, for the website, for the content posted on it, and so on.
How Is Analytics Used?
There are different types of analytics out there, but the four main categories are the following:
- Diagnostic. This type of data will help you determine what the main problem is in a given situation. For instance, an eCommerce’s diagnostic analytics will help you determine why the sales dropped in a specific month.
- Descriptive. This type of data will help you understand what happened in a given situation. For instance, an eCommerce’s descriptive analytics will tell you how many sales were recorded in a month.
- Prescriptive. This type of data will help you determine what action to take in the future. For instance, an eCommerce’s prescriptive analytics will tell the marketing, sales, and managerial team what actions to take in the future to boost the number of sales.
- Predictive. This type of data will help you predict what happens next. For instance, an eCommerce’s predictive analytics will help you predict (with a margin of error) how many sales you will record over a specific period of time (e.g., the time between Thanksgiving and Christmas, for example).
Every type of analytics works differently, but at their very foundation, they all use the same pattern: data collection and data analysis. Obviously, descriptive analytics tools tend to be more simplistic than predictive tools but overall, they all follow the same ground rules.
Famous Examples of Analytics in Use
Analytics tools are far more common than most people believe, and they are used in a very wide range of industries and situations.
Movies and Entertainment
For instance, Disney uses analytics and Artificial Intelligence to determine if their movies will be successful. At first, they collected a large chunk of facial expressions recorded during the screening of multiple movies. Then, they fed all that information into an Artificial Intelligence system they built. Over time, the AI got so good at predicting whether or not a movie will be successful that it now takes ten minutes for it to see what the reaction of the audience will be. [1]
In a similar manner, Netflix uses their analytics to better understand its viewers’ behaviours and how to create shows that are equally appreciated (and addictive). [2]
Science and Knowledge
IBM Watson is, perhaps, one of the single most famous examples in the use of analytics. IBM has a long-standing history of building computers capable of running advanced analytics. One of their first such computers was Deep Blue, a machine capable of successfully competing against some of the world’s best chess players. [3]
Blue Gene is another IBM super-computer that was able to decode the human genome in a time frame much shorter than entire teams of human specialists could.[4]
IBM Watson is one of the more recent examples. This particular supercomputer was initially built to be able to successfully compete in Jeopardy! The entire experiment was considered to be very complicated because this particular game show is based on new questions and quizzes that are meant to test human intelligence, cleverness, and reasoning. [5]
With no less than 3,000 microprocessors and with a database worth 200 million pages of information, Watson was able to actually compete in Jeopardy! To find the best answers, the supercomputer used more than 100 analytics models and a very wide range of types of sources that had been fed into it (including manuals, encyclopedias, and so on).
Watson won against the two most successful human Jeopardy! players of all time. These days, this supercomputer is used in a very diverse array of applications, including (but not only), selecting the best course of treatment in the case of cancer patients.
Matchmaking
Yes, you read that right.
Analytics can be used in relationship matchmaking as well. [6]
eHarmony is famous for the way they used analytics to predict whether or not a couple will match. To do this, they ask all members of the site to answer 436 questions, which are then analyzed against 26 dimensions of the users’ personalities. Once this process is done, eHarmony finds matches for everyone on their site.
Studies show that less than 4% of the marriages in America that are the result of an eHarmony match will end in divorce. By comparison, the national divorce rate is approximately 50%. [7]
How to Bring Analytics into Your Own Life
Like it or not, analytics, machine learning, and AI are already part of your life. You may not be the happy owner of a robot who cooks dinner for you every night (yet), but even so, these emerging technologies play a pretty big part in your everyday life. Their importance will only continue to grow over the following years and decades.
How to consciously bring analytics into your own life to make it better on a professional and personal level?
The most obvious answer is to get a job in analytics. With the shocking amount of information available in databases of all kinds, and with a deep need to use all this data, it seems that there is quite a gap between the number of jobs in this sector and the number of candidates.
Beyond that, analytics can help you run pretty much everything in your life. At work, it can help you take better business decisions regardless of what department you may work in. At home, it can help you decide everything from how much milk you need to buy for the next two weeks to what movies are better for you.
Analytics and Artificial Intelligence have long been regarded as evil tools that hold our information captive and use it for the bettering of the One Percent’s pockets.
In reality, however, analytics does nothing other than making our lives sweeter, better, more successful, and even longer.
The world of machines is coming whether you agree with it or not. So, the sooner you hop on the AI train, the better you will accommodate the new era knocking on everyone’s doors.
You may have seen a lot of movies on how AI can ruin the world. But the absolute truth is that it will only make it better. Just imagine being able to hold tools so powerful that they will take the most complex decisions for you so that you can simply focus on the things that truly matter for you – productivity, work efficiency, or, plain and simply, enjoying yourself.
Analytics and machine learning are the first steps to a Science Fiction-infused world – and they are just starting out. If you are interested in the topic, you are bound to see a lot of interesting research discoveries over the next few years precisely because the speed at which these research efforts are put together is getting higher and higher by the minute.
It’s a fast-paced world, and analytics are helping increase the tempo while maintaining mankind’s sanity.
What’s next?
Maybe we should ask an analytics tool – most certainly, it will be quite accurate in the predictions of its own future.
[1] https://emerj.com/ai-sector-overviews/ai-at-disney-viacom-and-other-entertainment-giants/
[2] https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe
[3] https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
[4] https://en.wikipedia.org/wiki/IBM_Blue_Gene
[5] https://www.ibm.com/watson
[7] https://www.datingadvice.com/online-dating/eharmony-success-rate
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