In the digital world, there's a lot of talk about data analytics. While the concept isn't a new one, it's becoming increasingly important as the amount of data available to businesses and other institutions is growing exponentially. But what is data analytics? It's basically the process of examining raw data and extracting conclusions about it, such as patterns, trends, new insights, and answers to important questions.
Why Use Data Analytics?
You'll have a hard time finding an industry that doesn't rely on data analytics. It's big in retail, especially with stores' digital marketing teams, as they work to create new marketing campaigns that appeal to their customers. It can also help improve customer service by offering a personalized experience to shoppers. All of this can lead to an increase in profits. It's also big in the health care sector, both on the business side of running a hospital or doctor's office and the scientific side of researching new cures for diseases. Government agencies, like your local police department, also use data analytics for tasks such as determining where to allocate funds in order to decrease crime in certain areas. These are just a few examples.
Types of Data Analytics
To break it down even further, there are four main types of data analytics. The first one is descriptive analytics, which focuses on the "what." An example could be how many people watched a TV show last week. The second type, diagnostic analytics, complicates things a bit more. This type answers the "why," and requires the person analyzing to do some guesswork. Why didn't more people watch a TV show during a certain week? Was there a football game on a competing channel? Did the show not receive enough advertising?
The third type of analytics is predictive, and, as the name suggests, it answers the question "what could happen in the future?" For example, if people stopped watching a TV show on Monday night because of a competing football game, there's a chance they're not going to watch it again until the football season is over. The final type of data analytics is prescriptive analytics. This answers the question "what can we do to change the outcome?" Maybe the TV network decides to air a show at an earlier time or on a different day of the week during a football season.
The Difference Between Data Analysis and Data Analytics
Many people often wonder about the difference between data analysis and data analytics, or they may assume the two terms mean the same thing. They are similar, but data analysis is a broader concept. It's more about putting together a group of data and examining it for any information that may interest its owner, while data analytics uses tools to extract specific information from the data.
What Jobs Are Available in This Field?
If you are fascinated by the concept of data analytics and want to turn it into a career, you may consider becoming a data scientist. Data scientists should be intellectually curious and have a desire to solve complex problems. They have a background in computer science and math, but they may also have some business knowledge and communication skills. If they were around before big data, they may have worked as statisticians.