What is Data Analytics

Data analytics is the science of drawing insights from raw information sources. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

BREAKING DOWN Data Analytics

Data analytics is a broad term that encompasses many diverse types of data analysis. Essentially any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so that the machines operate closer to peak capacity.

Of course, data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set rewards schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Why Data Analytics Matters

As we mentioned above, data analytics is important because it helps a business optimize its performance. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new (and better) products and services. 

Types of Data Analytics

Data analytics is broken down into four basic types.

  • Descriptive analytics describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?
  • Diagnostic analytics focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
  • Predictive analytics moves to what is likely going to happen in the near term. What happened to sales last time we had a hot summer? How many weather models predict a hot summer this year?
  • Prescriptive analytics moves into the territory of suggesting a course of action. If the likelihood of a hot summer as measured as an average of these five weather models is above 58%, then we should add an evening shift to the brewery and rent an additional tank to increase output.

Data analytics underpins many quality control systems in the financial world, including the ever-popular Six Sigma program. If you aren’t properly measuring something — whether it's your weight or the number of defects per million in a production line — it is nearly impossible to optimize it.  

Who's Using Data Analytics 

Some of the sectors that have adopted the use of data analytics include the travel and hospitality industry, where turnaround can be quick. This industry can collect customer data and figure out where there are any problems, if any, and how to fix them. Healthcare combines the use of high volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The information retailers collect and analyze can help them identify trends, recommend products and increase profits.