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Big Data Analytics

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Big data analytics is quickly gaining adoption. Enterprises have awakened to the reality that their big data stores represent a largely untapped gold mine that could help them lower costs, increase revenue and become more competitive. They don’t just want to store their vast quantities of data, they want to convert that data into valuable insights that can help improve their companies.

As a result, investment in big data analytics tools is seeing remarkable gains. According to IDC, worldwide sales of big data and business analytics tools are likely to reach $150.8 billion in 2017, which is 12.4 percent higher than in 2016. And the market research firm doesn’t see that trend stopping anytime soon. It forecasts 11.9 percent annual growth through 2020 when revenues will top $210 billion.

Clearly, the trend toward big data analytics is here to stay. IT professionals need to familiarize themselves with the topic if they want to remain relevant within their companies.

What is Big Data Analytics?

The term “big data” refers to digital stores of information that have a high volume, velocity and variety. Big data analytics is the process of using software to uncover trends, patterns, correlations or other useful insights in those large stores of data.

Data analytics isn’t new. It has been around for decades in the form of business intelligence and data mining software. Over the years, that software has improved dramatically so that it can handle much larger data volumes, run queries more quickly and perform more advanced algorithms.

The market research firm Gartner categories big data analytics tools into four different categories:

  1. Descriptive Analytics: These tools tell companies what happened. They create simple reports and visualizations that show what occurred at a particular point in time or over a period of time. These are the least advanced analytics tools.
  2. Diagnostic Analytics: Diagnostic tools explain why something happened. More advanced than descriptive reporting tools, they allow analysts to dive deep into the data and determine root causes for a given situation.
  3. Predictive Analytics: Among the most popular big data analytics tools available today, predictive analytics tools use highly advanced algorithms to forecast what might happen next. Often these tools make use of artificial intelligence and machine learning technology.
  4. Prescriptive Analytics: A step above predictive analytics, prescriptive analytics tell organizations what they should do in order to achieve a desired result. These tools require very advanced machine learning capabilities, and few solutions on the market today offer true prescriptive capabilities.

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