DATA ANALYTICS

In today’s society, technology has become more advanced than the human’s mind.In the era of information explosion, enormous amounts of data have become available on hand to decision makers. 

Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.Data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.

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.

Is data analytics only for big data?

No, data analytics is a general term for any type of processing that looks at historical data over time, but as the size of organizational data grows, the term data analytics is evolving to favor big data-capable systems.

The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively.

While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.

What is the status of the data analytics market place?

Today the field of data analytics is growing quickly, driven by intense market demand for systems that tolerate the intense requirements of big data, as well as people who have the skills needed for manipulating data queries and translating results.