Big Data and

The IOT Revolution

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Analysis of such big data, as the name suggests, involves applying statistical analysis techniques to large volumes of data with a view to discover hitherto unknown relationships, patterns, correlations or other such insights. Today, with the advent of cloud computing, huge amounts of storage and computing power can be harnessed by organizations to analyse their data and get answers from it almost on the fly.

The importance of Big Data

Big Data Analytics helps companies achieve the following related objectives:

Faster and better decision making. With the speed of in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.

New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. This helps companies create new and more relevant products to meet customers’ needs.

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Types of Big Data AnalyticsResponsive image

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Descriptive Analytics

These tools 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 reports.

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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.

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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.

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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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Data is the new Oil

Analytics is no longer a back office support function for organizations; with the explosion in the volume of data collected by organizations, analytics is now driving everything from Strategy to Marketing to Finance and Accounting and has now become a mainstream line function in its own right. This function needs the correct tooling and systems support in order for it to unlock value for the organization as a whole.

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We primarily use open source technologies and tools for your specific use case, so that you get to use the core tool set you require, without getting overwhelmed by all the other tools and techniques out there:

Apache Spark (part of Hadoop ecosystem), Python (our language of choice), SAS & Big Data, Hadoop and Splunk.

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