Tuesday, April 04, 2017

Understanding Data, Analytics and Technology platforms Part-2

Note: This article was co-authored with Saraswathi Ramachandra

Today, lot of young engineers often ask me about analytics.

Often, the word "Analytics" is used in conjunction with "Big Data" or "Hadoop". But in real business world, Hadoop or bigdata forms a small portion (but growing) of today's business analytics.

This prompted us to write this blog to educate folks on realities of analytics in business today. This blog is written in three parts for ease of reading.

Part-1: Understanding Data for Analytics
Part-2: Understanding different Uses of Analytics
Part-3: Understanding Analytics Technology Platforms


Part-2: Understanding different Uses of Analytics 

Once we know what types of data to use, one can now move on know about different uses of analytics.

The most common use of data analytics is reporting & visualization for top management. While performance management, forecasting and operations optimization is used by middle management to run business.

Newer analytics such as predictive analytics, Social Media analytics, etc are gaining steam. In next few years we will see the uses of these analytics double.

Today, with high performance computing, millions of events can be collected, transformed, & analyzed in real time for automating decisions. For example, webpage rendering on a e-commerce site is done dynamically based on users history and current market trends.

More recently, advanced analytics is being applied in streaming analytics. For example, a Fourier analysis might be performed in real time from electronic sensors data that produce time-series data. Because there may be so much information arriving that needs to be analyzed in the moment, this analysis is often done in-memory in real time. These kinds of solutions will increase in usage as machine-generated and IoT data increases.

Time-series, streaming data analysis are used for pattern detection. Pattern detection along with a rules engine is then used to automate decisions. For example in stock trading.

The benefits of real-time analytics are immense. Organizations that use sophisticated analytics are more likely to move forward with projects that involve embedding predictive models into operations. They have already experienced how valuable analytics can be and success begets more success.


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