Friday, April 07, 2017

Understanding Data, Analytics and Technology platforms Part-3

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-3: Understanding Analytics Technology Platforms

Data analytics uses a wide variety of technology platforms. Just like different types of data, there are different types of analytic platforms. Traditional data analytic platforms still dominate today, but major investments are going into newer analytics platforms.

Traditional analytics still dominate for a good reason: Companies have defined their business processes based on existing these traditional analytics and know the full benefit of these analytics on top line & bottom line of their business. The benefits & impacts of traditional analytics are well known and analytics is tightly integrated into business decision making.

It takes time and new generation of management to embrace newer analytics platforms. In general, newer companies tend to be a bit more advanced analytically. They are more likely to use techniques such as predictive analytics or event stream processing.

The results of the study done by TDWI shows rapid investments in newer analytics platforms such as in-memory analytics (HANA or SPARK) which gives near real time analytics, while Hadoop, which is batch processing, is lagging in usage.

Closing Thoughts 

Organizations that are more analytically sophisticated may be more likely to move rapidly forward with analytics projects and quicker to embed predictive models into business processes. They have experienced managers to know how valuable analytics can be and success begets more success creating a positive feedback loop around analytics.

As organizations become data driven, they will tie their analytics efforts to meaningful measures of business outcomes and measure the impact of analytics on top line & bottom line of their business.  

1 comment:

sathya said...
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