Wednesday, April 19, 2017

Data Analytics Primer ..

Information is processed data.

How can you process this data if it is very large running to terabits and is being generated on a continuous basis ?  One can easily get lost in the vast ocean of data. It is here the fields of big data and data analytics have joined hands to arrive at a tractable solution.
Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. credit -
Big Data refers to the extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. (source : wikipedia)

Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. (

Big Data comes from many sources it could be internal or external from customers, suppliers, transporters etc.., from the Internet, social media, sensors, IoT devices etc..

The 4 Vs of Big Data is

1. Volume (size of data running to Terabits) ,
2. Velocity (generated on a daily basis non-stop from units across the world),
3. Variety (from a large variety of sources like social media, retail stores, transportation, healthcare and so on) and
4. Veracity (how accurate is the source of data, is it corrupted, what is the quality of the data etc,,).

Big data could be in structured form like from a provision or retail store or unstructured form as from social media or facebook. Structured data can be handled by means of Database Management Systems or more efficiently with Relational Database Management Systems (RDBMS like MySQL, PostgreSQL, Oracle etc..) can handle strauctured data. But how can unstructured data that comes from twitter or Facebook or Google search be handled ?

The different models of Data Analytics is

A. Descriptive Data Analytics tells us what has happened in the past

B. Predictive Data Analytics they use past data to model future outcomes

C. Prescriptive Data Analytics uses optimisation or other mathematical models to help take better, effective, optimised decisions.

Big Data and Effective Analytics is a great competitive advantage for firms across the world. One model which helps one to achieve this competitive advantage is the DELTA model.

The DELTA model of Data Analytics is a simple yet effective model used across applications


Enterprise wide focus for data ensures data has to reach and should be accessible across all corners of the enterprise

Leaders across all functions that promote a analytics driven culture in the organisation

Targets, financial and non-financial that can help key business areas or departments meet their targets

Analysts help to analyse the data effectively and come out with right information to help take quality decisions.

Bid Data and Data Analytics is a great tool for the organisation to take the right decision and overtake competitors in he highly competitive world.

Click here to Understand Data mining - the three steps to analysing unstructured data ..

An excellent video from HBR click here ..

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