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Big Data Analytics

The scientific process of transforming data into insight for making better decisions.~INFORMS

Analytics leverage data in a particular functional process (or application) to enable context-specific insight that is actionable~Gartner

Different Definitions

for analytics

What is Analytics?

SIMPLY PUT->

CRMETL

Data Quality

Normalised Data

DATA WAREHOUSE

ERP

FINANCE

Business Administrator

Business Analyst

Business User

Traditional Data Analytics

Source :Wikibon 2011

BIG DATA

“Big data is the term increasingly used to describe the process of applying serious computing power—the latest in machine learning and artificial intelligence—to seriously massive and often highly complex sets of information.”

Big data opportunities emerge in organizations generating a median of 300 terabytes of data a week. The most common forms of data analysed in this way are business transactions stored in relational databases, followed by documents, e-mail, sensor data, blogs, and social media

Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is “big data.”

Types Of Data

Big Data is more associated with UNSTRUCTURED and EXTERNAL data

Structured:Can easily fit rows and columns of a database

Unstructured: Cannot be easily compiled into older database formats

SemiStructured:Uses tags to capture elements of data

Internal Data:From a company’s sales, employee records etc

External Data: From third party providers, social media etc

Data Defined By Source

Data defined by Sructure

Traditional Data BIG DATAGigabytes to Terabytes Petabytes to exabytescentralised DistributedStructured Semi Structured and

UnstructuredStable Data Model Flat SchemasKnown Complex interrelationships

Few Complex Interrelationships

1,000,000,000,000 Gigabytes1,000,000,000 Terabytes

1,000,000 Petabytes

1,000 Exabytes

1 Zettabyte

2012 2013 2014 2015 2016 20170

102030405060

5.1 10.216.8

32.1

48 53.4

Big Data Market Forecast(In $US billions)

Sources of Big Data


Big Data

Across CountriesUse of Big Data

Percentage

of companies

with big

data

Initiatives

Percentage of companies whose big data initiatives have improved decision making

Big data refers to enormity in five dimensions:

Big data

VOLUME

VARIETY

VELOCITYVARIABILITY

COMPLEXITY

Analysis Types DescriptionBasic Analytics for insight

Slicing and Dicing of data, reporting,simple,visualisations,basic monitoring

Advanced Analytics for Insight

More complex data analysis such as predictive modelling and other pattern matching techniques

Operationalised Analytics

Analytics becomes part of the business process

Monetised Analytics

Analytics used directly to drive revenue

BIG DATA ANALYTIC

SUsing Big Data To get results

Source :The Economist

Basic analytics can be used to explore your data, if you’re not sure what you have, but you think something is of value.

Slicing and Dicing-Breaking down data into smaller sets of data that are easier to explore.

Basic Monitoring-Monitor large volumes of data in real time

Anomaly identification-An event where the actual observation differs from what is expected.

Basic Analytics

Advanced Analytics

Advanced analytics can be deployed to find patterns in data, prediction, forecasting, and complex event processing.

Advanced analytics provides algorithms for complex analysis of either structured or unstructured data

Includes sophisticated statistical models, machine learning, neural networks, text analytics and other advanced data-mining techniques

Predictive Analytics-Techniques that can be used on both structured and unstructured data (together or individually)to determine future outcomes.

Text Analytics-the process of analyzing unstructured text, extracting relevant information, and transforming it into structured information

Other Methods-advanced forecasting, optimization, cluster analysis for segmentation or even microsegmentation, or affinity analysis

Data Mining-exploring and analysing large amounts of data to find patterns in that data.

Overview on Big Data Market Segment


Hadoop:Hadoop is an open source framework for processing, storing and analyzing massive amounts of distributed, unstructured data. Rather than banging away at one, huge block of data with a single machine, Hadoop breaks up Big Data into multiple parts so each part can be processed and analyzed at the same time.

NoSQL:NoSQL databases are aimed, for the most part (though there are some important exceptions) at serving up discrete data stored among large volumes of multi-structured data to end-user and automated Big Data applications.

Massively Parallel Analytic Databases:Unlike traditional data warehouses, massively parallel analytic databases are capable of quickly ingesting large amounts of mainly structured data with minimal data modeling required and can scale-out to accommodate multiple terabytes and sometimes petabytes of data.

Big Data Approaches