Big Data Analytics

Automotive

Analytic solution to rapidly launch complex sustainable vehicles, build and optimize dynamic value chains, capitalize on services for connected vehicles, and transform automotive retail.

Government

Analytic solutions for public safety and justice, defense and intelligence, cyber security, social programs and finance operations

Healthcare

Analytic solutions to collaborate to improve care and outcomes, build sustainable system, and increase access to healthcare.

Insurance

Analytic solutions to create a customer-focused enterprise, increase flexibility and streamline operations, and optimize enterprise risk management

Banking

Analytic solutions to create a customer-focused enterprise, optimize enterprise risk management and drive innovation while managing cost.

Electronics

Analytic solutions to create innovative products and services, build dynamic value chains, and provide differentiated customer experience.

Retail

Analytic solutions to deliver a smarter shopping experience., build smarter merchandising and supply networks and drive smarter operations.

Financial Markets

Analytic solutions to deliver client value through insight and innovation, increase flexibility and streamline operations, and optimize enterprise risk management

Oil and Gas

Analytic solutions to enhance exploration and production, improve global operations (upsteam and downstream) and improve refining and processing efficiency.

Education

Analytic solutions to help education administrators manage enrollment efficiently, retain students and strengthen ties with alumni and donors

Media

Analytic solutions to help monitor and capture consumer feedback from multiple sources (social media, website, blogs etc..)

Consumer Products

Analytic solutions to improve consumer insights and engagement, channel partner analytics and optimize the supply chain.

Travel and Transportation

Analytic solutions to improve end to end customer experience, maximize availability of assets and infrastructure, improve operational efficiency.

Telecommunications

Analytic solutions to improve subscriber insight transform business for higher efficiency, and innovate business models.

Energy and utilities

Analytic solutions to innovate business processes, gain a 360-degree view of customers and assume the role of the energy integrator.

Metals and Mining

Analytic solutions to optimize mill production operations, maximize mining productivity and reduce costs and maximize return on capital projects

It allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable.

How helpful is it to analyse a Big Data?

HydroSense

Is a pressure based sensor that automatically determine water usage activity and flow down to the source from a single-non intrusive installation point

ElectriSense

Is a single plug-in sensor that provides whole home device level usage data.

Big Data

It is any data that is expensive to manage and hard to extract value from. =Just an additional---Key idea: "Big" is relative! "Difficult Data" is perhaps more apt! =

-Increase of storage capacities -Increase of processing power -Availability of data

Key Enablers for the Growth of Big Data

-Volume -Velocity -Variety -Veracity

What are the 4V's of Big Data?

text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing

What are the advanced analytics techniques that helps business analyse previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions..

-Sensors used to gather climate information -Posts to social media sites -Digital pictures and videos -Software logs -Documents -Politics -GPS trails -Purchase transaction records -Traffic

Where does Big Data originates?

-"data exhaust" from customers -new and pervasive sensors -the ability to "keep everything"

Where does big data come from?

-More data leads to more accurate analyses. -More accurate analyses leads to better decision making. -Better decisions means greater operational efficiencies, cost reductions and reduced risks.

Why is Big Data Important?

such that it's difficult to capture, process, store, search and analyse this kind of data using conventional data management tools and traditional database management systems.

Why is there a need to use a "Big Data"?

Big data

is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency.

Big Data Analytics

is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes.

Big Data

is used to describe the collection of complex and large data sets such that it's difficult to capture, process, store, search and analyse this kind of data using conventional data management tools and traditional database management systems.

-The traditional databases require the database schema to be created in advance to define the data flow it would look like which makes it harder to handle variety of data -Traditional databases can't analyse data from social media, data from videos, data from sensors as this type of data grows at very fast speed and also this is unstructured data -The traditional databases are not designed to handle database insert/update rates required to support the velocity at which Big Data arrives or needs to be analysed

4Vs Challenges for Conventional Disk-based Relational Databases

Variety

*Data in many forms *Structred, unstructured, text multimedia *the diversity of sources, formats, quality, structures

Velocity

*Data in motion *Streaming data, milliseconds to seconds to respond *the latency of data processing relative to the growing demand for interactivity

Volume

*Data at Rest *Terabytes to exabytes of existing data to process *size of the data

Veracity

*Data in doubt *Uncertainty due to data inconsistency & incompleteness, ambiguities, latency, deception, model approximations *trust on data accuracy and data sources