Big Data Paradigm: Big Data Paradigm: Analysis, Application Analysis, Application

and Challengesand Challenges

Name : Uyoyo Edosio

13th Research Seminar Workshop

University of Bradfor

2

Introduction

What is Big Data?

Big Data Application

I

II

IV

Big Data AnalysisIII

3

8

14

10

Challenges Associated with Big DataV 18

ConclusionVI 21

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IntroductionSection 1

IDC estimates the volume of digital data will grow 40% to 50% per year. By 2020, IDC predicts the number will have reached  40,000 EB, or 40 Zettabytes (ZB). The world’s information is doubling every two years. By 2020 the world will generate 50 times the amount of information and 75 times the number of information containers.

Data Trends

Data Trends

Data Trends

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Definition f Big Data

Section 2

What is Big Data?The most accepted definition of Big Data is in terms of 3 characteristics, variety, velocity and Volume (3 V’s):

Variety : depicts its heterogeneous nature

Velocity : represent the pace to which data is acquired

Volume: illustrates the size of data.

More recently another v has been proposed its called “veracity”

Difference between Big Data and Traditional Data

Unlike traditional datasets which have corresponding predefined characteristics (Such as Char, int, Varchar), Big Data sets are in form of:Structured :eg Transaction details, bank account history,Unstructured: eg Tweets, Facebook Messages,

These features in addition to its 3V characteristics makes it impossible to analyze big data on traditional relational database

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Big Data AnalysisSection 3

How do we Analyse Big Data? Typically the process of managing data include processing,

Storage and Analytics. Before now a typical RDMS could serve all these purposes at once, but due too the nature of Big data this model has changed as follows:

Big Data Processing and Storage

Algorithm for Big Data Analytics

Machine Learning Clustering Algorithm Distributed learning Algorithm

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Application of Big Data

Section 4

Application of Big Data Predictive Analytics: Using data to predict trends and

patterns.

This is applied in supply chin to forecast furture demands on a product

Descriptive Analytics: Use of historical data to explain a business. This is associated with Business intelligence, it can be applied in order to gain understanding of consumer behavior

Prescriptive Analytics: using data to suggest optimal solution. Applied in inventory systems to predict inventory level

Government Electronic Campaign Crime prediction and prevention Predict economic trends

Healthcare Predict Outbreak Health monitoring and intervention

Travel & Transportation Customer analytics and loyalty

marketing Capacity & pricing optimization Predictive maintenance optimization Location Based Services

Consumer Products

Optimized promotions effectiveness

Micro-market campaign management

Real-time demand forecast

Energy and Utilities Distribution load forecasting and

scheduling Create targeted customer offerings Condition-based maintenance Enable customer energy

management Smart meter analytics

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ChallengesSection 1

Challenges aheadInvade User's privacyProduction of Noisy DataReal time is a real problemThe Missing Skills triangleBig data tools infancy

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ConclusionSection 5

ConclusionsBig data is a Phenomena,is a Methodology.Big data might be a Challenge,but also is a Cha

nceI recommend that more there is need for

more urgent research on stable hard ware systems and computational algorithms to manage and produce insights at optimum. As data growth is a going concern

Thanks...

Any Questions 😁?