Big Data and Analytics

Transcription

1 Big Data and Analytics

2 Industry Landscape

3 Big Data Everywhere! BIG DATA Data that is TOO LARGE & TOO COMPLEX for conventional data tools to capture, store and analyze. The 3V s of Big Data Shares traded on US Stock Markets each day: 7 Billion Number of tweets per day on Twitter: 400 Million Data generated in one flight from NY to London: 10 Terabytes Number of Likes each day on Facebook: 3 Billion 90 % OF THE WORLD S DATA WAS GENERATED IN THE VOLUME VARIETY VELOCITY LAST TWO YEARS

4 What is Analytics? Data on its own is useless unless you can make sense of it! WHAT IS ANALYTICS? The scientific process of transforming data into insight for making better decisions, offering new opportunities for a competitive advantage

5 Types of Analytics 1 Prescriptive Analytics Enabling smart decisions based on data What should we do? Analytics 2 3 Predictive analytics Predicting the future based on historical patterns What could happen? Descriptive analytics Mining data to provide business insights What has happened? 5

6 Growing Need for Analytics Generation of Large Amount of Data from Business Transactions DATA HARNESSING Companies store each piece of information generated during the business operations and customer interactions. Data is generated. Data is analyzed Learning from the data is used in the decision making and process optimization. DATA VOLUMES Volumes in Trillion GB DID? YOU KNOW 4 Billion Number of transactions every year 900 Number of Stores lakh Number of SKUs

7 Why Big Data Analytics? Why is Big Data Analytics important? Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.

8 Enter Data Scientists Data Scientist: The SEXIEST Job In The 21 ST century Harvard Business Review, Oct 2012 A Business analyst is not able to discover insights from huge sets of data of different domains. Data scientists can work in coordination with different verticals of an organization and find useful patterns/insights for a company to make tangible business decisions. 15,000% INCREASE IN JOB POSTINGS FOR DATA SCIENTISTS IN THE US BETWEEN

9 Demand for Analytics Professionals in India Domestic Market Analytics Market Job Openings $ 163 Million Jan, 2014 $ 1 Billion Jan, ,500 Jan, 2014 $ 375 Million Projected for 2018 $ 2.3 Billion Projected for ,50,000 Projected for 2018 Salary Report Source: NASSCOM Experience 0-2 Years 2-5 Years 5-10 Years 10+ Years Salary in INR 4.5 Lakhs 5.8 Lakhs 8.8 Lakhs 15 Lakhs

10 New Analytics Jobs By Industry Analytics Services and Pharma lead the pack in the highest percentage of Analytics jobs being created in developing nations. US INDIA CHINA UK BRAZIL JAPAN SINGAPORE ANALYTICS SERVICES 11% 54% 25% 9% 14% 6% 9% PHARMA 14% 24% 32% 19% 30% 44% 26% INSURANCE 39% 7% 8% 32% 11% 27% 24% BANKING 20% 11% 22% 25% 19% 14% 25% OIL & GAS 14% 3% 10% 13% 23% 8% 9% COMMUNICATIONS TECHNOLOGIES 2% 1% 3% 2% 3% 1% 7% TOTAL NUMBER OF JOBS 38,700 31,500 30,500 7,000 6,200 2,400 1,300

11 Employment Landscape in India Large IT Companies who have an Analytics Practice Analytics KPOs In-house Analytics Units of Large Corporates Niche Analytics Firms

12 Career Options in Analytics No matter what your educational background or aspirations, you have a fulfilling career in one of the many fields of Business Analytics. FIELDS IN ANALYTICS MIS REPORTING NON-PREDICTIVE ANALYTICS PREDICTIVE ANALYTICS MACHINE LEARNING KEY FEATURES Data management Data Exploration MIS and report creation Automation of reports Segmentation Customer profiling Portfolio Analysis Trend Analysis Forecasting Probability models Classification and regression trees Time series models Neural networks Multi-layer Perceptron Geospatial models Associative rule learning Inductive logic programming TARGET AUDIENCE BCom B.E BTech BCom B.E BTech MBAs B.E BTech MSc (Statistics) MBAs MCA B.E BTech MSc (Statistics) MBAs MCA INDICATIVE JOB ROLES MIS Analyst Data Analyst Strategy Analyst Cost Analyst Data Scientist Statistician Advanced Analytics Team Manager Market / Global Research Analyst Analytics Manager

13 Career Path and Indicative Salaries (CTC) CAREER TRAJECTORY 15+ Yrs Yrs Director VP Skills require to succeed in the Industry Strong analytical and critical thinking skills Statistics Predictive Analytics SQL Knowledge Understanding complex data & tools for Analytics Soft Skills & Communication Business Understanding 6-10 Yrs AVP Average Salaries 4-6Yrs Manager AVP Rs Lakhs 2-4 Yrs 0-2 Yrs Senior Analyst Analyst Manager Senior Analyst Analyst Rs Lakhs Rs. 5 Lakhs Rs 3-5 Lakhs

14 Popular Tools and Techniques

15 Why SAS? #1 Market Leader in Analytics The largest independent vendor in the business intelligence market The De facto industry standard for Clinical Data Analysis INTEGRATED PLATFORM FOR END TO END SOLUTIONS: SAS provides an integrated set of software products and services and integrated technologies for information management, advanced analytics and reporting. BUSINESS SOLUTIONS ACROSS DOMAINS AND INDUSTRIES: Unmatched domain specific industry focused analytics solutions Used in 60,000+ companies in over 135 countries Analytics powerhouse The Forrester Wave : Big Data Predictive Analytics Solutions, Q

16 Why R? Highest Paid IT Skill Dice Survey, Jan 2014 Most-used data science language after SQL O Reilly Survey, Jan % of data miners use R Rexer Survey, Oct 2013 Ranked #15 of all programming languages RedMonk rankings, Jan 2014 Growing faster than any other data science language KDNuggets Survey, Aug 2013 R is the #1 Google Search for Advanced Analytics software Google Trends, March 2014 More than 2 million users worldwide. Oracle Estimate, Feb 2012 Demand for R language skills is on the rise. Companies Already Onboard R Facebook Google Twitter Foursquare ANZ Bank FDA John Deere Lloyds of London & Many More R You Ready for R?

17 What is Hadoop? Hadoop is Transforming Businesses Across Industries 1 in 4 Organizations use Hadoop to manage their data today (up from 1 out of 10 in 2012) BIG DATA STORING AND FASTER PROCESSING Hadoop is an open source software framework created in 2005 that keeps and processes big data in a distributed manner on large collection of hardware. BUSINESS SOLUTIONS ACROSS DOMAINS AND INDUSTRIES: Low cost solution with a high fault tolerance to access and create value from data. The growing use of Apache Hadoop, increasing data warehouse volume sizes and the accumulation of legacy systems in organizations are fostering structured data growth. These factors are leading enterprises to understand how to reuse, repurpose and gain critical insight from this data. Gartner

18 Why Hadoop? Top 5 Reasons Organizations are using Hadoop Hadoop Big Data is a comprehensive class room training program that enables you to analyse data and create useful information for careers in Data Analytics. Low Cost Computing Power Scalability Storage Flexibility Data Protection Enterprises using Hadoop Top 5 Industries using Hadoop: Computer Manufacturing Business Services Finance Retail & Wholesale Education & Government

19 Why Python? Python is a powerful, flexible, open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis What are the reasons for its sudden popularity? Cost of Ownership Python is an open source software that is free to download. Versatility Multi-purpose language that can be used to build an entire application Big data compatibility Python has become one of the big go-to languages for big data processing due to its wide selection of libraries Python offers extensive analytics capabilities for Text & Predictive Analytics. IDLE & Spyder IDE is widely used for data mining. Big Data Analytics made possible by PyDoop and Scipy A Data Scientists Dream Python is particularly useful in data analytics because it has a rich library for reading and writing data, running calculations on the information and creating graphical representations of data sets. We can write map reduce programs in python using PyDoop. Here is where Python scores over R. While R uses in-memory processing, Python using PyDoop can process PetaBytes of data Integration In industry, the data science trend shows increasing popularity of Python. A Python-based application stack can more easily integrate a data scientist who writes Python code, since that eliminates a key hurdle in productionizing a data scientist's work.

20 Why Python? Official language of Google Among top in-demand data science skills KDNuggets, Dec % of job ads mention Python (after SQL) KDNuggets Dec 2014 Ranked #1 of all programming languages Codeeval rankings, Feb nd most popular data science language KDNuggets 2013 Companies Already Onboard Python Google Yahoo Quora Nokia ABN AMRO Bank IBM National Weather Service & Many More

21 What is Data Visualization? Data visualization is the presentation of data in a pictorial or graphical format. For centuries, people have depended on visual representations such as charts and maps to understand information more easily and quickly.

22 Why Tableau for Data Visualization? Tableau is a powerful, flexible Data Visualization tool that is easy to learn, easy to use, and has powerful libraries for data visualization and presentation. Cost of Ownership Tableau is a competitively priced software that is available for a trial download. Versatility Multi-purpose package that can be used to build an entire application Big data compatibility Tableau has become one of the big go-to software programs for Data visualization due to the wide variety of tools it provides and compatibility with Big Data platforms such as Hadoop.

23 Why Tableau for Data Visualization? Tableau offers Powerful visualization capabilities, without a single line of code. Experiment with trend analyses, regressions, correlations. Scalable, secure and Reliable Cloud and Mobile Connectivity. A BUSINESS ANALYSTS DREAM Tableau is easy to learn, use, and significantly faster than existing solutions. One can easily see patterns, identify trends and discover visual insights in seconds. No wizards, no scripts. Tableau facilitates live, up-to-date data analysis that taps into the power of the firm s data warehouse. Extract data into Tableau s data engine and take advantage of breakthrough in-memory architecture. INTEGRATION Tableau integrates exceptionally well with R and Hadoop, making it a powerful visualization tool for analytics and big data use cases. Developers creating web applications can integrate fully interactive Tableau content into their applications via the JavaScript API.

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