entering the data analytics industry

Download Entering the Data Analytics industry

Post on 16-Apr-2017

268 views

Category:

Data & Analytics

0 download

Embed Size (px)

TRANSCRIPT

  • 1

    UPGRAD WORKSHOP10TH DEC16 @HYD

    ENTERING THEDATA ANALYTICS INDUSTRY

    B GANES KESARIVP, GRAMENER

  • 2

    DATA ANALYTICS ?WHATS THE BUZZ AROUND ANALYTICS

  • We have internal information. Getting

    information from outside is our challenge. Theres no

    way of doing that.

    Senior EditorLeading Media Company

  • INDIAS RELIGIONS

    4

  • AUSTRALIAS RELIGIONS

    5

  • 6

  • WHAT ARE PEOPLE LOOKING FOR IN DATA ANALYTICS?

    7

    USA India

    data analytics jobs

    data analytics tools

    data analytics salary

    data analytics training

    Jobs & Salary Tools Companies Training & Courses

    data analytics courses

    data analytics tools

    data analytics jobs

    data analytics companies

    Source: https://google.com, https://google.co.in

  • WHATS THE POPULARITY OVER TIME?

    8

    Data Analytics

    Source: https://trends.google.com/

  • WHICH CITIES HAVE INTEREST IN DATA ANALYTICS?

    9Source: https://trends.google.com/

    0 20 40 60 80 100 120

    GurgaonPimpri-Chinchwad

    NoidaBengaluruHyderabad

    ChennaiSingapore

    MumbaiSan Francisco

    DublinBoston

    WashingtonPune

    HowrahToronto

    New YorkSydney

    New DelhiChicago

    Melbourne

  • 10

    WHATS THE STATE OF THE

    DATA ANALYTICS JOBS

  • WHOS RECRUITING THE TEAMS?

    11

    0 50 100 150 200 250 300 350 400 450

    IBM India

    Accenture

    JPMorgan

    KPMG

    Concentrix Daksh

    Microsoft India

    Ernst & Young

    UnitedHealth Group

    Shell India Markets

    Amazon Dev Centre

    GE India Technology

    Hewlett-Packard

    Deloitte

    Cisco Systems

    WNS

    Xerox

    eClerx Services

    Mphasis

    AIG Analytics

    Sapient Consulting

    #Jobs

    Source: https://www.naukri.com

  • WHAT INDUSTRIES USE DATA ANALYTICS?

    12

    0% 10% 20% 30% 40% 50% 60%

    Software

    Banking, Financial Services

    Internet, Ecommerce

    KPO, Research, Analytics

    BPO, Call Centre, ITES

    Recruitment, Staffing

    Strategy Mgmt Consulting

    Media & Entertainment

    Advertising & PR

    Accounting & Finance

    Telcom, ISP

    Education, Teaching & Training

    Pharma, Biotech & Clinical Research

    Insurance

    FMCG, Foods & Beverage

    Source: https://www.naukri.com

  • WHAT DO THEY PAY?

    13

    0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%

    0-3 Lakhs

    3-6 Lakhs

    6-10 Lakhs

    10-15 Lakhs

    15-25 Lakhs

    25-50 Lakhs

    50-75 Lakhs

    75-100 Lakhs

    100+ Lakhs

    Source: https://www.naukri.com

  • WHERE ARE THE DATA ANALYTICS JOBS?

    14Source: https://www.naukri.com

    0% 5% 10% 15% 20% 25%

    Bengaluru

    Delhi NCR

    Mumbai

    Gurgaon

    Hyderabad

    Others

    Pune

    Noida

    Chennai

    Delhi

  • WHO ARE THE BIG PLAYERS IN THIS SPACE?

    15Source: Gartner BI Magic Quadrant

  • WHICH STARTUPS OFFER DATA ANALYTICS IN INDIA?

    16Source: https://angel.co/... and more

  • 17

    WHY DATA ANALYTICS?WHATS CAUSING ALL THIS BUZZ

  • CLASSES OF ANALYTICAL SOLUTIONS

    18

    Proactive ActionWhat should I do to achieve my goal?Data products, data validated actions, increased success rate of strategic initiatives

    ModeApproach to data Benefits

    Proactive DecisionsWhat is likely to happen?Support for strategic initiatives, forward looking decision making

    Proactive Consumption

    ActiveWhat happened ? Marginal business benefits , process gap identification

    Why did it happen? Significant improvements from status quo, data backed management

  • 19

    Proactive Action

    ModeApproach to data Benefits

    Proactive Decisions

    Proactive Consumption

    ActiveOperational Reporting for measurement of efficiency & compliance

    Marginal business benefits , process gap identification

    CLASSES OF ANALYTICAL SOLUTIONS

  • TIMES NOW COVERAGE HAD

    80%+ VIEWERSHIP 20

  • 21

    Proactive Action

    ModeApproach to data Benefits

    Proactive Decisions

    Proactive ConsumptionRoot Cause Analysis , Benchmarking and multi-

    dimensional analysis

    Significant improvements from status quo, data backed management

    Active

    CLASSES OF ANALYTICAL SOLUTIONS

  • DETECTING FRAUD

    We know meter readings are incorrect, for various reasons.We dont, however, have the concrete proof we need to start the process of meter reading automation.

    Part of our problem is the volume of data that needs to be analysed. The other is the inexperience in tools or analyses to identify such patterns.

    ENERGY UTILITY

  • This plot shows the frequency of all meter readings from Apr-2010 to Mar-2011. An unusually large number of readings are

    aligned with the tariff slab boundaries.

    This clearly shows collusion of some form with the customers.

    Apr-10 May-10Jun-10Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11217 219 200 200 200 200 200 200 200 350 200 200250 200 200 200 201 200 200 200 250 200 200 150250 150 150 200 200 200 200 200 200 200 200 150150 200 200 200 200 200 200 200 200 200 200 50

    200 200 200 150 180 150 50 100 50 70 100 100100 100 100 100 100 100 100 100 100 100 110 100100 150 123 123 50 100 50 100 100 100 100 100

    0 111 100 100 100 100 100 100 100 100 50 500 100 27 100 50 100 100 100 100 100 70 1001 1 1 100 99 50 100 100 100 100 100 100

    This happens with specific customers, not randomly. Here are such customers meter readings.

    Section Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11Section 1 70% 97% 136% 65% 110% 116% 121% 107% 114% 88% 74% 109%Section 2 66% 92% 66% 87% 70% 64% 63% 50% 58% 38% 41% 54%Section 3 90% 46% 47% 43% 28% 31% 50% 32% 19% 38% 8% 34%Section 4 44% 24% 36% 39% 21% 18% 24% 49% 56% 44% 31% 14%Section 5 4% 63% -27% 20% 41% 82% 26% 34% 43% 2% 37% 15%Section 6 18% 23% 30% 21% 28% 33% 39% 41% 39% 18% 0% 33%Section 7 36% 51% 33% 33% 27% 35% 10% 39% 12% 5% 15% 14%Section 8 22% 21% 28% 12% 24% 27% 10% 31% 13% 11% 22% 17%Section 9 19% 35% 14% 9% 16% 32% 37% 12% 9% 5% -3% 11%

    If we define the extent of fraud as the percentage excess of the 100 unitmeter reading, the value varies considerably across sections, and time

    New section manager arrives

    and is transferred out

    with some explainable anomalies.

    Why would these happen?

    Simple histograms have been applied to manage ALM compliance,fraud in corporate directorships, and collusion in schools

  • What do the children in schools know and can do at different stages of elementary education?

    Have the inputs made into the elementary education system had a beneficial effect or not?

    24

  • HAVING BOOKS IMPROVES READING ABILITYHaving more books at home improves the performance of children when it comes to reading. (But children typically only have only 1-10 books at home)

    Number of students sampled

    What is the impact? How many more marks can having more books fetch?

    Circle size indicates number of students with this response. Few students have no books.

    Is this response (25+ books) good or bad? Small red bars indicate low marks. Large green bars indicate high marks. Students having 25+ books tend to score high marks.

    The most common response is marked in blue. This is also the circle.

    The graphic is summarized in words

    Indicates whether the best response is the most popular. Blue means that it is not. Green means that it is. Red means that the worst level is the most popular response.

    25

  • HAVING MORE SIBLINGS DOESNT HELP READINGChildren with 1 sibling do much better than children with many siblings

    26

  • BUT HELPS A LOT IN MATHEMATICSChildren with 4+ siblings do very well, children with 1 sibling fare poorly

    27

  • TUITIONS HELP A LITTLE

    BUT NOT CHILDREN WITH 4+ SIBLINGS

    28

  • TUITIONS HELP A LITTLE

    BUT NOT CHILDREN OF ILLITERATE PARENTS

    29

  • CHILDREN LIKE GAMES, AND THEYRE GOOD

    but playing daily hurts reading ability30

  • 31

    Proactive Action

    ModeApproach to data Benefits

    Proactive Decisions

    Proactive Consumption

    Active

    Statistical Analysis thru Segmentation, Decision Trees and

    Cause-effect Modelling

    Support for strategic initiatives, forward looking decision making

    CLASSES OF ANALYTICAL SOLUTIONS

  • 32

    Telecommunication

    How to predict customer churn, atleast a month ahead

  • 33

    Background & Objective

    Gramener Approach

    Customer churn is a well noted problem in telecom industry today. One of the leading telecom operator in the country wanted to predict the churn rate 2/4 week before using an analytical model.

    Exploratory Analysis & influencers

    Predictive Intervention

    Linear Discriminant Parameters

    Exploratorybusinessanalysisperformedtoidentifyinfluencers&createadditionalderivedmetrics&deriveddimensions

    Usingselectivemetrics,modelswerebuiltonLinearClassificationlikeDecisiontrees,LinearDiscriminantParameters

    Non Linear Models

    Usingselectivemetricsnon-linearfamiliesofmodelswerebuilt:NeuralNetworks,RandomForests&SupportVectorMachines

    Thebestmodelwasimplemented&comparedwithacontrolset

    Targetedpromotions forpredictedsetyielded~60%reductioninchurn

    CLASSES OF ANALYTICAL SOLUTIONS