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Building the Analytics Capability
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Building the Analytics Capability






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Building the Analytics Capability Building the Analytics Capability Presentation Transcript

  • Big Data & Business Analytics: Building the Capability Prof. Bala Iyer @BalaIyer March 04, 2014 1
  • Agenda  Big Data Context  Big Data for business  Building the capability  Questions to ask  Ecosystem Analysis  Recommendations 2
  • ―we now uncover as much data in 48 hours – 1.8 zettabytes (that's 1,800,000,000,000,000,000,000 bytes) – as humans gathered from "the dawn of civilization to the year 2003." 3
  • Categories  People  Machine  Social  Transactional 4
  • What do we mean by ―Analytical‖?  Analytical Decision-making: the use of data, analysis, models & systematic reasoning to make decisions  Questions to answer:    What decisions or business areas should analytics be applied? What kind of data do we have now & do we need? What kinds of analysis do we do? Source: ―What it Means to Put Analytics to Work‖, Davenport, Harris & Morrison, chapter from Analytics at Work: Smarter Decisions, Better Results, 2010. 5
  • Source: 6
  • Environment Decisions Your Data Models Decisions 7
  • 8
  • Virtual Business Environment Domain Resources Programs Model-base - Database Schema R e s o u r c e s M a n a g e r Engine manager Dialogue manger Business Context Engine Metaphors Visualization Interactive decision making Target Layered Knowledge base Cache Work processes 9
  • Stakeholders Owner Resources/Policies Modeler Data Scientist Business User User requirements And available services User requirements experiments Requests Sourcing data & models Development Platforms Analytics Capability Models Models/Data Validated models/ insights Validated Models Decisions 10
  • Data Scientist  A data scientist is an engineer who employs the scientific method and applies data-discovery tools to find new insights in data. The scientific method—the formulation of a hypothesis, the testing, the careful design of experiments, the verification by others—is something they take from their knowledge of statistics and their training in scientific disciplines. Data Scientists: The Definition of Sexy, Forbes 2013 link 11
  • Competencies or Stack Change Management Insights (Experimentation/Visualization) Domain Knowledge (best practices) Model Building (tools and techniques) Infrastructure (Data, Models/architecture) T O O L S 12
  • Obstacles  Shortage of data scientists  Huge technical challenges  Accessing talent in India  Lack of modeling knowledge  Decision-making culture (HIPPO)  Use cases emerging According to Wikibon the market is expected to reach USD53.4 billion in 2016 13
  • Target used data mining to predict buying habits of customers going through major life events  Target was able to identify 25 products (e.g., vitamin supplements) that when analyzed together helped determine a ―pregnancy prediction‖ score  Sent baby-related promotions to women based on this score  Outcome:   Sales of Target’s Mom and Baby products sharply increased soon after new advertising campaigns Privacy concerns: Target had to adjust how it communicated the new promotions Source: ―How Companies Learn Your Secrets‖, Duhigg, The New York Times, Feb. 16, 2012. 14
  • Many industries using data analytics for improving value disciplines  General Electric using Big Data to optimize the service contracts & maintenance1 The industrial internet.  Netflix used Big Data to predict if a TV show will be successful- ―House of Cards‖ series, Director & promotions2  LinkedIn used Big Data to develop ―People You May Know‖ products – 30% higher click-thru-rates3 Source: 1―What’s Your Strategic Intent for Big Data?‖, Davenport , CIO Journal in The Wall Street Journal, 1/23/2013. 2‖The Future of Entertainment is Analytical‖, Davenport , CIO Journal in The Wall Street Journal, 3/6/2013. Source: ―Data Scientist: The Sexiest Job of the 21st Century‖, Davenport & Patil, HBR, Oct 2012. 15
  • What are the sources of data?          ERP/CRM Transactional Systems Point-of-Sale/Scanner at Retail Customer Loyalty Programs Financial Transactions Click-Stream Data Social Media Data Mobile Personal analytics External Data Aggregators (e.g., AC Nielson) 16
  • What is a capability?  Firm’s capacity for undertaking a particular productive activity [Grant 1997]  Hamel & Prahalad coined the term core competences to distinguish those capabilities fundamental to a firm’s performance and strategy. They:   make a disproportionate contribution to ultimate customer value, or to the efficiency with which the value is delivered, and Provide a basis for entering new markets 17
  • Key competencies  Technical     Modeling Programming Statistical Science  Domain knowledge  Talent management  Cultural  Change management 18
  • How do companies build an analytics capability?  People: Data Scientist (need analytical + social + communication skills)  Leadership: Help decision-makers shift from adhoc analysis to ongoing conversations with data  Technology: for data management, programming and modeling  Process: workflows and methodologies for models and experiments Source: ―Data Scientist: The Sexiest Job of the 21st Century‖, Davenport & Patil, HBR, Oct 2012. 19
  • Choices  Insource  Outsource  Hybrid  Challenges with traditional IT outsourcing? 20
  • Sourcing intent  Augmentation  Adding new capacity  Validation  Knowledge transfer and IP  Building platforms 21
  • On Shore Off Shore Models of outsourcing  A company with its HQ in NY opens a analytics center in Chennai (India).  A company with its HQ in NY gets a third-party to do its work in Chennai (India).  Often called “Captive Centers” or “Captives.”  A company with its HQ in NY opens a analytics center in San Diego / Durham (NC). In-House  A company with its HQ in NY gets a third-party to do its work in San Diego. B P O Outsource BPO = Business Process Outsourcing 22
  • What should you outsource? 23 Strategic Sourcing From Periphery to the Core. By: Gottfredson, Mark; Puryear, Rudy; Phillips, Stephen. Harvard Business Review, Feb2005, Vol. 83 Issue 2, p132, 8p
  • Lessons from Outsourcing IT  Clear specifications  Increases flexibility in changing markets  Fast response  Fixed to variable costs  Proximity between onshore and offshore hub     matters Infrastructure and connectivity Language and technical skills IT adoption Contingency planning 24
  • Sourcing analytics  Core vs. periphery  Analytics for competitive advantage vs. parity  First time vs. in-house availability  Source all vs. source add-on capabilities 25
  • What challenges should one anticipate?  Problem definition complexity  IT implementation challenges  Modeling complexity  Change  Data regulation and compliance 26
  • Look for  Model building skills  Business domain knowledge  Technical or programming skills  Scientists vs. order takers 27
  • Client sophistication  Based on data management, talent management and analytics penetration in biz strategy. Tom Davenport  Analytics challenged Stage 5  First time users  Analytically superior  Internal capability exists Analytical Competitors Stage 4 Analytical Companie Stage 3 s Analytical Aspirations Stage 2 Localized Analytics Stage 1 Analytically Impaired 28
  • Questions  Unique vendor capabilities  Data protection  Analyst churn and satisfaction  Re-badging dedicated analysts  Cultural fit  Sourcing model  IP ownership  Low end vs. high end work  M&A risks 29
  • Variables to consider  Capability costs  Risk of failure  Size of vendor   Large body shops Small – niche skills and eager  Domain knowledge  Skills  RFPs 30
  • Traditional relationship framework  Includes setting detailed specifications  Pursuing costly renegotiations and  Participating in limited information exchanges  Discourage flexibility  Stifle innovation and  Erode trust 31
  • Analytics sourcing  Strategic importance to customer  Vendor has more expertise  Evolution and outcome of relationship is uncertain 32
  • 33
  • Strategic Adaptive Framework  Incentives,  Information and  Collaboration mechanisms. 34
  • Additional agreements  Exit options  Non-compete  Rights of first refusal 35
  • Centralization vs. Decentralization  One brain  Distributed knowledge  Federated model 36
  • Ecosystem Analysis 37
  • Analytics Ecosystem (840 nodes) Component Platform 38 Platform with high brokerage
  • High brokerage nodes Cloudera Pentaho IBM Fractal MuSigma Rapidminer SAS Cognizant MTECH Accenture Tableau SPSS Infosys AbsolutData Capgemini Genpact KXEN Oracle Wipro Opera TCS HCL LatentView Guavus 39
  • Types of service providers          Augmentation or spot services Pure play consultant Technology platform provider Change management services Digital thought leadership  Training for data scientists  Smart Lab  CoE Infrastructure and libraries Methodologies and Frameworks Assessment Data 40
  • Investments  Training/Recruitment    Data Scientist  Certification based on competency and project experience Techniques Domain knowledge  Product/platforms  Visualization metaphors  Knowledge communities  Build absorptive capacity 41
  • Risks  Privacy and ethics of data - ―Big brother‖  New skills for production and selling  Managing a pool of modelers  Communication between biz, modelers, programmers and scientists  Model management  Installed base of analysts/engineers 42
  • Questions? 43
  • 44