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Building the Analytics Capability

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Bala Iyer
Bala Iyer

This document provides an overview and agenda for building an analytics capability. It discusses key topics such as: - The importance of big data and analytics for business decisions - Building an analytics capability requires the right people, processes, and technology - Companies can build capabilities internally, outsource work, or use a hybrid approach - When outsourcing analytics work, firms need to consider issues like vendor skills, data protection, and intellectual property ownership

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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

More Related Content

Building the Analytics Capability

  • 1. Big Data & Business Analytics: Building the Capability Prof. Bala Iyer @BalaIyer March 04, 2014 1
  • 2. Agenda  Big Data Context  Big Data for business  Building the capability  Questions to ask  Ecosystem Analysis  Recommendations 2
  • 3. ―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
  • 4. Categories  People  Machine  Social  Transactional 4
  • 5. 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
  • 8. 8
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. 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
  • 17. 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
  • 18. Key competencies  Technical     Modeling Programming Statistical Science  Domain knowledge  Talent management  Cultural  Change management 18
  • 19. 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
  • 20. Choices  Insource  Outsource  Hybrid  Challenges with traditional IT outsourcing? 20
  • 21. Sourcing intent  Augmentation  Adding new capacity  Validation  Knowledge transfer and IP  Building platforms 21
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. What challenges should one anticipate?  Problem definition complexity  IT implementation challenges  Modeling complexity  Change  Data regulation and compliance 26
  • 27. Look for  Model building skills  Business domain knowledge  Technical or programming skills  Scientists vs. order takers 27
  • 28. 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
  • 29. 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
  • 30. Variables to consider  Capability costs  Risk of failure  Size of vendor   Large body shops Small – niche skills and eager  Domain knowledge  Skills  RFPs 30
  • 31. Traditional relationship framework  Includes setting detailed specifications  Pursuing costly renegotiations and  Participating in limited information exchanges  Discourage flexibility  Stifle innovation and  Erode trust 31
  • 32. Analytics sourcing  Strategic importance to customer  Vendor has more expertise  Evolution and outcome of relationship is uncertain 32
  • 33. 33
  • 34. Strategic Adaptive Framework  Incentives,  Information and  Collaboration mechanisms. 34
  • 35. Additional agreements  Exit options  Non-compete  Rights of first refusal 35
  • 36. Centralization vs. Decentralization  One brain  Distributed knowledge  Federated model 36
  • 38. Analytics Ecosystem (840 nodes) Component Platform 38 Platform with high brokerage
  • 40. 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
  • 41. 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
  • 42. 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
  • 44. 44