Analytics Service Framework

Entreprenuer, Consultant, - Big Data, Analytics & Digital à Unmithy
17 May 2016

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Analytics Service Framework

  1. Analytics growing as a business mandate. Data is Growing Performance Gap Widens Capability Gap Exists.. 4.4x 2.7x 2.4x 2.4x 2x Investment in Data and Analytics Top Performer Bottom Performer Sources: IBM Breakaway Now with Business Analytics and Optimization 17% 42% 28% 10% USE OF DATA BY BUSINESS* 75% or more 50-74% 25-49% 0-24% ++ There is a skill gap 60% executives say they “have more information than we can effectively use”** [IBM Report] . McKinsey Report on Big Data estimates 50-60% gap in the supply of deep analytical talent; equaling 140,000 to 190,000 unfilled positions. 40% growth in global data annually Globally 2.5 quintillion bytes of data per day 90 % of the data in the world today has been created in the last two years alone. Customer Transactions Customer records through device ubiquity and better data mgmt.. 1 Customer Interactions Social Unstructure, semantics.. 20B events / Day – Facebook 2 Machine Interactions Logs sensors intelligence on all equipment 3 IBM Report  Global Business Analytics market size is pegged around $105 billion and growing at CAGR 8%. Shifting Priorities for Management in Analytics..
  2. Potential for applying Analytics to Business Based on areas explored with verticals.. During BPVM ThemesFinance & Accounting GRC CRM Service& Warranty Vertical Solutions Worldwide financial services OpRisk and GRC technology market will grow to $2 billion by 2013 at a compound annual growth rate of 6.5%. The global financial data analytics market size has been potentially estimated at $5 billion The global warranty management technology market will represent more than $1.1bn in 2012, compared to $715m in 2007 Worldwide CRM Applications Market Forecast to Reach $18.2 Billion in 2011, Up 11% from 2010 In 10 years, leveraging big data in the health industry could capture $300 billion annually. Potential increase in retailers’ OM from big data could be 60% High %-age of spend directed towards Analytics Sources: 1 - Prithvijit Roy: New financial analytics hub; 2 - Chartis Research; 3 – IDC; 4 – Datamonitor; 5 - McKinsey BigData report, 1 2 3 4 5 Low
  3. Analytic Techniques that provide the most value MIT SMR – IBM Study – The New path to Value 2012
  4. Value Chain of Analytics in Business. CRITICAL BUSINESS KPIs DATA MANAGEMEN T PROCESS CHANGES Strategic Themes Volume, Variety, Velocity Actions from Insights / Foresights Business Analytics VISUALIZATIO N Real time / In Process ANALYTICS APPLICATION S Insights & Foresights
  5. Analytics Value to Business Business outcome Operations Transformation Insights Data •Customer Insight •Digital Marketing •Pricing / Risk •Product Design •Service / Operations •BI / Dashboards •Manual Operations •Embedded Analytics •CEP / Rules Engines •RT Integration •Analysis / Methods •Prediction / Data Mining •Machine Learning •Sample vs Large Data •Parameterized and NON •Data Sources { External, Unstructured } •Data Integration {ETL} •Data Lineage {Metadata} •Data Preparation {Index, Search} •Customer Segmentation, Behavior based models in all industry •Price Sensitivity analysis •NPD / Molecule research in Pharma •Risk in BFSI •Driving Digital Initiatives like Mobile •Triaging / Routing in Contact centers •Running a Analytics KPO that provides insights for Operations •Methods like Segmentation, Regression based scoring, • Sensitivity Scenarios , What-if •Text and media mining capabilities [ PCA ] •Semantic Search •70% of the effort is spelt out in Data •External sources, public and paid.. •Text, media processing / Index
  6. Analytics Services Maturity Model ALIGNED INTEGRATED OPTIMIZINGFRAGMENTED DATAANALYTICSVISUALIZATIONPROCESS[ACTIONS] SCALE / STRUCTURE SOURCE / RETRIEVE CONFIG - CONTROL INTERACTION ALGORITHM MODELING DESIGN EXECUTE MANAGE PRESENTATION STRUCTURE Simple 2-Dimensional Graphs and reports including Types of Visuals supported? Static simple play out Simple structure, numeric [ cardinal] and non-numeric- [ Ordinal] Internal Local Files, federated Ad-hoc Customer opportunity Operational Changes > Basic Functions and statistics User Configuration, Data Security Structured Data with metadata support, Integrated data sets through DB- DWH, SQL based retrieve Single Iteration playout Computational Flows Process Maps, Kpi- Metrics Breakdowns, Manual Process Change / Actions Tactical Changes – re-structure to Business operations, processes.. Linear Functions, Regression, Statistics, Strategy Changes - New services models, synthesis of business value Integrated Partner Actions, Automation into systems, scenario analysis, what -if analysis, Complex Statistics [econometrics] , Numerical Method, Clustering Analysis, System Generation-Automation , visual re-formation, Compliance and traceability effort in adding new data sources external connectors – API, Composite Visuals, infographics Unstructured text, Data Scale – Size and time Value Chain Analysis , Benchmark Data New Revenue Models Sense and response mechanisms, Simulation, optimization, Text & Analytics, Neural Networks, fractals, Actions integration - external systems. Storyboards, Virtual Reality late binding – auto discovery of structure Access to non standard data, late structure binding Real time search Data as Media like Voice, Image and Video Bigdata Management pivot based interaction – User self service Maps, Multi-dimensional Graphs,
  7. How are Businesses acquiring Analytics Inhouse / Captive Solution Utilities Services / Resources Platforms / Tools 1. A Typical Bank would have a 1Bn USD budget 2. 80% spend inhouse and in Captive 3. 1200 Person = 600Mn $ Value / 100 Mn $ Cost 4. Slow, lethargy, internal Constraints, IPR 1. Small Boutique companies getting seeded 2. Focusing on either large platforms [ splunk ] or a very specific Business use Case [ Mydrive ] 3. Scale issues, pricing, 1. Large resource houses, with 80% $ from staff Aug 2. Fragmented delivery, water fall, change is a challenge , Utilisation is key , security & leakage 3. Can Scale, some can partner, 1. Best complement to Inhouse / Captive 2. Developing the foundations for the next gen, 3. Focused more on tech rather than business 4. Partner to all above entities,
  8. Value Proposition for the Data Science Organization Building & Maintaining a Core Data Platform for Analytics: that includes setting up of specialized data marts (for pricing, reserving, etc.), identifying internal and external data sources, building connectors, integrating with internal core insurance systems and the like. Assisting in Effort Intensive, Repetitive Non-Core Analytical Activities that allow the client’s core analytical team to concentrate on modeling thus increasing core analytical bandwidth. Some activities that vendor could take over include:  Data Cleaning  Data Aggregation and Transformation  Creating Transformed Variables  Assisting in creating transformed variables  Model Validation  Checking model accuracy  Recalibrating models and reporting results Integration of Analytics with Business:  Reporting Services  Integration of Results into Core Systems  Business Process Integration  Building “Analytics as a Service” Platform Flexibility and Cost Optimization with “Lab 0n Hire” Service Model  Trained Data Scientists  Onsite-Offshore model for cost optimization  Licensing and Tool Costs spread across multiple projects  Multiple pricing options including utility-based models 1 2 3 4
  9. Delivering Analytics Value to Business Business outcome Operations Transformation Insights Data SolutionsservicesToolsPlatforms 300 400 7000 wipro Other players  CTS, TCS, Big 4, musigma TeraData Pivotal Opera Cloudera Tableau Clikview RevoR Mydrive InfoChimp 70 1200 500Bank captive
  10. Typical Analytics Practice Strategic Eco-system Alliances 1051 Analytics [ 140 – 60 USD ] BI [ 100 - 40 USD ] Data / Integration [ 100 – 30 USD] 1. 80% of the business is still Staff Augmentation 2. 80% of the business in BI / MI and low end data services.. 3. Large players like Wipro / TCS / MuSigma in the range of 5000- 10000 resources 4. Lot of SME consulting Smaller players 5. Clients are slower than the vendor.. 1. Staff Augmentation in various Skill Areas 2. Partnering and COE development for clients 3. Project based Delivery – Agile Waterfall 4. Embedded Analytics in Operations and other initiatives like Digital, mobile etc.. 5. Service Transformational Analytics – CTS 6. Very weak in industry / Business domain
  11. Industry Trend Past and Future • Rapid directionless ops growth – has helped ISV [+30% CAGR ] • Bringing structured data together • Now looking for Show and Tell + 0 consulting + More Action • Shifting Operations to Offshore – Captives • Partnerships, COE, Investments, Utilities = Value Add • BI Sophistication has kept managers in charm • Integrated solutions with Digital Initiatives • Large Data Initiatives – Lakes, Metadata, External Data • IOT / more sensors, new data • Unstructured Data, Media and therefore Big Data • Shift from Model to Compute • Specific Business Use Cases • Shift from Management to Operations and thereby Customer • Privacy and Security will be a big issue • More utilities and Plug-n-Play
  12. What to look for.. • Deep integration with a Business outcome [ MyDrive] • Show and Tell / Productized services • Eco System Partnerships • Non-Linear Scale in the Business Model • Easy to Consume, Utility, Pricing • Ability to Partner / Co-innovate • Future Proofing customers. • Agile Delivery Models • Charging and Collection Model [RDC] • Application potential across the Economy [ MyDrive] • Time to deploy and transform [ Splunk ] Business Model Factors
  13. Solution Capability Development Business Value Modeling. Analytics Program Model.. Business Value and thereby Performance Hotspots drive solutions and messages Sales & Marketing Member Mgmt & UW Provider Mgmt Claims Mgmt Customer Service Medical Mgmt Revenue - GTM Business Case Account Intel Pitch / Proposal Partnership / POC Events / ABM Engagements Quote Generation Broker Mgmt Campaig n Mgmt Market Research Member Retention 1. Brand Perception / Perf Ratio 2. Influence Ratio 3. Number of leads 4. Cost per lead 5. Medium Conversion Rate 6. Avg Premium Val 7. Days visit to purchase 8. Task Completion Rate SOLUTION CATALOG KEY OUTCOMES Key Resources Partnership Algorithm Training Research LAB/ COE Understand Business Landscape: What value is business after? Key pain points in decision making / operations Leverage Internal Capability: No duplication of work already done / capability already in existence In Sight of the Customer: Develop capability through the customer, interface, POC / Pilots Develop Ecosystem for delivery: Relationships with established & emergent OEM who will drive the market Time Bound: Ensure outcomes with time frame. 3 months to customer and 6 months to pilot Develop Systemic Solutions: Consulting to understand customer, quick entry, low change and capital…. 1 2 3 4 5 6Data Process Actions Analytics Visualization Capability Framework 1 2 3 Key principles Program Status
  14. Business Themes and Analytics COE Marketing RoI & Growth analytics Customer acquisition analytics Customer retention analytics Social media driven analytics Customer/Employee fraud & risk Competitive intelligence analytics Supply chain analytics MFG process quality & compliance Early warning analytics Asset Perf. Maint. & warranty Network analytics Service Problem Analysis Service Logistic & Resource Alloc. Governance, Risk & compliance Integrated financial perf. - EPM Store operations Analytics Merchandising & Pricing analytics Claims analytics Pre-Trade Post Trade Analytics Drug discovery analytics Post market analytics (Pharma) Care & Safety analytics Care analytics Member Retention Analytics Smart meter analytics Technology Business Automation Modeling Data Analysis Visuals Process People Methods Tools Vertical Themes Customer Lifecycle Service & Warranty GRC EPM/WIPM • Product Mgrs [10] • Clustered  Solution Themes + verticals • Teams for Verticals program mgmt • Modelers & Technologist report in. • Business Consulting • Innovation & Transformation Client Pitch / Engagement • Analytics Program Management • Long term  look at business Automation solutions • Modelers • Cluster  Solution Themes • Exploring Analysis Tools • Develop Models/Methods • # Of experiments • Play with data • Information Technologist • Cluster  1 • All Data Gather & Aggregation technologies • Solution Warranty / Scale • Speed, Variety – API • # Of experiments • Manage COEEnv. RCTG, HLS, E&U, Insurance, Securities Common + special aspects..  5PDM, expanded slowly. Telecom, RCTG, E&U, Banking, Insurance 2 PDM 1 BFSI, 1  OTH MFG, E&U, Telecom, 1 PDM  ALL BFSI 1PDM  ALL All verticals, close collab with WCS
  15. Systematic Modeling Approach to Persistency Propensity Premium Communication Strategy Customer Segments Act To neutralize the intent Collect Business need and Data Data Integration  Demographics for  Agency Information  Product Information  Pscyhographic History  Additional Sources of Data. Optimize Data  Data Analysis + Imputation  Bivariate Variable Business Objectives  Major Risks Affecting Business  Customer Segments Scope  What’s Communication Strategy Predict The potential customers Analytical Model Monitor + Feedback  Monitoring + Reports  Input feedback from operations to further fine tune the model.
  16. The Generic Analytical Modeling Process DATA COLLECTION Business Problem Definition BUSINESS PROBLEM DEFINITION DATA PREPARATION MODEL DEVELOPMENT MODEL DEPLOYMENT & MAINTENANCE Business Problem Statement Collect & Analyze Business Requirements Define Goals And Objectives FEEDBACK Define Data Requirements Identify Data Sources Unstructured, Structured, Internal & External Data Cleansing Data Aggregation Derived Variables Model Selection Build Connectors & Data Marts Data Transformation Variable Selection Modeling Alternatives Model Building Model Training Model Evaluation Pilot Implementation Model Validation Recalibration Monitoring Business Process Integration Business Processes & Systems Knowledge Data Modeling & Business Data Modeling Knowledge Intensive Core: Business Knowledge Intensive Analytical Modeling and Business Knowledge 10-20% of Total Effort 20-30% of Total Effort 25-30 % of Total Effort 5-10% of Total Effort 20-30% of Total Effort PHASESKEYACTIVITIES:CORE&NON-CORE KNOWLE DGE COST
  17. Reporting & BP IntegrationAnalytical Support Team Data Integration MODELINGINFRASTRUCTURE Internal Data [AIG] Enterprise Doc Manager Loss Notification System Claim Admin System Policy Admin System GL/Paymen t Engine Data Preparation Dashboards & Reports ANALYSISTEAM External Data Credit Records Social Networks Others Data Marts, ETL Mapping, Connectors Analytics - Structural View Core Analytical Modeling Team Generic Analytical Models Segmentation Regression Predictive Analytics Core Insurance Analytical Models Capital Adequacy Models Pricing & Rating Models Reserving Models Risk Transfer Mechanisms Modeling Foundation Data Governance Specialized Data Marts Insurance Models & Standards Data Mining Tools Modeling Repositories & Practices Fraud Models OUTCOME
  18. Interventions through Data & Analytics Data Data Quality & Cleansing Pricing & Rating Models Dashboards: Events & Triggers External Data Data Integration Services Visualization System Integration - AIG Reporting Services Reserving Models KPO / BPO Services Monitor model performance Modeling Business Services Internal Data Specialized Research Services Model Validation Unstructured Data Data - Readiness Assessments Actuarial Data Marts: Creation and maintenance Capital Adequacy Models Risk Transfer Mechanisms Model Maintenance Services for Market Research
  19. Vishwanath Ramdas Head Analytics FCC Compliance , Large MNC Bank 8 years in the industry with 17 Y experience in Business Transformation.