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Analytics ID Consulting Services.pdf
This document discusses options and key considerations for a data and analytics initiative. It presents two typical approaches: 1) being business outcome/use case driven by identifying business objectives and questions, and 2) focusing on building a "must have" data foundation from existing internal sources. The recommendation is to start with a quick win use case that has high priority while also building the necessary data foundation. Big data technologies are discussed as well as important factors for successful implementation, namely process, people, and technology. Consulting services are outlined to assess analytics maturity, enable competencies, assess new initiatives, and monitor project implementation.
Analytics ID Consulting Services.pdf
- 1. Consulting Services for Data& Analytics Initiative Analytics ID
- 2. Agenda • Preface: Data& Analytics • Data & Analytics Initiative • Approach Options • Key Considerations • Analytics ID Consulting Services • Q&A Discussion
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- 4. Data is theNew Sun
- 5. Data & AnalyticsEcosystem Overview
- 6. Data & AnalyticsEcosystem Landscape
- 7. Present, Common StateGlobally
- 8. Data & AnalyticsInitiative Approach and Consideration
- 9. Typical Approach for Data& Analytics Initiative
- 10. A. Business Outcome/ Use Case Driven •Business Objective •Business Priority •Business Questions Identify •Resource & data readiness/availability •Existing process & any potential adjustment •Surrounding supporting Environment/Technology Consider •Multi-phase, multi-year plan: •Process update/adjustment •Resource competency enablement •Technology investment Prioritize & Execute
- 11. A. Business Outcome/ Use Case Driven • Start with identifying Business Objective and/or Business Questions that your Company would like to fulfilled/answered, prioritize it • Workshop and further assessment with related Stakeholders (Business and IT) would be the ideal approach to meet the objective from point above, result: Analytics Roadmap • Things to consider: • Business priority • Internal data readiness/availability • Required process (existing and/or enhancement to it) • Required resources and competency • Data & Analytics environment (Data Ops, Data Repository, Analytics Engine, etc.) to deliver the Use Case
- 12. B. “Must Have”Data Foundation •Existing Data Sources •“Must Have” Data Foundation •Selected Business Questions Identify •Resource & data readiness/availability •Existing process & any potential adjustment •Surrounding supporting Environment/Technology Consider •Multi-phase, multi-year plan: •Process update/adjustment •Resource competency enablement •Technology investment Prioritize & Execute
- 13. B. “Must Have”Data Foundation • Identify the “must have” data foundation from existing internal source systems, start from there • The objective: to minimize “garbage in, garbage out”, and optimize the existing data delivery/output (streamline the process, ensure business continuity, improve performance, etc.) • Usually related with Customers data (e.g. single CIF from multiple LoBs/Products as path to enabling Customer-360) • Process required: Data Ingestion, Profiling, Standardization, Matching & Deduplication, then Integrate it with transactions from multiple LoBs/Products (in Phases, business prioritization applies) • Consideration: degree of Data Validation from the Source Systems, local content (language) for Data Standardization (e.g. abbreviation, dialect/slang, common typo, etc.) • Further data engineering and preparation to deliver each specific subject areas for visualization and analytics • Single CIF -> Customer-360 -> enabling multiple Analytics Use Cases (e.g. Cross-sell, Up-sell, Omni-Channel Campaign, Path to Churn Analysis, etc.)
- 14. So, which Approach,A or B? • Which approach? Our approach recommendation is both. • Start with Quick win approach with Business Use Case (A) that has high priority • Ensure data readiness for Use Case #1 from internal data sources; build Data Foundation (B) based on it • Gradually add new data sources (internal and external) to support new use cases
- 15. Big Data Consideration Putaside the hype; start with your real needs Relational Databases • Default data warehousing tech, use SQL • Stores relational data in tables – rows/columns • Schema on write (structure when load) • For wide audience access via dashboards/reports NoSQL Data Stores • Category of DBs that are not relational • Tend to be schema-less; don’t have to specify structure of data to be stored before loading • May store data as documents – collection of key/value pairs Graph Databases • Database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data • Scales and optimize to allow faster, efficient queries and data science for relationship-based analytics Hadoop • Core parts are file system (HDFS) and processing framework (MapReduce) • Schema on read (load first, then structure) • Spark mostly use as analytics processing engine
- 16. Key Considerations for SuccessfulImplementation • Process • To have a standard operating procedure with Industry-Best Practices in place to fulfil the business objective and defined SLA • People • To get the necessary internal resources with required competency and expertise to implement the required process; administer and manage the environment • Technology • To fully leverage Features and Functions that the Technology provide for implementation and to support the operational SUCCESS Process Technology People
- 17. Process – People– Technology Objective & Priority Technology & Innovation Collaboration with the Experts Technology that fit the needs, and also ready for the future Supported by broad Community (global and local) Management Support People Enablement Certification Building Team Roles & Responsibilities Regular Check Process Policy & Process Adjustment to meet the Objective
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- 19. Consulting Services #2 –Competency Enablement #1 – Analytics Maturity Assessment #4 – Analytics Project Monitoring & Evaluation #3 – Analytics Initiative Assessment
- 20. Consulting Services #2 –Competency Enablement #1 – Analytics Maturity Assessment #4 – Analytics Project Monitoring & Evaluation #3 – Analytics Initiative Assessment Assess existing Data & Analytics Ecosystem: • Applied Use Cases • Related Data Sources • Established Process • Technology Landscape • Required Competency Output: Analytics Maturity Assessment Report • Data & Analytics Enablement (Methodology, Technology and Process Best Practices) • Data & Analytics Resource Recruitment Process (Role- Profile mapping, Competency Path, Interview) Assess new Data & Analytics Initiative: • Use Cases and its related Data Sources • Required Technology Landscape and its criteria • Required Implementation Process Output: • Analytics Initiative Assessment Report (for single phase) • Analytics Roadmap (for multi phase) • RFI/RFP document design Support throughout RFI/RFP process Monitor and evaluate Analytics Implementation Project: • Project progress and health check • Identify roadblocks and potential alternative solutions • Key takeaway and optimization/improvement recommendation for future phase Output: Analytics Implementation Project Report
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