Enterprise Analytics Strategy: Taking Business Analytics to the User
- 1. Enterprise Analytics Strategy: Taking Business Analytics to the User Ruben Mancha, PhD Assistant Professor of Information Systems Babson College March 8, 2016
- 2. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Take-home message: Taking business analytics to the user requires strategic planning and action on technologies, processes, and key indicators.
- 3. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED The Purpose of Business Analytics •Improve decision making • Strategic insight • Product and service offerings • Operational efficiencies •Enable new business models Rationalization Reengineering Paradigm shift
- 4. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Business Analytics - Requirements •Data •Volume, variety, velocity, and veracity •Internal and external to the enterprise (APIs) •Models •Problem—data—models •Technology •Landscape of analytics technologies
- 5. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Data • Variety and velocity more important than volume to improve operations and decisions • Not necessarily “big data” • Veracity is an issue (sampling) • Structured and unstructured • Challenge of making the decision of what to keep and what to discard • Adequate and clean data is expensive to obtain and costly to maintain and store long-term
- 6. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Models • Models create value (foresight) • Models incorporate assumptions in the decision-making process • Models are built in technology and business environments
- 7. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Technology • Data is stored on hardware • Data is managed by algorithms, which are constrained by the technology • User interfaces with data through technology • Technology is used to collect data (e.g., IoT)
- 8. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Landscape of Analytics Technologies (Fast evolving and incomplete…)
- 9. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Analytics Maturity – Technologies CompetitiveAdvantage Reporting Visualization (Dashboards) Diagnostic Prediction Analytics Maturity Prescription (Optimization) Cognitive Analytics Foresight Description Insight Hindsight
- 10. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Analytics Maturity – Technologies Disconnect between technical competencies and analytics solutions • Data Scientists: 0.1% • Analysts: ~ 3% • Business User: 97% Automation QlikView
- 11. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Analytics Maturity – Data ManagementPotentialforCompetitiveAdvantage* Data Management Enabled Analytics Maturity Structured Data Structured + Unstructured Data RDBMS (SQL) DW Spread- mart Online Transaction Processing NoSQL • Performance • Scalability • Cost per GB Data Lakes *GIGO Parallel NoSQL + Analytics Operations & Reporting Analytics
- 12. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Analytics Maturity – Data ManagementStoragePerformance Volume of Data NoSQL Database Relational Database
- 13. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Analytics Maturity – Data Management XML JSON Application Program Interface (API) NoSQL Frameworks Parallel Data Processing and Distributed File Systems with Analytics Platforms HD File System IaaS Analytics as a Service
- 14. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Yes to Business Analytics, but how? Key Performance Indicators (KPIs) Business Processes Business Goals Enterprise Strategy Business Analytics Strategy Data Models Technology
- 15. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED “Analytics technologies are useless. They can only give answers” Adapted from Pablo Picasso “If you can’t measure it, you can’t manage it.” “People don’t want to buy a quarter-inch drill. They want a quarter-inch hole” Theodore Levitt
- 16. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED The Last Mile of Business Analytics Strategy We have business goals, we have identified relevant data, and we have formulated appropriate models; how do we realize value? •The last mile of business analytics transformation requires the alignment of goals, data, and models with business processes, technology and key performance indicators •Complementary assets must be in place
- 17. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED The Last Mile of Business Analytics Strategy - Goals - Data - Models - Business Processes - Technology - Key Performance Indicators Network of Complementary Assets: - Organizational culture and structures - Governance, security and ethics - Analytics acumen (digital innovators) - Skills: technical, communication, etc. - Infrastructure
- 18. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED The Last Mile of Business Analytics Strategy Key Performance Indicators (KPIs) Business Processes Business Goals Data Models Technology Business Analytics Last Mile:
- 19. © 2016 RUBEN MANCHA – ALL RIGHTS RESERVED Thank you. @RubenMMancha