Big Data API’s and Analytics
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The document discusses the rise of micro applications and real-time business decisions driven by big data analytics. It emphasizes the importance of integrating micro applications rapidly through API gateways to adapt to changes on one side of an organization without needing to update thousands of devices. It also discusses different types of clouds and analytics that can be used to gain a holistic view of events and make prescriptive decisions based on predictive modeling of big data. Key questions are outlined to drive business value from integrating and analyzing diverse data sources.
Big Data API’s and Analytics
- 1. © 2012 Business Connexion Big Data API’s and Analytics Andy Brauer CTO Business Connexion
- 2. Synopsis The era of micro applications is on the rise, Realtime Business Decisions and actions are of paramount importance. Time to react to market demand is key to any organisation. Any system that delivers the ability to integrate micro applications rapidly will have a clear advantage. Integrating with existing systems is best done with API gateways which means if a change happens on one side of an organisation only the gateway needs to be adapted rather than having to update thousands or millions of devices.
- 3. How big is Big Data
- 5. It is about Service Private Cloud Public Cloud Social Cloud Hybrid Cloud Media Cloud Industrial Cloud Instrumentation cloud Personal Cloud Government Cloud Space Cloud Network Cloud Service Cloud Aggregator Cloud Military Cloud Forensic Cloud Hacker Cloud Financial Clouds Educational Clouds Sky Clouds Infra etc Analytics Web HPC Social Public Hybrid Private IAAS PAAS SAAS KAAS BUSAS
- 8. BPM is an EcoSystem
- 9. Business Intelligence has been Internal Focus Six Sigma only part of the bigger picture
- 11. “The real world is mostly event driven, and event-driven situations are best addressed by event-driven business applications”. Business Event Processing Every 20 years a new dispute innovation changes the way we do IT Sense and Respond to Actionable situations 1970s 1990s 2010s Transaction Driven Paradigm Discrete (data oriented) transaction processing) Process Driven Paradigm Orchestration (process oriented) of human and system tasks Event Driven Paradigm Holistic (context oriented) event processing Social Media reflects Events faster than Traditional Business Systems Unified Business
- 12. Business Process Decision Base
- 13. Flood of Real time information
- 18. Real Time Focus
- 19. Prevention is better than cure
- 21. The Big Picture
- 25. Business Activity Monitoring has started to include Business Analytics Prediction
- 26. Nash Equilibrium
- 27. We are aiming for Prescriptive Analytics
- 29. Key Questions to Drive Business Value from Data • What business opportunity/problem are we trying to solve? • What questions do we need to answer to solve the problem? • What data do we need to answer the questions? • What data do we have? • How can data help differentiate us in the market? • What is IP for us? Revenue generating for us? • How do we integrate the right data together? • How do we manage the quality of the data? • What data does this relate to (master data)? • Do we have all the data about this (person, event, thing, etc.)? • What are the permissible purposes of the data? (compliance, regulatory environment) • Who is allowed to access the data? Use this data?
- 30. Success measures • Information is trusted • Speed to market for new products or services is improved • Time spent looking for data is reduced • Time-to-answer ratio is inversed • Questions are answerable (e.g. What is the value of a customer?) • Stratification and insight of customer is achieved • Customer intimacy achieved • Integration and development time slashed
- 31. Key important strategy for Big Data and predictive Analytics • Digitize your processes. • Identify key data sources. • Validate your source and ensure master data can be trusted • Make use of API Gateways to gather diverse sources of key data. • Ensure you have a place to house the data. • Ensure that you have a mechanism to distil the data. • Turn the Data info Information use or develop your algorithms. • Turn the Information into knowledge use or develop your algorithms. • Turn Information in Intelligence use or developed your algorithms. • Use the system with new found wisdom.
- 33. Thank You