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Data Mashups for Analytics


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Explore how data integration (or “mashups”) can maximize analytic value and help business teams create streamlined data pipelines that enables ad-hoc analytic inquiries. You’ll learn why businesses increasingly focused on blending data on demand and at the source, the concrete analytic advantages that this approach delivers, and the type of architectures required for delivering trusted, blended data. We provide a checklist to assess your data integration needs and capabilities, and review some real-world examples of how blending various data types has created significant analytic value and concrete business impact.

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Data Mashups for Analytics

  1. 1. Data Mashups for Analytics Bringing Everything Together for Actionable Insights Ben Hopkins Sr. Product Marketing Manager, Pentaho
  2. 2. We Enable the Modern Data-driven Business Modern, Cohesive Business Analytics and Data Integration Platform • Full spectrum of analytics for all key roles • Embeddable, cloud-ready analytics • Broadest and deepest big data integration Innovation Through an Open Heritage • Open, pluggable, purpose-built for the future • Sustained leadership in big data ecosystem Business Momentum • Over 1,500 commercial customers • Over 10,000 production deployments
  3. 3. Agenda ① Background ② Approaches to Data Blending ③ The Role of Data Integration ④ Real World Examples & Success
  4. 4. Background Much of the value from big data will come from “mashing up” proprietary data with external and open data. McKinsey Global Institute 10 IT-enabled Business Trends for the Decade Ahead, 2013
  5. 5. Poll Results from
  6. 6. Poll Results from
  7. 7. Poll Results from
  8. 8. Background Proportion Utilizing Unstructured Data From: Social Media: 66% Internet of Things: 65% Mobile Device Data: 58% “When individual sources include automated and/or manual inputs, originate from disparate systems with different architectures, and are subject to different levels of governance, an effective integration process is essential.” From “Delivering Governed Data For Analytics At Scale,” Forrester Consulting, 2015
  9. 9. The most powerful insights come from blending data on demand and at the source
  10. 10. On Demand and At the Source Architected & Trusted Approach • Designed with full knowledge of underlying systems and constraints • Utilize most efficient point of processing • Provide fast access, avoid unnecessary staging • Maintains governance rules • Preserve semantics, auditability
  11. 11. Where Does Data Integration Add Value? Business Intelligence and Data Warehousing “Effective decisions depend on aggregated, calculated, and time-series data values in a DW – data and data structure that wouldn’t exist without data integration” Builds New and Valuable Data Sets “Similar to a value-adding process in manufacturing, DI collects raw material (data from sources systems) and assembles it into a product (new data sets)” 360-Degree Views of Business Entities “Success in sales and service often depends on complete views of each customer, which are typically assembled with data integration tools and techniques” From “Ten Ways Data Integration Provides Business Value,” Philip Russom, TDWI, 2011
  12. 12. Data Readiness Checklist Do I Need Data Integration Capabilities? 1. Do I need to blend several different data sources? 2. Is my data cleansed and modeled? 3. Do I want to enrich my data with new data sources? 4. Have I already captured all the data I need? 5. Will my data sources change in 6, 12, or 18 months? 6. Do I need ad-hoc and drill-down analytic capabilities? All Signs Point to Data Integration
  13. 13. Data Blending Examples & Success Stories
  14. 14. Blending Web Analytics and Support Data Business Question: Am I supporting all of the right browsers for my web app? Blended by region and browser Software Product Manager Google Analytics web visits via API Flat file of historical product support requests Android visits, but we don’t support yet
  15. 15. Blending Machine and Production Data Business Question: What facility temperature is optimal for manufacturing output? HVAC sensor data in Hadoop accessed via Hive Production quotas and actuals from data warehouse Blended by facility and time Operations Manager Cold temperature ranges associated with higher production across almost all facilities See detailed mashup videos:
  16. 16. Caterpillar Delivering a 360-Degree View of Equipment Business Challenge • Identify opportunities for maintenance and fuel savings in industrial equipment operations • Predict equipment breakdowns to avoid downtime • Extend fleet-level insights to equipment operators
  17. 17. Caterpillar Delivering a 360-Degree View of Equipment Pentaho Benefits • Blend sensor data with customer data and more into unified analytics service • Operationalize predictive ‘useful life’ models in the data workflow • Provide a revenue-generating offering to customers that drives substantial fuel and maintenance savings
  18. 18. Entity 360 Marine Asset Intelligence Business User (COO) Reporting on Operations and Efficiency End Users Dashboards and Reports on Machine Performance Business Analytics Server Data Marts Data Scientist Data Mining and Predictive Data Governance Local Machine and Server Data Fleet Data via Satellite Cross Department Operations Data Data Integration Data Integration
  19. 19. British Telecom Protecting Against Cyber Threats Business Challenge • Launch new service to market: BT Assure Cyber, an enterprise solution for cyber security insights across many data types • Previously BT Assure Cyber could only integrate relational data sources and not big data sources
  20. 20. British Telecom Protecting Against Cyber Threats Pentaho Benefits • Native support for Hadoop in an enterprise environment • Ability to integrate telemetry data from sensors, security controls and advanced detection tools • Reduced detection time of cyber threats from weeks to seconds
  21. 21. In Closing Next Steps  Explore more mashup examples:  Take a look at Pentaho in the 2016 Gartner Business Analytics Magic Quadrant Key Takeaways  Teams are taking “data mashups” to new heights  Blend data on demand and at the source  Data integration can maximize analytic value
  22. 22. Questions and Discussion
  23. 23. Thank You