denodo datafest 2016: data science: operationalizing analytical models in real-time with data...
TRANSCRIPT
O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A
#DenodoDataFest
RAPID, AGILE DATA STRATEGIESFor Accelerating Analytics, Cloud, and Big Data Initiatives.
Data Science: Operationalizing Analytical Models in Real-time with Data Virtualization
Suresh Chandrasekaran
Senior Vice-President, Denodo
Agenda1.Data Science & Advanced Analytics
2.Role of Data Virtualization in 4 Phases
1. Discovery of Data Assets
2. Exploration of Analytic Models
3. Real-time Operationalization
4. Predictive Intervention & Optimization
3.Summary
4.Q&A
Data Science and Analytics Lifecycle
Data Preparation
Modeling
Evaluation
Exploratory Analytics
Business Understanding
Data Understanding
Data
Note: Adapted from CRISP-DM
Predictive Optimization
Real-time Operationalization
Definition of Data Virtualization (circa 2012) Evolution of Data Virtualization
Data Virtualization’s Growing Role
Data Virtualization
data store 1 data store 2 data store 3 data store 4
data consumer 1 data consumer 2 data consumer 3
Data virtualization is the technology that offers data consumers a unified, abstracted, and encapsulated view for querying and manipulating data stored in a heterogeneous set of data stores. - Rick van der Lans
Modern Approach (and Technology) to Enable Agile Data Access, Data Prep, Exploratory Analytics, Governed Data Services, Secure Sharing, and More … Enable Real-time and Agile IM / Analytics Lifecycle
Data
Virtualization
Self-Service Data
Data Integration
Data Governance
Single View
Data Services
“Enterprise Fast Data Strategy”
1. Connect, Discover, Prepare Data Sources and AssetsAnyone can explore any data source /asset, Get governed real-time access
100s of sources: Enterprise, Cloud, Big Data, Web, Files… +SDK
Wizards – 3 clicks to virtualize
Data Preparation & Integration with vast transform, DQ functions;
Data Modeling top-down, source-up, import, export w/ validation
Canonical entities, associations,
Publish/Access in 14 formats by any user
Business Glossary, Governed Info Self-Service, Data Services Marketplace
Find more details at: datavirtualization.bloghttp://www.datavirtualizationblog.com/?s=sources
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Connect, Virtualize w/Ease Data Preparation, Trfm, DQ
Information Self-ServiceDenodo Model Bridge
2. Exploratory Analytics, Sandboxing and CollaborationRapid Creation, Iteration and Collaboration with Virtual Analytic Sandboxes
Logical Data Views & MartsProvisioned in minutes
Analytical Functions supported via Virtual Layer
Introspect from External Tools
Collaboration – Share data, metadata and analytic model; Annotate add comments
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Logical Views Relationships
Collaboration / VersioningAnalytical Queries
3. Operationalize Across Broad Use Cases and User Types
Top 5 – Informational, Analytical, Operational, User Self-Service, Data Mgmt
Flexible Publishing data access via 12+ protocols; Support Search, Browse, Query
SQL access via JDBC, ODBC, ADO.NET; Data Services – SOAP, REST, Odata, JMS
Hybrid execution for mixed workloads -dynamic real-time query optimization; push-down analytics, caching, batch
Fine-grained access to view, row, column, cell, masking, Policy-based security
Resource manager manages workload per business priority, protects source overuse
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Find more details at: datavirtualization.bloghttp://www.datavirtualizationblog.com/data-virtualization-main-use-cases/
Streamlined Analytics in Healthcare Purchasing
Rapid Exploratory Analytics (Discovery Zone) turned into Operationalized Analytics
Embedded Analytics in Operational ScenariosSingle Customer View; Scripted Prompts Combine Multiple Analytic Insights
Single View of a Customer
Faster customer response from
6 to 4 mins with higher FCR
Calls routed based on profile
intelligence
Embed Cumulative Analytic
Insights Into Scripted Prompts
Lifetime value of customer +
Price Elasticity Retention
Satisfaction / Social
Feedback + Product
Preference Upsell
Improved customer satisfaction
4. Integrated Predictive Analytics with LDW (EDW+BigData+Cloud/SaaS+Unstructured)
High Performance Even When Processing Billions of Rows; Faster Time to Value
Integrated analytics across entire set of data & information
Move processing to the data paradigm … wherever it lives.
Common understanding of business entity & lineage – promotes trust and reuse of “single source of truth”
Find more details at: datavirtualization.bloghttp://www.datavirtualizationblog.com/physical-logical-data-warehouse-performance-numbers/
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Integrated Analytics in Healthcare Purchasing
Moving from Siloed Analytics to Integrated Analytics Across Functional & Business Lines
Big Data /IoT Predictive Analytics
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Predictive Maintenance
IoT - Big Data
Operational Data
Predictive Maintenance Service for large customers and dealers
Differentiator against low-cost competitors
Increase services and parts sales – higher margins
Future – optimize parts and services pricing
Summary: Denodo Supports Every Phase of Data Science and Analytics Lifecycle
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Exploratory Analytics
Real-time Operationalization
Data
Note: Adapted from CRISP-DM
Predictive Optimization
Understand Your Needsacross entire lifecycle of data science and analytics
Explore Denodo 6.0 capabilities as they relate to each phase
Seek Solutions Advice from Denodo experts based on successful customer implementations
Go Beyond … Experiment, Iterate, Share …fast and easy with Denodo
Thank you!
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O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A
#DenodoDataFest