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”

Role of Data Virtualization in Analytics Lifecycle

Discover, Explore, Operationalize, Optimize

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

Virtual Data Layer->Data Services->Marketplace

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

Overall Business Information Solution & Sandbox

From Analytic Sandbox to Operational Reporting

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

Q&A

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