Bringing Data to the Business in a BIG Way Tasso Argyros, Co-President and Founder Teradata Aster
May 10, 2015
Bringing Data to the Business in a BIG Way
Tasso Argyros, Co-President and Founder Teradata Aster
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“Enterprises often address new information management challenges with one-off solutions, and the big-data challenge could unfortunately follow the same pattern.”
Gartner, April 2011
Is Big Data for Business?
“By 2017 the CMO will be spending more on IT than the CIO.”
Gartner, Jan 2012
“Barnes & Noble has made a profound transformation from being a physical seller of books to a digital technology company. A key component of that is the ability we have gained to leverage 'big data' to derive consumer insights that are deployed multi-channel for better personalization.”
Marc Parrish, VP, Customer Retention, Barnes & Noble
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What is New About Big Data?
Source: CEO Advisory: ‘Big Data’ Equals Big Opportunity, Gartner, 31 March 2011.
Size Data Analytics Big
Data
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What Else is New About Big Data?
“Capture only what’s needed”
IT delivers a platform for storing, refining, and
analyzing all data sources Business explores data for questions worth answering
Big Data Analytics Multi-structured & Iterative Analysis
IT structures the data to answer those questions
Business determines what questions to ask
Classic BI Structured & Repeatable Analysis
“Capture only what’s needed”
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Cost and Complexity Kill Enterprise Adoption
Challenges with Emerging Big Data Technologies
Cost Complexity
Enterprise Adoption
“Sophisticated tools for data integration and analysis on this scale are largely lacking today. There are opportunities to create tools and applications for Big Data.”
- IDC Market Analysis, Worldwide Big Data Technology and Services, 20102-2012 Forecast
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Is Big Data Solving Your Problems Today? 3 Key Questions to Ask your CIO
How many people in your organization can directly ask Big Data questions? 1
How much time does it take to answer a new business question with Big Data? 2
Is it hard to find the right people and budget to tackle new Big Data problems? 3
Need right technologies to realize business value of Big Data
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Making Big Analytics Work: The Discovery Cycle Most Effective Way to Create Business Insight from Big Data
Analytical Idea
Evaluate Results
Zero-ETL Data Load/Integration
SQL & non-SQL Analysis
Operational DB
or EDW Operationalize
or Move On
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Key Requirements of a Discovery Platform
Highly Efficient & Performant Big Data Platform That Allows Quick Iterations 1
Hybrid Capabilities that Provide both Legacy (SQL, BI) and New (MapReduce) Interfaces 2
Significant Out-of-the-Box Analytical Apps that Minimize Development 3
Democratize Big Data & Maximize Enterprise Adoption
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Your Analytic & Advanced Reporting Applications
Store
Process
Rapid Analytics Development
Embedded Analytic Processing
Massively Parallel Data Storage
• Commodity-hardware based • Software-only; Appliance; Cloud • Fault-tolerant & one-click expansion
• SQL-MapReduce framework • Analyze both non-relational +
relational data • Top performance for both SQL & MR
• 50+ pre-built analytical apps • Visual IDE: custom apps in hours • Several programming languages
Analysts Data Scientists Business Users Customers
Develop
Aster’s Discovery Platform Democratizes Big Data
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SQL & MapReduce: the Gap
But what if you need both?
• Easy and Fast • Integrates well
with BI/Viz. tools
• But…not always powerful enough
SQL • Powerful, but… • Batch-oriented • Requires lots of
coding
MapReduce
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Filling the Gap: SQL-MapReduce®
Industry’s Only Standard SQL + MR Combination
Standard SQL Support for Business Analysts Integrates Seamlessly with Most BI Tools 10x MapReduce Performance Advantage
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Some of the 50 out-of-the-box analytical apps
Analytic Foundation: the App Store of Big Data
Path Analysis Discover patterns in rows of sequential data
Text Analysis Derive patterns and extract features in textual data
Statistical Analysis High-performance processing of common statistical calculations
Segmentation Discover natural groupings of data points
Marketing Analytics Analyze customer interactions to optimize marketing decisions
Data Transformation Transform data for more advanced analysis
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How SQL-MapReduce® Connects Business Analysts To MapReduce Processing
Invoke Pre-Bulid SQL-MapReduce® App Through SQL and Visualize Directly in Tableau®
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Beyond the Discovery Platform: Unified Big Data Architecture Enabling All Users for Any Data Type from Data Capture to Analysis
Discover and Explore Reporting and Execution in the Enterprise
Capture, Store and Refine
Audio/ Video Images Docs Text Web &
Social Machine
Logs CRM SCM ERP
Java, C/C++, Pig, Python, R, SAS, SQL, Excel, BI, Visualization, etc.
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ANALYTICS
Unified Big Data Architecture for the Enterprise
Discovery Platform Active Data Warehouse
Audio/ Video Images Text Web &
Social Machine
Logs CRM SCM ERP
Engineers Business Analysts Quants Data Scientists
Java, C/C++, Pig, Python, R, SAS, SQL, Excel, BI, Visualization, etc.
Capture, Store, Refine
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Aster SQL-H™ A Business User’s Bridge to Analyze Hadoop Data
Aster SQL-H gives analysts and data scientists a better way to analyze data stored cheaply in Hadoop
• Allow standard ANSI SQL to Hadoop data
• Leverage existing BI tool investments
• Enable 50+ prebuilt SQL-MapReduce Apps and IDE
• Lower costs by making data analysts self-sufficient
Announced June 12th, 2012
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Cross-Channel Customer Interactions 17,000 Customers, 1 Month
Challenge • Know the “last mile” of a decision • Data Mining tools predict probability
but do not ID the “last mile”
With Aster • SQL-MapReduce listens and predicts
the “last mile” - Identifies all interaction patterns
prior to acquisition or attrition
Business Impact • 10-300x less effort to pinpoint a
customer in the “last mile”
Aster in Retail Banking: “Last Mile” Marketing
92k Online Sessions
25k ATM Sessions 34k Branch Visits
5,000 Call Center Sessions
43k E-mails
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Aster MapReduce: Understanding the “Last Mile”
What if I knew that this customer was likely to leave? I could… • Apologize • Offer an explanation • Reverse the $5 fee
“It takes 3x more to acquire a customer than to retain one”
Jan 5: Reverse Fee Request Jan 10: Request Made Again
Jan 15: Request Made Again Jan 7: Request Made Again
Jan 20: Account Closed
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Multi-Channel Customer Analysis Iterative Discovery Analytics
STORE DATA
Business Question(s): • Is there any identifiable pattern of behavior prior to account closure? • Prior to new product additions? • If so, what does this pattern look like?
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Events Preceding Account Closure
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Events Preceding Account Closure
SELECT * FROM npath ( ON ( SELECT … WHERE u.event_description IN ( SELECT aper.event FROM attrition_paths_event_rank aper ORDER BY aper.count DESC LIMIT 10) ) … PATTERN ('(OTHER|EVENT){1,20}$') SYMBOLS (…) RESULT (…) ) ) n;
Interactive Analytics
Reducing the “Noise” to find the “Signal”
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Events Preceding Account Closure
SELECT * FROM nPath ( ON (…) PARTITION BY sba_id ORDER BY datestamp MODE (NONOVERLAPPING) PATTERN ('(OTHER_EVENT|FEE_EVENT)+') SYMBOLS ( event LIKE '%REVERSE FEE%' AS FEE_EVENT, event NOT LIKE '%REVERSE FEE%' AS OTHER_EVENT) RESULT (…) ) n;
Closed Accounts
Fee reversal seems to be a “Signal”
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Collect & analyze not only customer transactions, but also customer interactions
Use SQL-MapReduce pre-built operators to identify behavioral patterns and uncover business insight
Utilize standard BI tools to ensure insights are consumed by the right business analysts and acted upon
Big Data for Business = use more data and more analytics to achieve a competitive edge
How did Big Data Help?
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Summary
1 Huge Business Value in Big Data
Paradigms Like Discovery Platform Reduce Cost, Time-to-Market and Maximize Adoption
Teradata Aster Enables Business Users to Access Directly Big Data Technologies