The Briefing Room with Richard Hackathorn and Teradata Live Webcast March 25, 2015 Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2 Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement. Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include: Joining sensor data in Hadoop with data warehouse labor schedules in seconds How bridging corporate cultures and systems creates new business opportunities The 360 view of customer journeys using weblogs in Hadoop via BI tools How can you put the data where you want and query it however you want Virtualizing Hadoop data with Teradata QueryGrid Visit InsideAnalysis.com for more information.

1. Grab some
coffee and
enjoy the
pre-show
banter before
the top of the
hour!

2. The Briefing Room
Achieving Business Value by Fusing Hadoop and Corporate Data

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Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh

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Reveal the essential characteristics of enterprise
software, good and bad
Provide a forum for detailed analysis of today s innovative
technologies
Give vendors a chance to explain their product to savvy
analysts
Allow audience members to pose serious questions... and
get answers!
Mission

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Topics
March: BI/ANALYTICS
April: BIG DATA
May: CLOUD

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Data Gravity
Ø Moving data is problematic
Ø Data likes to stay where it is
Ø Access methods are improving
Ø We live in an increasingly multipolar world!

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Teradata
Teradata is known for its analytics data solutions with a
focus on integrated data warehousing, big data analytics
and business applications
It offers a broad suite of technology platforms and solutions
and a wide range of data management applications
Last year, Teradata announced QueryGrid, a data access
layer that can perform analytics across multiple databases
and Hadoop

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Guests
Dan Graham
Technical Marketing Director
Teradata
Dr. Robin Bloor
Chief Analyst
The Bloor Group
Dr. Richard Hackathorn
Industry Analyst
Bolder Technologies

9. 9
​ Teradata QueryGrid Use Cases
​ Dr. Richard Hackathorn – Bolder Technology
​ Dan Graham, Teradata
​ March 25, 2015

10. 10
• Based on technical interviews
• QueryGrid concepts
• Customer success stories
– Teradata to Hadoop
– Aster to Hadoop
– Aster to both
• Themes across multiple
customers
QueryGrid Successes Agenda

11. 11
​ Three-Peer Platform Ecosystem
• IDW = structured system of
reference data widely shared
• Discovery = research and
exploration
• Data = curate new, changing
data sources
Data
Scientists
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
ASTER
DATABASE
DISCOVERY
PLATFORM
DATA
PLATFORM
HADOOP
Business
Analysts
Casual
Users
Programmers

12. 12
© 2015 Teradata
Teradata and Aster: QueryGrid
IDW
TERADATA
DATABASE
Discovery
ASTER
DATABASE
Business users Data scientists
TERADATA
ASTER
SQL,
SQL-MR,
SQL-GR
OTHER
DATABASES
Oracle,
MongoDB
Teradata
Systems
TERADATA
DATABASEHADOOP
Push-down
to Hadoop
SAS, Perl,
Python,
Ruby, R
LANGUAGES

13. 13
QueryGrid
Data Lake/DataHubData Warehouse
SELECT TDP.Prod_ID
,TDP.Prod_Name
,HW.Sensor_Mfr
,HW.Sensor_ID
FROM Sensor_data@HW_Hadoop
,TD_Products TDP
WHERE TDP.Sensor_ID =
HW.Sensor_ID;
Sensor_data@HW_Hadoop

14. 14
QueryGrid Use Cases
Company Use Case Maturity
Vehicle Manufacturer
Identifying maintenance targets Daily use
Detecting unnecessary maintenance Prototype
Communications Improving customer retention Daily use
Financial Services
Monitoring brokerage compliance Production
Processing weblog sessionization Prototype
Travel Services
Conversion funnel using website logs Daily use
Conversion funnel using IVR call center logs Prototype
Improving website design with A/B testing Daily use
Computer
Manufacturer
Generating leads from customer journey Daily use
Telecommunications
Reducing customer churn Daily use
Customer satisfaction call center dashboard Prototype
eCommerce Improving website searching Production
Financial Systems
Mfg.
Reducing travel costs Production
Electronic
Manufacturer
Monitoring process quality control Prototype

15. 15
​ Vehicle Manufacturer
​ Problem
• Predicting machine repairs using sensors
• Business and Engineers disconnected
​ Challenge
• Get more value from the sensor data
• How to get data to SQL tools and users?
​ Results
• QueryGrid provides the bridge between
cultures – now they collaborate!
DATA
PLATFORM
HADOOP
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Business
Analysts
Engineers &
Programmers
Query Grid

16. 16
​ Financial Services
​ Problem
• Brokers overstating financial returns
• Government audits and $$$ penalties
​ Challenge
• Text analysis of Outlook emails
• Investigate possible violations
• Sessionize interactions
​ Results
• 50% reduction in false positives
• Reduced labor reading emails
ASTER
DATABASE
DISCOVERY
PLATFORM
DATA
PLATFORM
HADOOP
Business
Analysts
Query Grid
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Business
Analysts

17. 17
​ Travel Services
​ Problem
• Conversion funnel of lookers to bookers
• How best to spend Marketing funds?
​ Challenge
• Correlate weblogs and call center IVRs
​ Results
• Data placement on Hadoop
– Sessionize consumer interactions
• Calculating A/B testing to increase sales
DATA
PLATFORM
HADOOP
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Business
Analysts
Programmers
Query Grid

18. 18
​ Computer Manufacturer
​ Problem
• Track customer website journey
​ Challenge
• Hadoop security limitations
• “Everybody has BI tools”
– No Java or Map reduce skills
​ Results
• Identifies 66% of visitors, up from 33%
– Enhances propensity models
– Improved marketing campaigns
• Batch scoring every 2-3 months
à near real time
ASTER
DATABASE
DISCOVERY
PLATFORM
DATA
PLATFORM
HADOOP
Business
Analysts
Data
Scientists
Query Grid

19. 19
​ Telecommunications
​ Problem
• What are the steps to canceling service?
How many steps until they cancel?
• What step is the customer in right now?
​ Challenge
• Analyzing 20+ data event streams
​ Results
• Call center 3600 customer dashboard
– Less churn and termination fees
• 3-6 month projects now done in 3 days
ASTER
DATABASE
DISCOVERY
PLATFORM
DATA
PLATFORM
HADOOP
Business
Analysts
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Business
Analysts
QueryGrid
Query Grid

20. 20
​ eCommerce
​ Problem
• Improving website search for online customers
​ Challenge
• Using Hadoop for text mining of EDW data
• Bring analytic results back into EDW
​ Results
• Allow analysts to do the things they are best at
on their preferred platform
ASTER
DATABASE
DISCOVERY
PLATFORM
DATA
PLATFORM
HADOOP
Online
Customers
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Business
Analysts
QueryGrid
Query Grid

21. 21
​ Financial Systems Provider
​ Problem
• Containment of employee travel costs
​ Challenge
• How to correlate phone calls, travel, and
Webex use by employee?
​ Results
• Training classes for high travel low Webex
use employees drops travel costs
• Also condition based maintenance
analysis of installed machines
ASTER
DATABASE
DISCOVERY
PLATFORM
DATA
PLATFORM
HADOOP
Business
Analysts
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Data
Scientist
QueryGrid
Query Grid

22. 22
​ Electronics Manufacturer
​ Problem
• Yield management, manufacturing
equipment fault detection
• Slow identification of root cause and fix
​ Challenge
• Huge sensor data volume à Hadoop
• Difficult to get Hadoop reports to users
​ Results
• Protoype à production in 2015
– Unit level traceability
– Quality root cause analysis
DATA
PLATFORM
HADOOP
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Engineers,
Plant workers
Finance,
& Analysts
Programmers
Query Grid

23. 23
• QueryGrid provides bridges between cultures and
systems
• QueryGrid expands choices on data placement
• Marrying new data with the data warehouse
generates biz value
• Understanding event sequences leads to killer apps
• High speed parallel data exchange enables
business innovations
• Conclusion: Enterprise analytics at scale requires
an integrated information ecosystem.
Recurring Themes

24. 24
24
The End

25. 25
QueryGrid Connectivity Usage
Industry Teradata Aster Hadoop Usage
Vehicle Manufacturer X X Bridging cultures
Communications Provider X X Analytic Workflow
Financial Services X X Compliance/Security
Travel Services X X Parallel Streams
Computer Manufacturer X X Precision Views
Telecommunications X X X Massive Discovery Lab
eCommerce Provider X X Website Search
Financial Systems Provider X X X Travel versus WebEx
Electronics Manufacturer X X Process Control

26. 26
Teradata Unified Data Architecture: QueryGrid Layer
TERADATA OR ASTER DATABASE
Marketing
Executives
Operational
Systems
Customers
& Partners
Frontline
Workers
Business
Analysts
Data
Scientists
Engineers &
Programmers
TERADATA QUERYGRID ACCESS LAYER
SQL-H SQL SQL, NOSQL
Teradata Unified Data Architecture
DATA
PLATFORM
HADOOP OR
TERADATA
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
ASTER
DATABASE
DISCOVERY
PLATFORM
COMPUTE
CLUSTERS
SAS, PYTHON,
R, PERL, RUBY…
OTHER
DATABASES
ORACLE,
MONGODB, ETC
SQL VARIOUS

27. 27
QueryGrid Use: BI to Hadoop
• Business users
– No Hadoop skills
– No programmers to help
– Want self service query and reporting
– And two kinds of data
- Raw and integrated
• Solution: BI Toolsà QueryGrid
– Sometimes easy is all we need DATA
PLATFORM
HADOOP
DATA
WAREHOUSE
TERADATA
DATABASE

28. 28
​ Communication Provider
​ Problem
• Churn analysis of consumer journey
• Enormous data volume
​ Challenge
• SQL not for event sequence analysis
• Analysts use SQL, can’t write Java
​ Results
• Aster direct Hadoop access + nPath
simplifies customer journey analysis
ASTER
DATABASE
DISCOVERY
PLATFORM
DATA
PLATFORM
HADOOP
Business
Analysts
INTEGRATED DATA
WAREHOUSE
TERADATA
DATABASE
Business
Analysts
Query Grid

29. Robin Bloor, PhD

30. § Inexpensive (?)
§ Any data
§ May have metadata
§ Poor performance
§ Weak scheduling
§ Weak data mgmt
§ Security?
§ Data lake
§ Expensive
§ Prepared data
§ Will have metadata
§ Optimized performance
§ Optimized scheduling
§ Good data mgmt
§ Secure
§ Data workhorse
Hadoop vs. Data Mgmt Engine
Hadoop DBMS/EDW

31. Big Data Architecture - 1
Think Logical, Implement Physical

32. Big Data Architecture - 2

33. Big Data Architecture - 3

34. Data Access
In our view Hadoop has become
strategic
As the Enterprise Staging Area
for Data
And as an Enterprise File System

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April: BIG DATA
May: CLOUD

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ATTENTION!
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