Getting your data to “play nice with others” using

Alteryx and the Teradata Unified Data Architecture

Bruce Johnson, Teradata

Jim Schattin, Alteryx

Technology track

March 7, 2013

1:15 – 2:00 pm

Person with the hose?

Person trying to get the hose?

Dog waiting for the bath?

OR

The Data

The Analyst

The Problem to be solved

Which one are you?

Data (Big & Small) Provides Sources For Insight

• Organizations compete on analytics

So are the challenges to figure out how to best enable this capability

Why?

• Narrowly focused analytics against

narrow data sets produce narrow insights

• Enriching and putting all data to work

makes for smarter decisions and data-

driven business models

• Opportunities for organizations to

benefit from analytics across more of

their data is greater than ever before…

• Organizations are not making use of all the

data they possess NOW!

• Undiscovered nuggets of information about

customers, products and performance

hidden in hard to reach places – e.g., ERP,

legacy systems, web logs, social media

Challenge = Opportunity

Data should be easily accessible and usable

Platforms and tools should be flexible and scalable

Business should be able to “ask any question at any time”

Need to understand the role of new technologies (e.g., Hadoop, MapReduce)

Need to understand the value in Big Data, how it can improve what we know today!

• Teradata’s response to this opportunity is

the Unified Data Architecture™ (UDA) to

deploy available technologies to unleash

the value of data

• Creates a strong analytic foundation by

embracing existing, new and emerging

technologies in a cohesive manner

Take advantage of it!

• Manage all the data with workload specific engines and a consistent set of tools

> Apply the right technology to the right analytical opportunities

> Isolate intelligent signals in a world of noise

> Turn invisible opportunities into actionable decisions

Three capabilities working in conjunction with one another…

• Teradata Data Warehousing: Integrated and shared data environment serves as the foundation

for any analytic environment, provides a single source of centralized data for reuse, delivers

strategic and operational analytics to the extended organization

• Aster Data Discovery: Pre-packaged SQL-MapReduce capabilities for data-driven discovery,

helping unlock insights from big data, performed with a technology with rapid exploration

abilities via a variety of analytic techniques and accessible by mainstream business analysts

• Hadoop Data Staging: Preparation for analytics in a low-cost

technology proven to be very effective for loading, storing

and refining data

Data, answer-sets, and insights passed seamlessly among

the architecture capabilities

Synchronized components with transparent management,

access and analysis of all of the data

Teradata Unified Data Architecture™

Teradata Unified Data Architecture™

ADVANCED ANALYTICS

Enable any analysis against any type or volume of data at any time…

Discovery and

exploration platform

that enables agility

with limited

constraints

Data warehouse for

insight deployment

into a reliable

production

environment…

So you’ve got big and little data, a discovery

platform and a flexible architecture…. Now what?

Analytics Ecosystem Strategy That Fits

Business Users Executives Analysts Data Scientists

NUMBER OF USERS

Str

ate

gic

Goals

and

Init

iati

ves

Feedback

KPIs

Insight

Opportunity

Advanced Analytics

EDW

Guided Analytics

Guided Analysis

CO

MPLEXIT

Y

Low

H

igh

Low High

Analytics Lab

Develop Analytics to Solve Business Problems…

Statistics

Forecasting

Geospatial Graph Analysis

Augment traditional analytic

approaches

with new approaches

Text Analysis

… And Make Your New Insights Operational

Marketing Service Provider Digital Marketing Attribution

• Benefits:

- Marketing analysts more productive with Aster

- Lower cost - storage and batch refining done on

Raw Web Logs

Analytic Tools

Teradata Aster

Co

ok

ie-

level d

ata

Arc

hiv

al

Hadoop (on AWS) (Storage, aggregations,

cleansing)

Ad Server Logs

Media Data (Aggregated)

Custom Data by Client

• Segmentation:

Custom SQL-MR algorithms to match

and create centralized identifiers

• Sessionize by client

• nPath identifies segment path analysis

(behavior after ads)

Big Box Grocer – Initial Use Case Affinity Analysis

What is the customer

buying in key categories?

Does this affect other

categories and how?

Analytics Architecture

Teradata

Aster Lab/Test

Teradata EDW Prod

Hadoop Store/Cleanse

• Reporting

• 2 – 3yrs data

• Productionization

• Prototyping

• Fail Fast

• Analytics Lab

• Aggregations / ETL /API • Data Hub – Active Archive • Cleansing Multi-Structured

Guided Analytics

Layer Prototyping Layer Application Layer

Reports Mobile Analyst Data Scientist Social

Data

base

Layer

Data

Layer

Enterprise Systems Transactional Data Documents Social/Text/Log Audio Images IT/OT Video

Affinity Analysis

Current Method UDA Method

Tool SQL SQL-MapReduce

(Collaborative Filter operator)

Dataset Time Span 13 weeks 8 years (32X time span)

Affinity Calculation One category against

others

All Categories vs. all others

Calculation Time 4 hours 48 Minutes

2.4 Minutes - same calculations against same data

Affinity Analysis: Shelf Stable Juice Seasonal Affinity with Other Categories

0

0.005

0.01

0.015

0.02

0.025

1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7

2004 2005 2006 2007 2008 2009 2010 2011

Collab.

Score

Year/Month

Alcohol

Cereal

Frozen - Ice Cream

Laundry Detergent

Other Cheese

Paper Towels

Pizza

Shredded Cheese

Sliced Cheese

String Cheese

Affinity Analysis:

Analyzing Affinity of items over a long duration (6-10yrs) will provide key insights into running better promotions,

planogram and price planning using affinity of items. Affinity Analysis on 8 Years of Data for All Categories against All Other Categories

Consumer Migration:

Analyzing declines in consumer segments over large timeframes. Determine the items missing from declining baskets and why

How much time best (Platinum, Gold) consumer is spending in different segments before becoming unengaged?

Pricing Affinity:

Analyzing item price movement and its impact on basket size and affinity of items over a long duration (6 years). Determine individual and multiple item their price movement impact on total basket

Competitor Impact:

Analysis of various competitor impacts over time Understand impact of competitor store opening on basket size and consumer loyalty (trips per month)

Determine if the effects are temporary or permanent

Social Media :

Integrating consumer online data (Social Media - Facebook) with existing transaction data to understand loyalty Understand the number of fans by demographic

Understand social media behaviors of best consumers (Platinum and Gold)

Differences in behavior of consumers in categories who are Facebook fans versus non-Facebook fans

Retail Use Cases

• Understanding Customer Service Interactions key to product ‘fit’

• Churn, Adoption, Attrition, Path to product cross sell

• Combine Check Image, Voice, Social, Web, Transaction data

• Web Analytics (90% of data)

• Combine and Sessionize HTTP raw, HTTPS and XML Application logs

• Find Golden path to Application Submit

• Execution requires discovery across multiple channels to see patterns and paths in

data to use as new variables for propensity scoring

• 4 steps

1) Customerization (ID the custom in data)

2) Sessionization

3) Sequencing Analytics (Discover behavior across a period of time and all channels)

4) Productionize in Models, Events and Campaigns

Financial Services Use Cases

Financial Services - Churn Prevention Hadoop captures,

stores and transforms social,

images and call records

Aster does path and sentiment analysis

with multi-structured data

Data Sources

Multi-Structured Data

Call Center Voice Records

Check Images

Traditional Data Flow

Analysis +

Marketing

Automation

(Customer

Retention

Campaign)

Capture, Retain and

Refine Layer

ETL Tools

Hadoop

Call Data

Check Data

Teradata

Integrated DW

Dim

en

sio

na

l

Data

Analy

tic R

esu

lts Aster Discovery

Platform Sentiment

Scores

SOCIAL

FEEDS

CLICKSTREAM

DATA

BRANCH, ATM

DATA

Email/Survey Data

Events Preceding Account Closure

Interactive Analytics

Reducing the “Noise” to find the “Signal”

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;

Events Preceding Account Closure

Events Preceding Account Closure

Closed Accounts

Fee Reversal Seems to Be a “Signal”

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;

Paths to Attrition (Version 2)

Multiple Fee Reversal and Viewing Product/Rates and

Offers happens in the last mile for Account Closure

Telco Use Case with Teradata UDA and Alteryx Problem A Global Communication Service Provider is interested in preventing customer churn by identifying at-risk customers and then

providing special offers that reduce the likelihood of churn in a profitable way. This requires use of predictive analytics.

Joint Solution Alteryx loads call records of customers that churned over the last 5 years into Aster to identify a golden path

Alteryx moves output into a Teradata Data Lab to combine with customer data from Teradata DW to drive in-database analytics

Alteryx performs detailed geospatial engineering/network analysis and then provides to Business Analysts for review

Results • Ability to identify key customers that are likely churn candidates

• Visualization of problem spots on the network (cell sites, network elements, ...) that are driving churn

• Understanding of other key reasons for churn – performance, competitive offers, ...

• See what offers have avoided churn by similar customers in the past

• Able to identify which offers will work and to evaluate a least cost offer to prevent churn

• The ability to make offers to keep customers from churning

• Deeper understanding of customer behavior

Perform predictive analytics to identify customers most likely to churn

Retail Use Case with Teradata UDA and Alteryx Problem Improving customer insight by identifying customers most likely to shop at a competitor, to drive better marketing campaigns,

to bring social media analytics into decision making, and to get a 360 degree view of product demand requires use of a strong

analytics solution that is accessible to business users.

Joint Solution Alteryx loads customer purchase history into Aster to identify golden paths for purchase and churn

Alteryx moves output into a Teradata Data Lab to combine with customer data from Teradata DW to drive in-database analytics

Results enhanced by applying Alteryx Drive Time analysis to understand which customers drive by competitors to shop them

Results • Ability to identify key customers that are likely churn candidates

• Gain customer insight and identify key attributes for purchase

• Combine ratings, reviews, mobile, and interaction data, and apply predictive model clustering, to determine products

with the most and least demand

• Integrate social media content to enable business units to understand market perception and to analyze sentiment

values in decision making

• Integrate drive time geospatial with data such as weather, sensor, economic, competitive, traffic, logistics and other

data sources to improve labor costs and maximize customer service

Enable business users to access a single analytics platform for a variety of requirements

E-MAIL CUSTOMER

SEGMENTS COMPETITOR ON-LINE STORE DATA IRI / NIELSEN PRICING

Affinity analysis

Consumer migration

Price elasticity

Competitor Incursion

X-promotion affinity analysis

Influencer analysis

Sessionize

Golden Path Determination

Fraud Sentiment Analysis

Channel Hopping

Attrition Paths

Fraudulent Paths

Productionize insights

Dashboards and reporting

Vendor managed inventory

Assortment optimization

Analytical Scoring

Event Triggers

Customer Behavior Analysis

Spend Analysis

Performance Analysis

Customer Segmentation

Risk Analysis

Customer Profitability

Portfolio Analysis

DISCOVERY

PLATFORM

CAPTURE | STORE | REFINE

INTEGRATED

DATA WAREHOUSE

TERADATA UNIFIED DATA ARCHITECTURE

LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS

Engineers

Data Scientists

Business Analysts

Front-Line Workers Customers / Partners Quants

Operational Systems Executives

Aggregations / ETL /API

Data Hub –Archive

Cleansing Multi-Structured

Consumerize

Voice to text; ID keyword

Image

X-Platform Aggregation

Demo

• Leverage Alteryx to direct analytics across Teradata and Teradata Aster to

gain consumer insight for your retail brand.

• Append 3rd party content to your customer records and load into Teradata

• Execute statistical algorithms in database via the Teradata R package

• Run nPath MapReduce function in Teradata Aster to reveal how online

customers navigate your web site to make a purchase

• Perform product correlation calculations in Teradata to understand

purchasing behaviors at brick and mortar locations

>>> Merchandise e-Commerce and Physical locations for optimal results

Retail Analytics

Let’s get started!

Ready to Play?

Thank You! Bruce.Johnson@Teradata.com JSchattin@Alteryx.com