Overview of the data landscape

Overview of the Data Landscape
How Big Data is Changing It…
Paul Zikopoulos, BA, MBA
IBM Vice President, Big Data and Technical Sales
Paul C. Zikopoulos, B.A., M.B.A., is the Vice
President of Technical Professionals for IBM
Software Group’s Information Management division
and additionally leads the World Wide Competitive
Database and Big Data Technical Sales Acceleration
teams. Paul is an award winning writer and speaker with
more than 19 years of experience in Information Management. Paul is
seen as a global expert in Big Data and database - independent groups
often recognize Paul as a thought leader in Big Data, with nominations
to SAP’s “Top 50 Big Data Twitter Influencers”, Big Data Republic’s
“Most Influential”, and Onalytica’s “Top 100” lists. Technopedia listed
him a “A Big Data Expert to Follow” and he was consulted on topic of
Big Data by the popular TV show “60 Minutes”. Paul has written more
than 350 magazine articles and 16 books, some of which include
“Harness the Power of Big Data”, “Understanding Big Data: Analytics
for Enterprise Class Hadoop and Streaming Data”, “Warp Speed, Time
Travel, Big Data, and More: DB2 10 New Features”, “DB2 pureScale:
Risk Free Agile Scaling”, “DB2 Certification for Dummies”, “DB2 for
Dummies”, and more. In his spare time, he enjoys all sorts of sporting
activities, including running with his dog Chachi, avoiding punches in his
MMA training, and trying to figure out the world according to Chloë—his
daughter. You can reach him at [email protected].
Studies show that organizations competing
on analytics outperform their peers
substantially outperform
IBM IBV/MIT Sloan Management Review Study 2011
Copyright Massachusetts Institute of Technology 2011
1.6x
Revenue
Growth
business initiative
2.5x
Stock Price
Appreciation
BUSINESS IMPERATIVE
2.0x
IQ
EBITDA
Growth
without analytics
BigData
is
just a bunch of data
Disruptive Forces Empower Data Drive Marketing
1
The emergence of
Big Data
Creating new
opportunities to
capture meaningful
information from
new varieties of
data and content
coming at
organizations in
huge volumes and
at accelerated
velocity.
5
2
The shift of power
to the consumer
Creating the
need for
organizations to
understand and
anticipate customer
behavior and needs
based on customer
insights across all
channels.
3
Accelerating
pressure to do
more with less
Creating the
need for all
parts of the
organization to
optimize all of their
processes to create
new opportunities,
to mitigate risk, and
to increase
efficiency.
There is a perfect storm where a vast constellation
of applications meets a massive, ubiquitous, and
unlimited network of endpoints
Social
Cloud
Mobile
Today’s Data: Auto-Temporal & Spatially Enriched
Spatial Triggered-Based Marketing with Personalization
Retailer Fan Page
Retailer
Customer Profile
Product Catalog
Telco
Customer Profile
Registers with Retailer, gives
Follows a friend’s
post on FB and clicks
the Like button on an
Item she likes
Permissions to Retailer and Telco
“Opt In”
Receives a
message with an offer
reminding her to stop
by if she’s in the area
Lisa uses
promo code to
purchase offer
at POS
Receives promo
code for offer while
passing by the store
Customer Action
Telco / Retailer Action
Intelligent Advisor
Intelligent Advisor
Platform
platform processes Lisa’s
activity for relevant actions
using Telco and Retailer
information
v
41M data points (including court surface correlations) mixed with
years of historical data to identify “Keys to the Match” predictors of
winning a set
10
A Customer is a Puzzle Made up of Many Pieces
Contact Information
Name, address,
employer, marital…
Business Context
Account number, customer
type, purchase history, …
Legal/Financial Life
Property, credit rating,
vehicles, …
Social Media
Every interaction
requires someone
to piece together
parts of the
puzzle
Social network,
affiliations, network …
Leisure
Hobbies, interests …
Professional Life
Employers, professional
groups, certifications …
Information about
your customers is
dispersed, forcing
your employees
to extract it pieceby-piece
When the Unaffordable becomes Affordable…
the Impossible becomes the Possible
Threshold
warnings
and
preferences
Service
Suspended
in Past?
Lost device?
Payment?
Lifecycle..
-moving
-new TV
-+++
Professional
Architect &
Small Business
Owner
Email
Social
apps
Single mobile
account for both
personal and
business use
Web
browsing
Usage
Classification
High LD?
Evenings?
Roam?
Storage
on
Device
Personal &
business
calls
Apps
Married with
no children
Only
somewhat
tech-savvy
Known for
being indecisive,
offer acceptance
history
Opt in | out
promotions
Public
Calendar
(Gift giving
season)
  Increasing abundance of automated consumer-facing service opportunities
gives us the data to know more about an entity than ever before
–  BUT ironically, we know less (think local banking branch)
The Death of the Average: Client D.N.A
Usage Data Summary (3 mos):
80% of calls out-of-network
Made 3 calls to a competitor call center
5 streaming video events per day
Heavily uses smartphone app
Data roamed in Japan 6 times
Service Profile:
Current Handset = RealPhone
Next Upgrade = March 2013
Data Plan = Unlimited Domestic
Features = Basic
Preferred
Customer Insights:
Product
Cat egories
referred
Customer P
Seg
=
SME
Channel
Lengt hCustomer
of Time Value = High
as Cust omer
Influencer Score = Moderate
Churn Risk = Mod/High
Loyalty Member = No
Recency +
Frequency +
Value
Response t o
Media
Preference:
Movies & video
Sports
International Travel
Social Media (Facebook)
Time unt il
Repurchase in Key
Cat egories
E
conomet ric:
Real-est at e &
Unemployment
Annual
S
pend Level
Annual
Transact ions
Age +
Income +
Geography
Billing Profile:
Average Bill = $200 per mo
Pays by autopay
Breadt h of
Cat egories
S
hopped
Ret urn /
E
xchange
Behavior
Use of S
ervice
Programs
Use of In-House
Credit Card
Part icipat ion in
Loyalt y
Program
Customer Profile:
Gender = Male
Marital = Married
Children = No
Income = Upper/Mid Tier
Language = English
D.N.A. Engineered Next Best Action
Bill has had Impact on
aAction
number of Churn
dropped
$20 off this month’s bill
calls
Deliver an recently
apology —
Issue with cell tower
being fixed this
weekend
6 months free
unlimited data plan
Upgrade phone
Impact on
Customer
Lifetime Value
Likelihood
to respond
positively
to action
Bill called a
competitor’s
contact
center
Connect the Dots: Real Time Web Behaviors
Audience and ID:
Bill Middleton, 1234567
Products of Interest:
NanoPhone
Product Education: 20
Likelihood to Purchase: 20
Churn Risk: 65
Big Data CSR Dashboard of the Future
Audience and ID:
Bill Middleton, 1234567
Products of Interest:
NanoPhone
Product Education: 60
Likelihood to Purchase: 65
Churn Risk: 15
Focus on the Call Center
How many places do your
agents have to look?
Knowledge
about the
customer
Knowledge
about the
product
How to diagnose
the problem
Domain
knowledge
Where’s
the order?
And more …
It’s not unusual for agents to
have to look into five or more
different systems to resolve a
single customer’s issue!
• What is the impact on the customer?
• What’s the impact on agent
productivity?
• What’s the impact on engagement
and job satisfaction?
Product offers based
on past purchases and
conversations
Consolidated list of
products owned based
on account affiliation
Contact
information from
CRM
List of past purchases
by this contact from
order tracking system
Recent conversations
from multiple sources:
e.g., CRM, e-mail, etc.
It’s Not the Only Thing…
BUT! Social Has Changed the Marketing Game
After a poor customer experience,
26% of consumers posted a
negative comment on a social
networking site like Facebook or
Twitter. By 2015, 75% percent of
consumers will tell their friends
about their good and bad
experiences using social media, up
from 25% in 2010.
1 in 3 social users prefer
social care vs. contacting a
company by phone
Propensity to Purchase Financial Products within a Year Based on
Social Media and Online Reviews.
Text Analytics Use Cases
• 
CRM Analytics
• 
–  Voice of customer
–  Product / Services gap analysis
combined with churn prediction via
Social Media
• 
Social Media Analytics
–  Retail applications such as intent
identification and customer churn
–  Reputational Risk applications such
timeliness of response
• 
Machine Data Analytics
–  Systemic risk analysis based on publicly
available data
–  Event monitoring to help identify adverse
events around counterparties from news
articles and web sources
• 
Email Analytics
–  Legal discovery
–  Regulatory compliance
Healthcare Analytics
–  Extract facts from transcribed medical
progress notes for complete patient
• 
Digital Piracy
–  Illegal broadcast of streaming and
video content
–  Illegal dissemination of copyrighted
digital material
–  Parse logs into individual fields to enable
applications such as detection and
prediction of potential failures
• 
Financial Analytics
• 
Data Redaction
–  Identify sensitive information for redaction
and masking
Call Center Analytics
Yes, I’m calling about my smart phone; you folks are seriously
starting to…with all due respect because this is the first time
we’ve talked, but really starting to annoy me. I’ve been
transferred 3 times for starters. I’ve had more dropped
calls than Mark Sanchez has fumbles…
Smart Phone ticket for client Paul Zikopoulos
Subject/Issue
Polarity
transferred 3 times
Negative
Parsing
Action
annoy
calls
Negative
dropped
ConnectTel
Churn
called
Deep Understanding of Text
Facts
I’ve called ConnectTel, and they have a switch now offering
that provides me with significant discounts above what you
have in my current package.
Example: Revenue for Drug by Region and Year
•  Information extracted in structured format for further
automated analysis
•  Input data available as free text
Input document: …\EliLilly-59478
Example Application: Lead Generation
Real-time product
intents enriched with
consumer attributes
Entries contain promotional messages,
wishful thinking, questions, etc
Integration across Social Media sites
Micro-segmentation of
product intents by
occupation
Real-time tracking by
micro-segmentation
For many of the attributes we need to extract,
cleanse, normalize and categorize
Micro-segmentation of
consumers by hobbies
Leading $2 Trillion Operations Global Finance Firm
•  Extract insights from massive volumes of publicly available data
(structured and unstructured) to deliver actionable insights by
determining high quality life-event based marketing leads based on
their financial interest, needs, and intentions
–  Micro-segmentation insights from populations segmented by social, internal
and combined attributes for brand management
–  Insights at individual level for use cases such as lead generation
–  Identification and categorization of concepts such as life-events, buzz and
sentiment with high precision from noisy social media data
–  Lead analysis vs. internal data such as transaction and production ownership
–  Discovery of patterns in the data with micro-segment
•  Detected sentiment/reaction to the brand for existing and to be clients
•  Solution helped build a $40M pipe line of potential clients and
delivered $1.5M of savings to a single division
Which Ad Yields a Better Net Promoter Score?
BETTER
ENGAGEMENT
BETTER REACH
BETTER
BETTER CHURN
ENGAGEMENT
BETTER
SENTIMENT
Data Quality: Turning Data into Trusted Information
Control and Monitor Quality
Assess and monitor the quality of your data in any place and across systems
Unique capability to align Data quality metrics with Business and Governance objectives
A
Unsupervised Learning
A, A, A, A, A, A, A, A, A, A, A,
A, A, A, A, A, A, A, A, A, A, A, A,
A, A, A, A, A, A, A, A, A, A, A, A,
A, A, A, A, A, A, A, A, A, A, A,
A, A, A, A, A, , A, A, A, A, A,
A, A, A, A, A, A, A, A, A, A, A, A,
A, A, A, A, A, A, A, A, A, A, A,
A, A, A, A, A, A, A, A
•  Pins / Re-pins
•  Likes / Dislikes
•  Tweets
•  Favorites
Photo Albums and Pinboards
•  Photo Semantic
Analysis
•  User
Segmentation
Models
Products
Style
Kitchen Gallery
Styles
Logos
Brands
Consumer
Computer
Designs
Dream Home
•  Advertisements
•  Promotions
•  Campaigns
•  Planning
Wedding
Retailers, Marketers and Planners
•  Preferred Styles
•  Designs
•  Products
•  Interests
Business Analytics
A Big Data Platform Manifesto
Understand and Navigate
Federated Big Data Sources
Federated Discovery
and Navigation
Manage and Store Huge
Volume of any Data
Hadoop File System
MapReduce
Structure and Control Data
In-Memory Analytics
Data Warehousing
Manage Streaming Data
Analyze Unstructured Data
Integrate and Govern
all Data Sources
Stream Computing
Text Analytics Engine
Integration, Data Quality,
Security, ILM, MDM
Business Analytics
A Big Data Platform Manifesto
Understand and Navigate
Federated Big Data Sources
Federated Discovery
and Navigation
Manage and Store Huge
Volume of any Data
Hadoop File System
MapReduce
All of the Demos youIn-Memory
sawAnalytics
Data Warehousing
where built on the IBM Big
Manage
Streamingplatform
Data
Data
that maps
to
Stream Computing
this manifesto
Structure and Control Data
Analyze Unstructured Data
Integrate and Govern
all Data Sources
Text Analytics Engine
Integration, Data Quality,
Security, ILM, MDM
@BigData_paulz
THINK
and visit the IBM booth!
33