1. The Digital Enterprise
Big Data and Analytics Lead the Way!
Thomas H. Davenport
Babson/MIT/Harvard
December 5, 2013

2. The Digital Enterprise
Key Capabilities
•
Efficient, fast transactions
•
Agile system development
•
IT-enabled processes
•
Knowledge management
•
The ability to make sense of
exabytes of data: analytics!
•
Ranked the #1 priority at WSJ CIO
Summit last week

3. Working wonders for
Google, eBay, & LinkedIn
…but what about
everyone else?
Big data begins at
online firms
& startups
No technical or
organizational
infrastructure to
co-exist with
Findings show evolution
of a new analytics
paradigm
What happens in
20 big companies when
analytics are
well-entrenched?

4. “Big Data in Big Companies” Study
• How new? “Not very” to many –continually
adding data over time
UPS – Started building telematics capabilities in 1986
• Excited about new sources of data, new
processing capabilities
• Familiar rationales for big data:
Same decisions faster – Macy’s, Caesars
Same decisions cheaper – Citi
Better decisions with more data – United Healthcare
Product/service innovation – GE, Novartis
• Need new management paradigm

5. Analytics 1.0
Traditional Analytics
•
•
Internally sourced, relatively small, structured data
•
1.0
Primarily descriptive analytics and reporting
“Back room” teams of analysts
•
Internal decision support focus
•
Slowly-developed models

6. Analytics 1.0
Data Environment
ERP
Reporting
CRM
OLAP
Legacy
Ad Hoc
3rd Party Apps
Modeling

7. Analytics 1.0
Other Technologies
•
Spreadsheets
•
BI and analytics “packages”
•
ETL tools
•
OLAP cubes
•
On-premise servers
•
Out-of-database/memory analytics

8. Keep inside the
sheltering confines of
the IT organization
Take your time—
nobody’s that interested
in your results anyway
Focus on the past,
where the real threats to
your business are

9. Analytics 2.0
The Big Data era
•
•
2.0
Complex, large, unstructured data about
customers
New analytical and computational capabilities
•
“Data Scientists” emerge
•
Online and startup firms create data and analyticsbased products and services

10. 2.0 Data Products
From Online Firms
• Google—Search, AdSense, Books, Maps, Scholar, etc., etc.
• LinkedIn—People You May Know, Jobs You May Like, Groups You May Be
Interested In, etc.
• Netflix—Cinematch, Max, etc.
• Zillow—Zestimates, rent Zestimates, Home Value Index, Underwater Index, etc.
• Facebook—People You May Know, Custom Audiences, Exchange

11. Analytics 2.0
Data Environment
Web Logs
HDFS
Images & Videos
Operational
Systems
Social Media
Docs & PDFs
Map/Reduce
Data
Warehouse
Data Marts
& ODS

12. Agile is
too slow
We need to
be “on the
bridge”
We’re
changing
Consulting = the world
dead zone

13. Analytics 3.0
Fast, Pervasive Impact in the Age of Smart Machines
•
•
3.0
Analytics used for data products and Industrialized
decision processes
A seamless blend of traditional analytics and big data
•
Analytics integral to all business functions
•
Rapid, agile insight and model delivery
•
Analytical tools available at point and time of decision
•
Analytics are everybody’s job
TODAY

14. Analytics 3.0
Competing in the Data Economy
• Every company – not just online firms – can create data and
analytics-based products and services that change the game
• Use “data exhaust” to help customers use your products and
services more effectively
• Continuous, real-time analytics
• Start with data opportunities or start with business problems?
Answer is yes!
• Need “data products” team good at data science, customer
knowledge, new product/service development
• Internally, analytics built at scale and embedded into decision
processes

15. Analytics 3.0: Data Types
Articles
• Customer profiles
• Organization
contacts
• Billing
• Marketing
• Contracts/orders
• Shipping
• Claims
• Call center
• Customer service
•
•
•
•
•
•
•
•
•
Purchase history
Segmentation
Customer value
Purchasing behavior
Recommendations
Sentiment analysis
Target marketing
Satisfaction
Customer
experience
management
• Service tiers
Social Feeds
Mobile devices
XML
Videos
Twitter
Device sensors
Blogs
Cloud
Spatial GPS
LinkedIn
Presentations
RSS
Images
Hosted applications
Documents
Email
Website activity
Text messages
Clickstream logs

16. Analytics 3.0
Data Management Choices

17. Analytics 3.0
Technology & people
•
Heavy reliance on machine learning
•
In-memory and in-database analytics
•
Integrated and embedded models
•
Analytical “apps” by industry and decision
•
Focus on data discovery
•
Blended data science/business/IT teams
•
Chief Analytics Officers in many firms
3.0

18. Procter & Gamble 3.0
176 years old
•
•
Primary focus on improving management
decisions at scale
•
“Information and Decision Solutions” (IT)
embeds over 300 analysts in leadership teams
•
Over 50 “Business Suites” for executive
information viewing and decision-making
•
“Decision cockpits” on 50K desktops
•
35% of marketing budget on digital
•
Real-time social media sentiment analysis for
“Consumer Pulse”

19. GE 3.0
120 years old
•
$2B initiative in software, analytics, and
“Industrial Internet”
•
Primary focus on data-based products and
services from “things that spin”
•
Will reshape service agreements for
locomotives, jet engines, turbines
•
Gas blade monitoring in turbines produces 588
gigabytes/day—7 times Twitter daily volume
•
Offering new industrial data platforms and
brands like “Predictivity” and “Predix”

20. Ford 3.0
110 years old
•
Bill Ford: “The car is really becoming a rolling
group of sensors.”
•
Ford’s Digital Analytics and Optimization team
has full responsibility for all B2C channels and
N. American business units
•
Dynamic multichannel testing and targeting with
automation and integration of SEO/SEM, CRM,
email, media, etc.
•
Hyper-local dealer support digital algorithm
delivered 85% increase in action rate and 48%
decrease in cost per action

21. Recipe for a 3.0 World
1.
Start with an existing
capability for data management
and analytics
2.
Add some unstructured,
large-volume data
3.
Throw some product/service
innovation into the mix
4.
Add a dash of Hadoop
and a pinch of NoSQL
5.
Cook up some data in
a high-heat convection oven
6.
Train your sous chefs in big data
and analytics

22. Implications for
Software/Services Providers
•
Need to embed analytics into other systems
•
May be role for ongoing monitoring of
embedded analytics
•
Software firms hold up the “data mirror”
•
Dealing with the law of large numbers on
analytical skills
•
Analysts often need to be embedded to have
an impact

23. Thank you!
tdavenport@babson.edu