Enterprise executives and IT teams alike know that data science is not optional, but struggle to benefit from it because the process takes too long and operationalizing models in applications can be hairy. Join guest speaker, Forrester Research’s Mike Gualtieri and Pivotal’s Jeff Kelly and Dormain Drewitz for an interactive discussion about operationalizing data science in your business. In this webinar, the first of a two-part series, you will learn: - The essential value of data science and the concept of perishable insights. - Five common pitfalls of data science teams. - How to dramatically increase the productivity of data scientists. - The smooth hand-off steps required to operationalize data science models in enterprise applications. Presenter : Guest Speakers Mike Gualtieri, Forrester, Dormain Drewitz and Jeff Kelly, Pivotal

1. Five Pitfalls When Operationalizing
Data Science and a Strategy for
Success
August 2, 2017

2. Operationalizing Data Science
Today’s Speakers
Dormain Drewitz
Pivotal
Jeff Kelly
Pivotal
Mike Gualtieri
Forrester Research
Guest Speaker

3. Firms invest millions in data and analytics
© Forrester Research, Inc. Reproduction Prohibited

4. …and advanced analytics is surging.
© Forrester Research, Inc. Reproduction Prohibited

5. Data science is about creating predictive
models.
© Forrester Research, Inc. Reproduction Prohibited

6. What offers should you make to your customer if
they are eCommerce’ing right now?
© Forrester Research, Inc. Reproduction Prohibited

7. What systems require maintenance right now?
© Forrester Research, Inc. Reproduction Prohibited

8. SCIENTIST
DATAProfessionals who use algorithms to analyze data
to find models – models that can predict
outcomes or understand context with significant
accuracy and improve as more data is available.
© Forrester Research, Inc. Reproduction Prohibited

9. It’s not just about
uncovering insights
But ...

10. You Also Must ...
Operationalize
Data Science

11. Why Data Science Efforts
Often Fall Short
Common Pitfalls

12. Common Pitfalls
1. Predictive
Insights
Insufficient

13. Success depends on the collective efficacy
of decisions about customers, operations,
and strategy...
© Forrester Research, Inc. Reproduction Prohibited

14. …and the ability to rapidly model, improve, and
change decision logic…
© Forrester Research, Inc. Reproduction Prohibited

15. …infused in applications to scale decision-
making.
© Forrester Research, Inc. Reproduction Prohibited

16. Common Pitfalls
2. Lack of
Business
Process
Integration

17. © Forrester Research, Inc. Reproduction Prohibited
Operationalize a model is to use it within applications
and/or business processes.
Streaming data
Application
interface
App Logic
Applications
Context
Actions
Current
Context
Programmed
Logic
Predictive
LogicData science Models
External
Actions
External
Context
From other data
sources of
applications
To other data
sources or
applications

18. Common Pitfalls
3. Pace of
Insight
Generation
Mismatch

19. © Forrester Research, Inc. Reproduction Prohibited
“As you look to improve your data processing and analytics capabilities, what aspect of
the implementation is most important to your business? Please select one.”
11%
11%
12%
16%
24%
25%
Quick turnaround on customer requests
More data availability
Expanded access to more business users (i.e.,
self-service)
Low cost
Advanced analytics capabilities (e.g. predictive.
prescriptive, streaming)
Faster performance (time to value)
Faster time to value and advanced analytics
is most important to business
Base: 100 data science and data analytics leaders at enterprises within the US
Source: A commissioned study conducted by Forrester Consulting, April 2016

20. All data originates in real-time!
© Forrester Research, Inc. Reproduction Prohibited

21. But, analytics to gain insights is
usually done much, much later.
© Forrester Research, Inc. Reproduction Prohibited

22. Stop wasting money on unactionable or even
harmful insights.
Forrester Report: Perishable Insights – Stop Wasting Money On Unactionable Analytics
BusinessValue
Time To Action
Data
originated
Analytics
performed
Insights
gleaned
Action
taken
Outdated
insights
Impotent or
harmful
actions
Negative
Decision
made
Poor
decision
Positive
© Forrester Research, Inc. Reproduction Prohibited

23. You must compress analytics time-to-insight
to maximize the value of data
BusinessValue
Time To Action
PositiveNegative
Maximum
Business
Value
© Forrester Research, Inc. Reproduction Prohibited

24. Common Pitfalls
4. Inability to
Act on
Perishable
Insights

25. Insights are perishable.
© Forrester Research, Inc. Reproduction Prohibited

26. Real-time
insights
Operational
insights
Performance
insights
Strategic
insights
Insight: Shopping for
furniture
Action: Recommend
cleaning supplies
Insight: Profit lower than
goal
Action: Optimize price
Insight: Demand forecast
strong
Action: Increase inventory
Insight: Furniture demand
high
Action: Expand product line
TimetoAct
Perishability
Sub-second to
seconds
Seconds to
hours
Days to
weeks
Weeks to
years
Sub-second to
seconds
Seconds to
hours
Hours to
weeks
Weeks to
years
Enterprises must act on a range of perishable
insights to get value from data and analytics
© Forrester Research, Inc. Reproduction Prohibited

27. Data science models must be deployed
immediately.
© Forrester Research, Inc. Reproduction Prohibited

28. Common Pitfalls
5. Failing to
Learn from Past
Experience

29. xyzzy
© Forrester Research, Inc. Reproduction Prohibited

30. Garbage In = Garbage Out
© Forrester Research, Inc. Reproduction Prohibited

31. Common Pitfalls
3. Pace of Insight
Generation
Mismatch
2. Lack of
Business Process
Integration
1. Predictive
Insights are
Insufficient
4. Inability to
Act on
Perishable
Insights
5. Failing to Learn
from Past
Experience

32. Operationalizing Data Science
A Strategy for Success

33. A Strategy for Success
1. Develop
Prescriptive
Insights

34. cover this square with an image (540 x 480 pixels)
Case Study: Large,
nationwide retailer
• Wanted to institute dynamic pricing
based on numerous factors, like
weather, time of year, competition,
etc.
• Analyzed historical transaction data,
demographic data, other sources to
determine pricing
• Operationalized insights via
integration with e-commerce
platform, operational application

35. A Strategy for Success
2. Integrate
w/ Business
Processes

36. cover this square with an image (540 x 480 pixels)
Case Study:
Dell EMC IT
• Wanted to use product usage and
machine-generated data to support
preventative maintenance
• Collected and analyzed storage
array data to predict likely part
failures
• Integrated insights into service
workflow to proactively inform
customers of likely issues and
remedies

37. A Strategy for Success
3. Right
Insights,
Right Time

38. cover this square with an image (540 x 480 pixels)
Case Study:
Conversant
• Needed to create detailed user
profiles to determine best ads to
place
• Analyzed user data to create
profiles, analysis to determine best
ads
• Operationalized insights via real-
time adtech platform

39. A Strategy for Success
4. Software
Automation
Required

40. cover this square with an image (540 x 480 pixels)
Case Study:
General Electric
• Needed to optimize asset
performance to improve customer
outcomes
• Analyzed sensor data, weather
data, other sources to determine
preventative maintenance
• Operationalized via software
automation that adjust asset
performance based on insights

41. A Strategy for Success
5. Close the
Analytics
Loop

42. cover this square with an image (540 x 480 pixels)
Case Study: Clinical
healthcare
• Wanted to improve patient
diagnosis and treatment plans
• Analyzed clinical data, research
data, other sources to develop
recommendations via app
• Clinicians record outcomes, which
are fed back into analytics system to
improve future recommendations

43. A Strategy for Success
3. Right Insight,
Right Time
2. Business
Process
Integration
1. Prescriptive
Insights
4. Software
Automation
5. Close the
Analytics Loop

44. Upcoming Webinar
Learn How to Operationalize IoT Apps
on Pivotal Cloud Foundry
with Pivotal Data Scientist Chris Rawles
August 15, 2017 @ 11am PT/2pm ET
Register:
https://content.pivotal.io/webinars/aug-15-learn-how-to-operationalize-
iot-apps-on-pivotal-cloud-foundry-webinar

46. Data Science
Everybody’s doing it!

47. • Personalize the customer
experience
• Improve operational efficiency
• Create new market
opportunities
Why?
Data science enables enterprises to ...