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Five Pitfalls When Operationalizing
Data Science and a Strategy for
Success
August 2, 2017
Operationalizing Data Science
Today’s Speakers
Dormain Drewitz
Pivotal
Jeff Kelly
Pivotal
Mike Gualtieri
Forrester Researc...
Firms invest millions in data and analytics
© Forrester Research, Inc. Reproduction Prohibited
…and advanced analytics is surging.
© Forrester Research, Inc. Reproduction Prohibited
Data science is about creating predictive
models.
© Forrester Research, Inc. Reproduction Prohibited
What offers should you make to your customer if
they are eCommerce’ing right now?
© Forrester Research, Inc. Reproduction ...
What systems require maintenance right now?
© Forrester Research, Inc. Reproduction Prohibited
SCIENTIST
DATAProfessionals who use algorithms to analyze data
to find models – models that can predict
outcomes or unders...
It’s not just about
uncovering insights
But ...
You Also Must ...
Operationalize
Data Science
Why Data Science Efforts
Often Fall Short
Common Pitfalls
Common Pitfalls
1. Predictive
Insights
Insufficient
Success depends on the collective efficacy
of decisions about customers, operations,
and strategy...
© Forrester Research,...
…and the ability to rapidly model, improve, and
change decision logic…
© Forrester Research, Inc. Reproduction Prohibited
…infused in applications to scale decision-
making.
© Forrester Research, Inc. Reproduction Prohibited
Common Pitfalls
2. Lack of
Business
Process
Integration
© Forrester Research, Inc. Reproduction Prohibited
Operationalize a model is to use it within applications
and/or business...
Common Pitfalls
3. Pace of
Insight
Generation
Mismatch
© Forrester Research, Inc. Reproduction Prohibited
“As you look to improve your data processing and analytics capabilities...
All data originates in real-time!
© Forrester Research, Inc. Reproduction Prohibited
But, analytics to gain insights is
usually done much, much later.
© Forrester Research, Inc. Reproduction Prohibited
Stop wasting money on unactionable or even
harmful insights.
Forrester Report: Perishable Insights – Stop Wasting Money On...
You must compress analytics time-to-insight
to maximize the value of data
BusinessValue
Time To Action
PositiveNegative
Ma...
Common Pitfalls
4. Inability to
Act on
Perishable
Insights
Insights are perishable.
© Forrester Research, Inc. Reproduction Prohibited
Real-time
insights
Operational
insights
Performance
insights
Strategic
insights
Insight: Shopping for
furniture
Action: Re...
Data science models must be deployed
immediately.
© Forrester Research, Inc. Reproduction Prohibited
Common Pitfalls
5. Failing to
Learn from Past
Experience
xyzzy
© Forrester Research, Inc. Reproduction Prohibited
Garbage In = Garbage Out
© Forrester Research, Inc. Reproduction Prohibited
Common Pitfalls
3. Pace of Insight
Generation
Mismatch
2. Lack of
Business Process
Integration
1. Predictive
Insights are
...
Operationalizing Data Science
A Strategy for Success
A Strategy for Success
1. Develop
Prescriptive
Insights
cover this square with an image (540 x 480 pixels)
Case Study: Large,
nationwide retailer
•  Wanted to institute dynamic p...
A Strategy for Success
2. Integrate
w/ Business
Processes
cover this square with an image (540 x 480 pixels)
Case Study:
Dell EMC IT
•  Wanted to use product usage and
machine-gene...
A Strategy for Success
3. Right
Insights,
Right Time
cover this square with an image (540 x 480 pixels)
Case Study:
Conversant
•  Needed to create detailed user
profiles to de...
A Strategy for Success
4. Software
Automation
Required
cover this square with an image (540 x 480 pixels)
Case Study:
General Electric
•  Needed to optimize asset
performance to...
A Strategy for Success
5. Close the
Analytics
Loop
cover this square with an image (540 x 480 pixels)
Case Study: Clinical
healthcare
•  Wanted to improve patient
diagnosis ...
A Strategy for Success
3. Right Insight,
Right Time
2. Business
Process
Integration
1. Prescriptive
Insights
4. Software
A...
Upcoming Webinar
Learn How to Operationalize IoT Apps
on Pivotal Cloud Foundry
with Pivotal Data Scientist Chris Rawles
Au...
Data Science
Everybody’s doing it!
•  Personalize the customer
experience
•  Improve operational efficiency
•  Create new market
opportunities
Why?
Data scie...
Five Pitfalls when Operationalizing Data Science and a Strategy for Success
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Five Pitfalls when Operationalizing Data Science and a Strategy for Success

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

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Five Pitfalls when Operationalizing Data Science and a Strategy for Success

  1. 1. Five Pitfalls When Operationalizing Data Science and a Strategy for Success August 2, 2017
  2. 2. Operationalizing Data Science Today’s Speakers Dormain Drewitz Pivotal Jeff Kelly Pivotal Mike Gualtieri Forrester Research Guest Speaker
  3. 3. Firms invest millions in data and analytics © Forrester Research, Inc. Reproduction Prohibited
  4. 4. …and advanced analytics is surging. © Forrester Research, Inc. Reproduction Prohibited
  5. 5. Data science is about creating predictive models. © Forrester Research, Inc. Reproduction Prohibited
  6. 6. What offers should you make to your customer if they are eCommerce’ing right now? © Forrester Research, Inc. Reproduction Prohibited
  7. 7. What systems require maintenance right now? © Forrester Research, Inc. Reproduction Prohibited
  8. 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. 9. It’s not just about uncovering insights But ...
  10. 10. You Also Must ... Operationalize Data Science
  11. 11. Why Data Science Efforts Often Fall Short Common Pitfalls
  12. 12. Common Pitfalls 1. Predictive Insights Insufficient
  13. 13. Success depends on the collective efficacy of decisions about customers, operations, and strategy... © Forrester Research, Inc. Reproduction Prohibited
  14. 14. …and the ability to rapidly model, improve, and change decision logic… © Forrester Research, Inc. Reproduction Prohibited
  15. 15. …infused in applications to scale decision- making. © Forrester Research, Inc. Reproduction Prohibited
  16. 16. Common Pitfalls 2. Lack of Business Process Integration
  17. 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. 18. Common Pitfalls 3. Pace of Insight Generation Mismatch
  19. 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. 20. All data originates in real-time! © Forrester Research, Inc. Reproduction Prohibited
  21. 21. But, analytics to gain insights is usually done much, much later. © Forrester Research, Inc. Reproduction Prohibited
  22. 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. 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. 24. Common Pitfalls 4. Inability to Act on Perishable Insights
  25. 25. Insights are perishable. © Forrester Research, Inc. Reproduction Prohibited
  26. 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. 27. Data science models must be deployed immediately. © Forrester Research, Inc. Reproduction Prohibited
  28. 28. Common Pitfalls 5. Failing to Learn from Past Experience
  29. 29. xyzzy © Forrester Research, Inc. Reproduction Prohibited
  30. 30. Garbage In = Garbage Out © Forrester Research, Inc. Reproduction Prohibited
  31. 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. 32. Operationalizing Data Science A Strategy for Success
  33. 33. A Strategy for Success 1. Develop Prescriptive Insights
  34. 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. 35. A Strategy for Success 2. Integrate w/ Business Processes
  36. 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. 37. A Strategy for Success 3. Right Insights, Right Time
  38. 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. 39. A Strategy for Success 4. Software Automation Required
  40. 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. 41. A Strategy for Success 5. Close the Analytics Loop
  42. 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. 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. 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
  45. 45. Data Science Everybody’s doing it!
  46. 46. •  Personalize the customer experience •  Improve operational efficiency •  Create new market opportunities Why? Data science enables enterprises to ...

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