The document discusses a case study on customer-centered product strategy for machine intelligence, highlighting methodologies and practices that facilitated product discovery and strategic planning for new analytics products at a leading SaaS company. It details the evolution of product portfolios, improvements in data management and processing, and new features in version 1.4.x of Oracle's Big Data Discovery. The overall focus is on establishing effective product strategies that enhance customer experience and drive business outcomes in emerging technology spaces.

1. LONG TERM OUTCOMES
CUSTOMER-CENTERED PRODUCT STRATEGY FOR
MACHINE INTELLIGENCE

2. JOE LAMANTIA
Currently: Available > call me / hire me!
Previous: Amazon, SallieMae, Bottomline, Capital One, Paypal, Oracle, Endeca
Focus: Product & Design leadership, Strategy for emerging tech
joe.lamantia@gmail.com
joelamantia.org
https://www.youtube.com/joelamantia
https://www.slideshare.net/moJoe/presentations
https://www.linkedin.com/in/digitaljoelamantia/

3. Flying Blind On A Rocket Cycle: Case Study on Customer-centered
Product Strategy for Machine Intelligence
This session shares a case study on the growth and evolution of B2B product portfolios
driven by machine intelligence for a leading SaaS product maker. This case study
reviews a series of new product efforts; outlines the methods, tools, and practices that
powered product discovery and strategic planning; traces the evolution of product
portfolios; and considers business outcomes from building and growing a portfolio of
new analytics products and services for Oracle.
▸ Craft customer-centered product strategies
▸ Build and evolve customer-centered products and portfolios
▸ Establish effective, innovative, customer-centered product strategy capabilities and
practices

4. LONG TERM OUTCOMES
MACHINE INTELLIGENCE PRODUCTS AND SERVICES

5. =

6. ANALYTICS PRODUCTS
EMERGING SPACES

7. "PRODUCT STRATEGY CHARTS THE
COURSE OF PRODUCT INVESTMENT AND
EVOLUTION"
Joe Lamantia
DEFINITION

8. EMERGING SPACES
NEW
PRODUCTS
ANALYTICS
PRODUCT STRATEGY $$$

9. WHY ARE YOU HERE?
WHERE ARE YOU GOING?

10. 1. GET IN THE HEADS OF DATA SCIENTISTS
2. BE THE SPIRIT OF THE PRODUCT

11. HOW WILL YOU GET
THERE?

12. CONTINUOUS LEARNING
& LEAN STRATEGY
HYPOTHESIS
> 1-2 WK SPRINT
>> OUTCOME

13. SPIRIT OF THE PRODUCT:
DATA SCIENTISTS:

14. PRODUCT LANDSCAPE:
VALUE CHAIN:

15. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Confidential – Internal
Requires a Fundamentally New Approach
15
A single intuitive,
interactive and visual
user interface
Explore
Transform
Discover
Find
for anyone to quickly find,
explore, transform and
analyze data in Hadoop
then share results for
enterprise leverage
New role >>
New product category

16. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Confidential – Internal
• Understand shape of the
data. Visualize attributes
by type
• Entropy based sorting by
information potential
• View attribute statistics,
data quality and outliers
• Use scratch pad to see
statistical correlations
between attribute
combinations
• Evaluate whether a data
set is worthy of further
investment
16
Explore the Data and Understand Potential

17. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Confidential – Internal 17
Oracle Big Data Discovery. The Visual Face of Hadoop
Explore
Transform
Discover
Find

18. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
A SERIES OF INVESTMENTS

19. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
~14 months

20. CLARITY
SPEED
TIMING
MOMENTUM
IMPACT
=

21. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
A SERIES OF INVESTMENTS

22. Chapter 5
What's New and Changed in this Release
This section describes the changes made for this release of BDD, including new, deprecated, and
unsupported features.
New and updated features
Data set settings controlled by edp-cli.properties or in Studio
New and updated features
The following features have been added, improved, or updated for the Oracle Big Data Discovery 1.4.x
release.
Transform-related features
This release includes these transform-related features and improvements:
• You can use a new Get Sentiment transformation in Studio to extract positive or negative sentiment from
attributes in your data, or label some attributes as having a positive or negative sentiment.
Improvements in data scale and management
This release includes these improvements to data and scale management:
• Continued data ingest parallelization further improves performance for data loading.
• The Data Processing component of BDD uses Parquet format for its sample files, instead of the Avro
format used in the previous releases. During an upgrade to this release, Avro files are converted to
Parquet. Parquet is an efficient storage format natively supported in Hive that separates storing data from
metadata and improves query performance for columnar-based data in analytics applications.
BDD as a complete toolkit in your data lab
This release includes these changes:
• As the BDD administrator, you can better manage job status for data processing jobs in BDD. You can
use new command line flags to get the job status, list currently running jobs, and cancel jobs by job ID.
• The stability and robustness of the indexing process in BDD has been improved: the Dgraph process
validates all of its provided command line options and ports, reports inconsistencies, uses defaults in
cases when invalid values are specified, and exits with errors in cases of port conflicts.
• The Workflow Manager Service is added in this release. Studio and the DP CLI use the Workflow
Manager Service to run several data processing workflows in BDD. You can control the Workflow
Manager Service settings in the edp.properties file in $BDD_HOME/workflowmanager/dp/config.
This file is also new in this release. The introduction of the Workflow Manager Service separates the front-
Oracle® Big Data Discovery : Getting Started Guide Version 1.4.0 • October 2016
OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
~6 months

23. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
~12 weeks
X

24. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
* + 2 years to prioritize…
~4 weeks

25. CLARITY
SPEED
TIMING
MOMENTUM
IMPACT
=

26. CUSTOMERS

27. PRODUCT
6 releases portfolio consolidation

28. PRODUCT
6 releases portfolio consolidation
the return voyage
designs an ideal course

29. EVOLUTION
“Long view = tools in this segment could ‘eat’ BI marketshare by adding reporting and other structured analytical
capabilities that capture customers who do not have large BI stacks now, begin investing here, and subsequently
need BI capability.”

30. SAME
DESTINATION
EVOLVING
COURSE

31. PORTFOLIO
New capabilities Expanded portfolio

32. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
Chapter 5
What's New and Changed in this Release
This section describes the changes made for this release of BDD, including new, deprecated, and
unsupported features.
New and updated features
Data set settings controlled by edp-cli.properties or in Studio
New and updated features
The following features have been added, improved, or updated for the Oracle Big Data Discovery 1.4.x
release.
Transform-related features
This release includes these transform-related features and improvements:
• You can use a new Get Sentiment transformation in Studio to extract positive or negative sentiment from
attributes in your data, or label some attributes as having a positive or negative sentiment.
Improvements in data scale and management
This release includes these improvements to data and scale management:
• Continued data ingest parallelization further improves performance for data loading.
• The Data Processing component of BDD uses Parquet format for its sample files, instead of the Avro
format used in the previous releases. During an upgrade to this release, Avro files are converted to
Parquet. Parquet is an efficient storage format natively supported in Hive that separates storing data from
metadata and improves query performance for columnar-based data in analytics applications.
BDD as a complete toolkit in your data lab
This release includes these changes:
• As the BDD administrator, you can better manage job status for data processing jobs in BDD. You can
use new command line flags to get the job status, list currently running jobs, and cancel jobs by job ID.
• The stability and robustness of the indexing process in BDD has been improved: the Dgraph process
validates all of its provided command line options and ports, reports inconsistencies, uses defaults in
cases when invalid values are specified, and exits with errors in cases of port conflicts.
• The Workflow Manager Service is added in this release. Studio and the DP CLI use the Workflow
Manager Service to run several data processing workflows in BDD. You can control the Workflow
Manager Service settings in the edp.properties file in $BDD_HOME/workflowmanager/dp/config.
This file is also new in this release. The introduction of the Workflow Manager Service separates the front-
Oracle® Big Data Discovery : Getting Started Guide Version 1.4.0 • October 2016
~5 years

33. BUSINESS STRATEGY:
ANALYTICS PRODUCTS
FOR
EMERGING SPACES

34. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Joe Lamantia | Product Strategist: Oracle Endeca Big Data Discovery 34

35. ANALYTICS PRODUCTS
EMERGING SPACES

36. Chapter 5
What's New and Changed in this Release
This section describes the changes made for this release of BDD, including new, deprecated, and
unsupported features.
New and updated features
Data set settings controlled by edp-cli.properties or in Studio
New and updated features
The following features have been added, improved, or updated for the Oracle Big Data Discovery 1.4.x
release.
Transform-related features
This release includes these transform-related features and improvements:
• You can use a new Get Sentiment transformation in Studio to extract positive or negative sentiment from
attributes in your data, or label some attributes as having a positive or negative sentiment.
Improvements in data scale and management
This release includes these improvements to data and scale management:
• Continued data ingest parallelization further improves performance for data loading.
• The Data Processing component of BDD uses Parquet format for its sample files, instead of the Avro
format used in the previous releases. During an upgrade to this release, Avro files are converted to
Parquet. Parquet is an efficient storage format natively supported in Hive that separates storing data from
metadata and improves query performance for columnar-based data in analytics applications.
BDD as a complete toolkit in your data lab
This release includes these changes:
• As the BDD administrator, you can better manage job status for data processing jobs in BDD. You can
use new command line flags to get the job status, list currently running jobs, and cancel jobs by job ID.
• The stability and robustness of the indexing process in BDD has been improved: the Dgraph process
validates all of its provided command line options and ports, reports inconsistencies, uses defaults in
cases when invalid values are specified, and exits with errors in cases of port conflicts.
• The Workflow Manager Service is added in this release. Studio and the DP CLI use the Workflow
Manager Service to run several data processing workflows in BDD. You can control the Workflow
Manager Service settings in the edp.properties file in $BDD_HOME/workflowmanager/dp/config.
This file is also new in this release. The introduction of the Workflow Manager Service separates the front-
Oracle® Big Data Discovery : Getting Started Guide Version 1.4.0 • October 2016
ANALYTICS PRODUCTS
EMERGING SPACES

37. WHY ARE YOU HERE?
WHERE ARE YOU GOING?
HOW WILL YOU GET THERE?
ANALYTICS PRODUCTS
EMERGING SPACES
NEW PRODUCTS
EXPANDED PORTFOLIO

38. NEW PRODUCTS
NEW SERVICES
NEW CATEGORIES
NEW CUSTOMERS
NEW CAPABILITIES
NEW TECHNOLOGY
ANALYTICS PRODUCTS
EMERGING SPACES

39. ANALYTICS PRODUCTS
EMERGING SPACES

40. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
A SERIES OF INVESTMENTS

41. next generation
secret project
200 person organization
legacy offerings
whole new product
new category & platform
vision
napkin plan
fi
rst-mover CIOs
end-to-end
new technology
conventional wisdom

42. next generation
secret project
200 person organization
legacy offerings
whole new product
new category & platform
ANALYTICS PRODUCTS
EMERGING SPACES
vision
napkin plan
fi
rst-mover CIOs
end-to-end
new technology
conventional wisdom

43. NEW CATEGORY
43

44. “Platfora is an Hadoop-only, big data discovery platform that includes data preparation and visualisation.
Its goal is to empower business users to blend, transform and integrate data sets to, as part of exploratory analyses which produce stunning visualisations on PBs
of data. Yes, that 's a P.
It 's neither an ETL tool nor an advanced analytics tool and does not pretend to be either, although future releases had alluded to non-Hadoop data source
integration and machine learning capabilities.
Think of Platfora as a combination of superb tools like Trifacta and ZoomData. Platfora 's value proposition can be summarised in a simple sketch comparing the
old-world vs new world approach to data engineering for insight.”
https://www.kainos.com/insights/news/why-workdays-acquisition-of-platfora-makes-sense
CATEGORY EVOLUTION

45. “Platfora is an Hadoop-only, big data discovery platform that includes data preparation and visualisation.
Its goal is to empower business users to blend, transform and integrate data sets to, as part of exploratory analyses which produce stunning visualisations on PBs
of data. Yes, that 's a P.
It 's neither an ETL tool nor an advanced analytics tool and does not pretend to be either, although future releases had alluded to non-Hadoop data source
integration and machine learning capabilities.
Think of Platfora as a combination of superb tools like Trifacta and ZoomData. Platfora 's value proposition can be summarised in a simple sketch comparing the
old-world vs new world approach to data engineering for insight.”
https://www.kainos.com/insights/news/why-workdays-acquisition-of-platfora-makes-sense
CATEGORY EVOLUTION

46. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
~5 years

47. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING

48. DATAMEER ANNOUNCES $40M INVESTMENT AS IT PIVOTS AWAY FROM HADOOP ROOTS
“It’s never easy pivoting like this, but the investors are likely
hoping that the company can build on its existing
customer base, while taking advantage of the market
need for data science processing tools. Time will tell if it
works.”
https://techcrunch.com/2019/10/29/datameer-announces-40m-investment-as-it-pivots-away-from-hadoop-roots/
+ =

49. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
~5 years

50. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING

51. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Joe Lamantia | Product Strategist: Oracle Endeca Big Data Discovery 51
Featurize
Wrangle
Visual
Analysis
Interactive
Queries
Discovery Modeling
Data Application
Acquire Ingest
& Clean
Manage &
Update
Model Train
Evaluate
Update
Build
Train
Deploy
Monitor
Store &
Expose
Discovery Workbenches
BDD (now)
ML services
Oracle Machine Learning
Discovery & Modeling Platform
BDD & ML (combined analysis offering ?)
New Category
$$$$

52. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
~8 years

53. ANALYTICS PRODUCTS
EMERGING SPACES

54. ANALYTICS PRODUCTS
EMERGING SPACES
$4 BILLION

55. PRODUCT STRATEGY $43B
EMERGING SPACES
NEW
PRODUCTS
ANALYTICS

56. CLARITY
SPEED
TIMING
MOMENTUM
IMPACT
=

57. OPPORTUNITY
ASSESSMENT
PRODUCT
INVESTMENT
PRODUCT
DISCOVERY
PRODUCT & PORTFOLIO
PLANNING
CYCLE TIME
~12 years

60. THANK YOU!
CALL ME…
joe.lamantia@gmail.com
joelamantia.org
https://www.youtube.com/joelamantia
https://www.slideshare.net/moJoe/presentations
https://www.linkedin.com/in/digitaljoelamantia/