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Data Monetization
Webinar Series / Expert Sessions
Henrik Gabs Liliendahl, Product
Data Lake
Dr. Rado Kotorov, Information
Builders
Data Revolutions and
Their Impact on Business2
3
 The invention of writing allowed
for the collection and store of
DATA
 This lead to:
 Sales records
 Aggregate results
 Tax collection
 This lead to:
 Trade expansion
 Cities
 Large construction projects
4
 Invention of double entry
accounting in the 15th century:
 The general leger creates
undisputable record of
business transactions, profit
and losses
 Agency trade emerges
 Then came the computer and
ERP Systems
 Then came Blockchain – the
universal ledger
Goal is to reduce transaction costs!
5
 The sensor –causing the 4th
industrial revolution
 Every process and activity is
under microscopic scrutiny
 Extreme insight into processes,
events and condiotions
The rush is to embed as
many sensors in
everything and reap the
opportunities.
6
 Physical Assets
Businesses
 Digital Businesses
7
Scale
Scope
Effect on Competition
 First mover advantage matters
 First mover scales fast
 Digital businesses expand
scope fast
Penetrate adjacent markets
at super speed
Hypercometition is the new
norm.
8
If you didn’t think that first
mover matters, here is how
Bezos titled his book
released in February 2018.
9
Three flavors of data monetization
Wrapping data around
products
Advanced analytics and
decision making
Selling data
Third party data First party dataSecond party data
10
Data management and data monetization
MDM
Supplier self-service Customer self-service
Self registrationSelf registration
Product data syndication Syndicated product information
Big reference data: Business directories, consumer/citizen information, location data
Business ecosystems
Social data:
profiles and
streams
Sensor data / IoT Data lakes
11
Quest no 1: Enterprise wide data management
Location master data as an example Wrapping location data around products
and services
Including location data in advanced
analytics and decision making
12
Quest no 2: Ecosystem wide data management
Example: Product data syndication
Trading products with
chaotic (often excel and
email based) data exchange
Trading products with shared data
wrapped around
13
Result: Product data monetization
Wrapping data around products
We do not always
want to be told
what to buy.
We want to know
what we are
buying.
14
Well known examples from best performers
Wrapping data around products and services
Two way
reviews
Data governance in
self-service
Poll Question
Webinar Series / Expert Sessions
The Vote Is Still Out on Self-Service BI
16
 Self-service failed to
deliver pervasive BI
 Less than 30%
penetration
 Operational people want
information but do not want
DIY analysis!!!
Analysis vs. Decision Making
17
 What and Why?
 Data and method
 Time consuming
 When and How?
 Clear choice options
 Quick decision
• Self-Service failed to deliver pervasive BI
• Less than 30% penetration
 Companies are significantly more aware of
the differences and benefits of Operational
BI for on the job decision making
FACT Change
Analysis is different from deciona making!
19
Make a decision in less than 6 min Make a fact based decision instantly
Aesthetics of data presentation Storytelling and Infographics
FACT Change
 Transition from showing just data to stories
 Conclusions jump at you
 Design builds emotional connecton
 Dashboards are boring
 Too many; all look the same
 Conclusions do not jump at you
User habits are changing Voice for mobile BI
FACT Change
Multiple ledgers, i.e.
each company
keeping its own
ledger, increase
transactions and
verification costs
The number of peer-
to-peer digital
transactions are
growing reapidly
A universal unbreakable ledger
increase the speed of transacting
and lowers the cost
FACT Change
Architecture to Support Survival in the Digital World
23
The digital enterprise technology platform
includes five interrelated systems with BI
and information management being at its
core:
 Scaling your BI out requires a deep
technology that is highly customizable &
secure
 Scaling your BI across requires robust
information management stack
Data Monetization
Webinar Series / Expert Sessions
Henrik Gabs Liliendahl, Product
Data Lake
Dr. Rado Kotorov, Information
Builders

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Data Monetization Expert Session Webinar

  • 1. Data Monetization Webinar Series / Expert Sessions Henrik Gabs Liliendahl, Product Data Lake Dr. Rado Kotorov, Information Builders
  • 2. Data Revolutions and Their Impact on Business2
  • 3. 3  The invention of writing allowed for the collection and store of DATA  This lead to:  Sales records  Aggregate results  Tax collection  This lead to:  Trade expansion  Cities  Large construction projects
  • 4. 4  Invention of double entry accounting in the 15th century:  The general leger creates undisputable record of business transactions, profit and losses  Agency trade emerges  Then came the computer and ERP Systems  Then came Blockchain – the universal ledger Goal is to reduce transaction costs!
  • 5. 5  The sensor –causing the 4th industrial revolution  Every process and activity is under microscopic scrutiny  Extreme insight into processes, events and condiotions The rush is to embed as many sensors in everything and reap the opportunities.
  • 7. 7 Scale Scope Effect on Competition  First mover advantage matters  First mover scales fast  Digital businesses expand scope fast Penetrate adjacent markets at super speed Hypercometition is the new norm.
  • 8. 8 If you didn’t think that first mover matters, here is how Bezos titled his book released in February 2018.
  • 9. 9 Three flavors of data monetization Wrapping data around products Advanced analytics and decision making Selling data Third party data First party dataSecond party data
  • 10. 10 Data management and data monetization MDM Supplier self-service Customer self-service Self registrationSelf registration Product data syndication Syndicated product information Big reference data: Business directories, consumer/citizen information, location data Business ecosystems Social data: profiles and streams Sensor data / IoT Data lakes
  • 11. 11 Quest no 1: Enterprise wide data management Location master data as an example Wrapping location data around products and services Including location data in advanced analytics and decision making
  • 12. 12 Quest no 2: Ecosystem wide data management Example: Product data syndication Trading products with chaotic (often excel and email based) data exchange Trading products with shared data wrapped around
  • 13. 13 Result: Product data monetization Wrapping data around products We do not always want to be told what to buy. We want to know what we are buying.
  • 14. 14 Well known examples from best performers Wrapping data around products and services Two way reviews Data governance in self-service
  • 15. Poll Question Webinar Series / Expert Sessions
  • 16. The Vote Is Still Out on Self-Service BI 16  Self-service failed to deliver pervasive BI  Less than 30% penetration  Operational people want information but do not want DIY analysis!!!
  • 17. Analysis vs. Decision Making 17  What and Why?  Data and method  Time consuming  When and How?  Clear choice options  Quick decision
  • 18. • Self-Service failed to deliver pervasive BI • Less than 30% penetration  Companies are significantly more aware of the differences and benefits of Operational BI for on the job decision making FACT Change Analysis is different from deciona making!
  • 19. 19 Make a decision in less than 6 min Make a fact based decision instantly
  • 20. Aesthetics of data presentation Storytelling and Infographics FACT Change  Transition from showing just data to stories  Conclusions jump at you  Design builds emotional connecton  Dashboards are boring  Too many; all look the same  Conclusions do not jump at you
  • 21. User habits are changing Voice for mobile BI FACT Change
  • 22. Multiple ledgers, i.e. each company keeping its own ledger, increase transactions and verification costs The number of peer- to-peer digital transactions are growing reapidly A universal unbreakable ledger increase the speed of transacting and lowers the cost FACT Change
  • 23. Architecture to Support Survival in the Digital World 23 The digital enterprise technology platform includes five interrelated systems with BI and information management being at its core:  Scaling your BI out requires a deep technology that is highly customizable & secure  Scaling your BI across requires robust information management stack
  • 24. Data Monetization Webinar Series / Expert Sessions Henrik Gabs Liliendahl, Product Data Lake Dr. Rado Kotorov, Information Builders