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Ten 2015 Technology Predictions
1
Dr. Rado Kotorov
Chief Innovation Officer, Information Builders
Rick F. Van der Lans
Independent Analyst, R20/Consultancy BV
15 January 2015
1: IoT Gains Momentum
 Prediction: IoT Will expand
significantly in manufacturing,
energy sector, healthcare,
logistics, and other industries.
 Fact: GE has generated $1 billion
in incremental revenues form IoT
and PaaS in 2013.
 Action: IoT data can be cost
effectively gathered in columnar
high performance databases (like
Hyperstage) for quick analysis,
discovery, and experimentation.
2
Imagine the possibilities in a hyper-connected world…..
1: IoT Gains Momentum
 Connected devices include
thermostats, cars, lights,
alarms, shoe insoles
 Car industry example
 Currently each vehicle has
60-100 sensors
 Future: 200 sensors per car
 2020: Total 22 billion sensors
used in the automotive
industry
 Cisco: 37 billion new things will
be connected by 2020
3
Imagine the possibilities in a hyper-connected world…..
2: Dealing with the data deluge
 Prediction: Most data will be
analyzed before it is fully processed
and put into a data warehouse.
Social and unstructured data are
becoming more analytically
accessible.
 Fact: The volume of business data
worldwide, across all companies,
doubles every 1.2 years.
 Action: Adopt a data lake approach
– access and analyze first, and
integrate later. Use search-BI tools
to create apps for structured and
unstructured data analytics.
4
Imagine when data flows in from everywhere…
2: Dealing with the data deluge
 Tools must allow us to sort and
find quickly
 Complex, multi-step
architectures are not flexible
enough
 Integrated solutions required
to avoid reinventing the wheel
5
Imagine when data flows in from everywhere…
3: Apps and self-service
 Prediction: Most companies will
implement different self service
for different stakeholders – tools
for the analysts and apps for front
line employees.
 Fact: BI has a less than 30
percent adoption rate in the
enterprise today.
 Action: Turn analysis and insights
into custom InfoApps for on-the-
job decision support.
6
Analysis and insights
create opportunities!
Operational apps create
value by changing behavior!
3: Apps and self-service
 Self-Service for the masses
 Self-service is moving
upstream and must move
downstream
7
Analysis and insights
create opportunities!
Operational apps create
value by changing behavior!
4: The analytics skills gap
 Prediction: Companies will not
be able to fill the skill gap.
Therefore, CDOs and CAOs will
try to commoditize analytics.
 Fact: The demand for people
with deep analytical skills is 10
times greater than supply.
 Action: Commoditize analytics
with infoapps and appstore
like portals for employees.
8
Finding and hiring good data scientists…
4: The analytics skills gap
 Data is still considered a by-
product
 Data is produced for internal
consumption only
 Data must be regarded as a
key product
9
Finding and hiring good data scientists…
5: Machine learning
 Prediction: To bridge the skills
gap and to cope with highly
dimensional data deluge
companies will adopt machine
learning
 Fact: IBM Watson is here and
ready for business
 Action: Use machine learning
in combination with data
discovery to explore the field
and provide faster time to
market analytics
10
“Robots will be smarter than humans within 15 years,
Google’s new chief on artificial intelligence has claimed.”
5: Machine learning
 Many BI systems only do
reporting
 ROI of reporting hard to
calculate
 Analytics is the way to go
11
“Robots will be smarter than humans within 15 years,
Google’s new chief on artificial intelligence has claimed.”
6: Master data management (MDM)
12
The quest for the golden record…
 Prediction: The implementation
cycles for MDM will shrink
drastically from a couple of years
to a few months with new and
innovative approaches.
 Fact: Miscoding and billing errors
from doctors and hospitals
totaled $20 billion in USA.
 Fact: The average billion-dollar
company is losing $130 million a
year due to poor data
management.
 Action: Adopt an MDM platform
with built in templates, wizards &
best practices approach.
6: Master data management (MDM)
13
The quest for the golden record…
 MDM will only be a success if
it’s setup in a flexible way,
technologically and
organizationally
Vote:
How successful is your MDM Strategy?
14
7: Data warehouse decline
 Prediction: Unmodelled data
analytics will grow due to
competitive pressure. NoSQL,
Columnar and in-memory offer
alternatives to DW for many use
cases.
 Fact: Relational databases still
dominate the market, but 30% to
35% of enterprises have invested
in big data. Is it a tipping point?
 Action: Conduct powerful
analytics against columnar, in-
memory, and Hadoop using
standard query and analysis tools.
15
Imagine how quickly data can be analyzed if data modeling
and schemas were not necessary….
7: Data warehouse decline
 The future is for the Logical
Data Warehouse
 Multiple data sources using
different storage
technologies together
forming one logical database
 Big data is too big to move
16
Imagine how quickly data can be analyzed if data modeling
and schemas were not necessary….
8: Tech gets personal
 Prediction: The benefits of
predictive analytics are great, but
many companies will be lured to
buy easy to use tools, ignore the
pitfalls, and fail.
 Fact: Deloitte research shows
more than 60% of companies
have experienced project failure.
 Action: Implement verification
processes and commoditize
analytics with expert certified
InfoApps.
17
Is your prediction scientifically sound?
Vote:
What percentage of your users are accessing BI
on mobile devices?
18
9: Mobile workforce
 Prediction: Gartner predicts that
over 50% of BI users will be
mobile users.
 Fact: BI Scorecard: “BI adoption as
a percentage of employees
remains flat at 22%, but
companies that have successfully
deployed mobile BI show the
highest adoption at 42% of
employees.”
 Action: Offer self-service BI with
an appstore like portal and
InfoApps.
19
If BI and analytics could be downloaded from an appstore?
9: Mobile workforce
 The ROI of mobile analytics is
not clear
 Mobile analytics and
consumer-driven analytics
could become a marriage
made in heaven
20
If BI and analytics could be downloaded from an appstore?
Vote:
What percentage of your users do you think will
be accessing BI on mobile devices in 2 years
time?
21
10: The CIO transformed
 Prediction: Successful CIOs will
transform their roles into
business leadership roles and
eventually become CEOs.
 Fact: Of 384 hospitals only one
selected the CIO as the next CEO
in 2014.
 Fact: GE CEO says, “Every
company will be a software
company.”
 Action: Use software to transform
processes, organizational culture,
customer facing experience, and
to monetize data.
22
The rise of the techno-leader
10: The CIO transformed
 More in-depth knowledge of
technology needed on c-level
 What can we learn from the
CEOs of Google, Facebook, and
Twitter?
23
The rise of the techno-leader
24
Discussion
Further Resources
Blog post: Gartner’s 2015 Tech Trends Lead To
Pervasive BI
Webinar: Big Data + Enterprise Data = Big
Information, 15 January 2015, 14.00 GMT /
15:00 CET
25
Questions?
26
Rick F. van der Lans, R20/Consultancy BV
@rick_vanderlans
Rado Kotorov, Information Builders
@rado_kotorov
View a recording of the webinar online here.
27

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Ten 2015 Technology Predictions

  • 1. Ten 2015 Technology Predictions 1 Dr. Rado Kotorov Chief Innovation Officer, Information Builders Rick F. Van der Lans Independent Analyst, R20/Consultancy BV 15 January 2015
  • 2. 1: IoT Gains Momentum  Prediction: IoT Will expand significantly in manufacturing, energy sector, healthcare, logistics, and other industries.  Fact: GE has generated $1 billion in incremental revenues form IoT and PaaS in 2013.  Action: IoT data can be cost effectively gathered in columnar high performance databases (like Hyperstage) for quick analysis, discovery, and experimentation. 2 Imagine the possibilities in a hyper-connected world…..
  • 3. 1: IoT Gains Momentum  Connected devices include thermostats, cars, lights, alarms, shoe insoles  Car industry example  Currently each vehicle has 60-100 sensors  Future: 200 sensors per car  2020: Total 22 billion sensors used in the automotive industry  Cisco: 37 billion new things will be connected by 2020 3 Imagine the possibilities in a hyper-connected world…..
  • 4. 2: Dealing with the data deluge  Prediction: Most data will be analyzed before it is fully processed and put into a data warehouse. Social and unstructured data are becoming more analytically accessible.  Fact: The volume of business data worldwide, across all companies, doubles every 1.2 years.  Action: Adopt a data lake approach – access and analyze first, and integrate later. Use search-BI tools to create apps for structured and unstructured data analytics. 4 Imagine when data flows in from everywhere…
  • 5. 2: Dealing with the data deluge  Tools must allow us to sort and find quickly  Complex, multi-step architectures are not flexible enough  Integrated solutions required to avoid reinventing the wheel 5 Imagine when data flows in from everywhere…
  • 6. 3: Apps and self-service  Prediction: Most companies will implement different self service for different stakeholders – tools for the analysts and apps for front line employees.  Fact: BI has a less than 30 percent adoption rate in the enterprise today.  Action: Turn analysis and insights into custom InfoApps for on-the- job decision support. 6 Analysis and insights create opportunities! Operational apps create value by changing behavior!
  • 7. 3: Apps and self-service  Self-Service for the masses  Self-service is moving upstream and must move downstream 7 Analysis and insights create opportunities! Operational apps create value by changing behavior!
  • 8. 4: The analytics skills gap  Prediction: Companies will not be able to fill the skill gap. Therefore, CDOs and CAOs will try to commoditize analytics.  Fact: The demand for people with deep analytical skills is 10 times greater than supply.  Action: Commoditize analytics with infoapps and appstore like portals for employees. 8 Finding and hiring good data scientists…
  • 9. 4: The analytics skills gap  Data is still considered a by- product  Data is produced for internal consumption only  Data must be regarded as a key product 9 Finding and hiring good data scientists…
  • 10. 5: Machine learning  Prediction: To bridge the skills gap and to cope with highly dimensional data deluge companies will adopt machine learning  Fact: IBM Watson is here and ready for business  Action: Use machine learning in combination with data discovery to explore the field and provide faster time to market analytics 10 “Robots will be smarter than humans within 15 years, Google’s new chief on artificial intelligence has claimed.”
  • 11. 5: Machine learning  Many BI systems only do reporting  ROI of reporting hard to calculate  Analytics is the way to go 11 “Robots will be smarter than humans within 15 years, Google’s new chief on artificial intelligence has claimed.”
  • 12. 6: Master data management (MDM) 12 The quest for the golden record…  Prediction: The implementation cycles for MDM will shrink drastically from a couple of years to a few months with new and innovative approaches.  Fact: Miscoding and billing errors from doctors and hospitals totaled $20 billion in USA.  Fact: The average billion-dollar company is losing $130 million a year due to poor data management.  Action: Adopt an MDM platform with built in templates, wizards & best practices approach.
  • 13. 6: Master data management (MDM) 13 The quest for the golden record…  MDM will only be a success if it’s setup in a flexible way, technologically and organizationally
  • 14. Vote: How successful is your MDM Strategy? 14
  • 15. 7: Data warehouse decline  Prediction: Unmodelled data analytics will grow due to competitive pressure. NoSQL, Columnar and in-memory offer alternatives to DW for many use cases.  Fact: Relational databases still dominate the market, but 30% to 35% of enterprises have invested in big data. Is it a tipping point?  Action: Conduct powerful analytics against columnar, in- memory, and Hadoop using standard query and analysis tools. 15 Imagine how quickly data can be analyzed if data modeling and schemas were not necessary….
  • 16. 7: Data warehouse decline  The future is for the Logical Data Warehouse  Multiple data sources using different storage technologies together forming one logical database  Big data is too big to move 16 Imagine how quickly data can be analyzed if data modeling and schemas were not necessary….
  • 17. 8: Tech gets personal  Prediction: The benefits of predictive analytics are great, but many companies will be lured to buy easy to use tools, ignore the pitfalls, and fail.  Fact: Deloitte research shows more than 60% of companies have experienced project failure.  Action: Implement verification processes and commoditize analytics with expert certified InfoApps. 17 Is your prediction scientifically sound?
  • 18. Vote: What percentage of your users are accessing BI on mobile devices? 18
  • 19. 9: Mobile workforce  Prediction: Gartner predicts that over 50% of BI users will be mobile users.  Fact: BI Scorecard: “BI adoption as a percentage of employees remains flat at 22%, but companies that have successfully deployed mobile BI show the highest adoption at 42% of employees.”  Action: Offer self-service BI with an appstore like portal and InfoApps. 19 If BI and analytics could be downloaded from an appstore?
  • 20. 9: Mobile workforce  The ROI of mobile analytics is not clear  Mobile analytics and consumer-driven analytics could become a marriage made in heaven 20 If BI and analytics could be downloaded from an appstore?
  • 21. Vote: What percentage of your users do you think will be accessing BI on mobile devices in 2 years time? 21
  • 22. 10: The CIO transformed  Prediction: Successful CIOs will transform their roles into business leadership roles and eventually become CEOs.  Fact: Of 384 hospitals only one selected the CIO as the next CEO in 2014.  Fact: GE CEO says, “Every company will be a software company.”  Action: Use software to transform processes, organizational culture, customer facing experience, and to monetize data. 22 The rise of the techno-leader
  • 23. 10: The CIO transformed  More in-depth knowledge of technology needed on c-level  What can we learn from the CEOs of Google, Facebook, and Twitter? 23 The rise of the techno-leader
  • 25. Further Resources Blog post: Gartner’s 2015 Tech Trends Lead To Pervasive BI Webinar: Big Data + Enterprise Data = Big Information, 15 January 2015, 14.00 GMT / 15:00 CET 25
  • 26. Questions? 26 Rick F. van der Lans, R20/Consultancy BV @rick_vanderlans Rado Kotorov, Information Builders @rado_kotorov
  • 27. View a recording of the webinar online here. 27