The document discusses how the roles of experts and scientific publishing are changing in an era of open science, sensors, and big data. It argues that expertise is becoming more collaborative and less hierarchical as knowledge moves faster globally. Experts will need to focus on developing hypotheses, understanding signals in data, and adapting to new requirements for delivering insights. Scientific publishers will need services that support collaborative innovation and make better use of open data and signals to build insight and discovery. They are encouraged to focus on hypothesis generation and testing over static reports and consider how to adapt their business models.

1. The New Role of Expertise
Open Science in a Web of Sensors, Senses and Semantics
31 October 2014 John Blossom, Shore Communications Inc.

2. About Shore
● Content Marketing Strategists
o For publishing and content technology
products & services in
enterprise and media markets
● We provide:
o Market research, intelligence & analysis
o Marketing strategy review and advice
o Go-to-market content and services
● Recognized:
o Twice-awarded EContent 100 Company
o SIIA CODiE – Best Media Blog
shorecominc.com

3. What is at issue?
The challenge of 21st century innovation

4. What is today’s SciTech challenge?
● Curation and copyright vs. innovation and interaction?
● Subscriber access vs public/open access?
● Impact ratings vs. social media “clout”?
All this, to be sure, and yet...

5. There is a broader issue in play.
● What is the value of expert insights in a global,
mobile real-time economy?
o How is that value realized through publishing?
o What forces are shaping its value in this economy
independent of publishing?

6. And huge implications if we blow it.
“Shareholder value” is not scaled to global
challenges

7. How do we get to “aha” better?
Does publishing today create value for discovery?

8. How do we monetize “aha” moments?
Is our expertise really paying off through discovery?

9. Other industries have faced this.
● What happened to financial publishing?
o Real-time insights in global markets trumped reports
o The most profitable analysis became automated
o Wide & equal access to public information sources

10. It seemed to have an impact.

11. Open builds proprietary advantages.
Source: Gartner, Inc.
The competitive cost of not adapting is high
o When customers’ money is spent better they respond

12. Is scientific publishing different?
● In some ways, yes. But not as much as you think.
o Your information isn’t there when they need it most
o So your customers are finding insights anyway
o They have to if they’re going to keep up
economically

13. So what is open science really about?
● It’s about adjusting the value of publishing
o Report/data distribution value = ￠
o Moving to the next right insight ASAP = $$$$$
o Getting the right feedback at the right time = $$$$$

14. Why impact ratings won’t save you.
● Careers move too fast for them
o Tenure-track positions not as economically valuable
● Research funding moves too fast for them
o International competition in a tight economy
= money flows fast to the fastest innovation

15. Why copyright won’t save you.
● May isolate content from its most valuable contexts
o Is this information really valuable right now?
o Can I extract meaning from it later efficiently?

16. Why libraries won’t save you.
● Information professionals face same expert
challenge
o Search engines push them into collaborative roles
o Global enterprises less likely to fund collections - and
their managers - that don’t produce tangible results

17. Soooo...why am I subscribing?
● “Because you have to” is not a growth strategy
o For your clients or for their clients
● Technical fixes won’t eliminate adaptive pressures
o Expertise creates value today in new patterns

18. A new approach to expertise
Confronting the shifting role of the
expert

19. Why today’s expertise is different
● Knowledge and discovery moves fast globally
o Reputation as guaranteed insight less certain
● Pushes experts into the realm of the collaborative
o As their hierarchical, technical problem-solver role is
automated, focuses more on peer idea exploration

20. Experts moving from...
● Creating economic value out of effective signals
o Driving innovation
o Exploiting more “blue skies”
o Accelerating marketing
o Support as research
EXPERTS
(Valuable Hypotheses) (Accepted Hypotheses)
● From expert-driven
decision making to
collaborative, data-driven
decision making
DATA SYSTEMS
● Marketing before markets are defined
(No Hypotheses) (Applied Hypotheses)
Basic diagram source: Cognitive Edge Pty.
Temporary
Collaboration

21. Experts moving to...
● Creating economic value out of effective signals
EXPERTS
in perpetual
collaboration
o Driving innovation
o Exploiting more “blue skies”
o Accelerating marketing
o Support as research
● From expert-driven
decision making to
collaborative, data-driven
decision making
SYSTEMS
SYSTEMS
DATA via
perpetual systems
(SIGNAL)
● Marketing before markets are defined
Basic diagram source: Cognitive Edge Pty.
$
(Valuable Hypotheses) (Accepted Hypotheses)
(No Hypotheses) (Applied Hypotheses)

22. ...But publishing services aren’t there.
● Focused mostly on certifying knowledge for the
domain of the “complicated”
o A domain that’s moving towards automation!
● Ignoring the rise of more easily absorbed data
o As chaotic domains become understood as signal

23. The role of signal for discovery
A new information economy emerges

24. What is signal?
● sig·nal ˈsignəl/ noun
“A gesture, action, or sound that is used to
convey information or instructions”
● Clear status & action indicators
derived from complex inputs
● Highly actionable
information at the
right time & place

25. Everything can generate signal
Internet Protocol Version 6 provides
340 trillion trillion trillion addresses!
EVERY THING in the world
can send signal via the Web
and Web-aware networks
The world IS signal

26. The economic impact of signal
FROM:
Information
Autocategorization
Building data sets
Extracting entities
Computing
Analysis

27. Signal: Everything is analyzable
Rapidly scalable resources
Massive data sets
Real-time artificial intelligence
Signifying vs. storage + retrieval

28. Signal: Everything has syntax
Knowing relationship, focus and intent in real-time

29. Signal: Everything is predictive
Mass prediction
of personal
demand & action

30. Signal: Redefining innovation

31. An economy of signal-driven markets
Understand and fulfill unique demands at scale
before others even see them
...using less time, fewer resources
and more effective filtering of options

32. An economy of tailored production
Signal drives scalable micromarkets rapidly

33. How does signal change science?
● The world is modeling itself in data constantly
o Dynamic hypothesis formulation, testing & application
● Avoiding data fragmentation is key
o Public signal resources can increase research efficiency

34. What to do?
Responding to new requirements for
delivering discovery and insights

35. What to do now?
● Experts focused on collaboration need a new
generation of publishing and discovery services
o Collaborative innovators more dispersed and less likely to
have budget for collection access
o Need to address opportunities & challenges of both signal and
automated insight technologies that act on hypotheses quickly

36. What publishers like to look like.
Distribution
Editorial
Text
Data Media

37. Where the Web moved you.
Demand
Search/
Distribution
Social
Sites/Apps x Your Stuff

38. Where you could be moving.
Your Stuff
Sensors/ x
Big Data x
Predictive
Services
Analytics
Web &
Gov’t
Stuff

39. Your to-do list
1. Focus on developing systems for collaborative experts
o “How can we develop good hypotheses for action more efficiently?”
o “What is data saying now?”
o “What are ‘failures’ telling us?”
2. Consider adaptive responses
o Not just technical/tactical issues
o What “DNA” is really essential to retain
o How can people & institutions adapt with integrity
3. Use open science to lower costs for collaboration
o Lower the cost of data to build ROI-driven insight services

40. A closing thought...
.”..Something can be a real failure until it’s not. It’s just an absolute dud until it’s a hit.
So you have to be able to sense those early indicators of success, and the
leadership has to really lean in and not let things die on the vine. When you have a $70
billion business, something that’s $1 million can feel irrelevant. But that $1 million
business might be the most relevant thing we are doing.”
- Satya Nadella, CEO, Microsoft

41. For Follow-Up
PHONE
(+01)203.293.8511
EMAIL
jblossom@shorecominc.com
WEB
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TWITTER/GOOGLE+
@jblossom google.com/+JohnBlossom
POST
John Blossom
President
Shore Communications Inc.
80 Talcott Road
Guilford, CT 06437-5002 USA