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How will we know? Investigating the future of knowledge
production, consumption and sharing
By the National Research Council Canada
ICSTI Insights series – February 2015
Knowledge Management
How will we know? Investigating the future of knowledge
production, consumption and sharing
June 25 2014
You are invited to spend time
in the not-so-distant future
Knowledge management practices and initiatives are
challenged by, and evolving through, the increasing
complexity and interconnectedness of the knowledge
domain, and the need for new KM paradigms and tools that
combine and extend the techno- socio-cultural foci of
traditional KM activities.
This document has been prepared as a backgrounder to set
the stage and inspire discussion by addressing the following
question:
What are the major
trends that have the
potential to affect the
future of knowledge
production, consumption
and sharing?
This research explored the interplay between various trends influencing the evolution of knowledge work. As we did, we continuously bumped into a set of tightly meshed contextual factors underlying each of these trends, pointing to the value of relationships in the creation of
knowledge and business value.
These factors highlighted an under-
lying tension between:
• Trust and fear
• Collaboration and competition
• Transparency and control
(privacy/security)
These factors reverberate in various
social, technological, ethical, legal,
economic, political and environmental
fronts and are present both internally
between employees and externally in
relation to partners and competitors.
The following pages highlight five
thematic areas in which the foremost
trends and influences converge:
• The Porous Organization,
(Context - knowledge production)
• The Connected Nomads,
(Tools and processes affecting
future knowledge production
and consumption)
• Interactive Knowledge,
(Knowledge sharing and production)
• Augmented Insights,
(Information analysis and Foresight)
• The Rise of the Living Data,
(Data Management)
You will notice that we are “talking
from the future” within these thematic
areas, with a fluid time horizon ranging
from the very near future (as little as
1-2 years, as with mobile and social
technologies) to 15+ years down the
road (as with artificial intelligence and
automation of knowledge work).
The porous organization
Driven by the benefits of collaboration, organizational
knowledge is unlocked, circulating across organizations,
countries and sectors of society. Innovation is spurred by
encouraging non-specialists to think openly and share ideas
with the specialists capable of translating their visions into
reality. Transient advantages outperform stable competitive
advantage models. Scientific enterprises are transformed
from the primary producers of knowledge to facilitators of
collaborative innovation and managers of collective digital
assets. Trust and relationship management strategies top
the C-Suite agenda.
Intellectual property models redefined
Distributed IP ownership models have
emerged from the dynamic management
of strategic alliances and outsourcing
partnerships, thereby accelerating
knowledge flows and commercialization
processes. Knowledge mobilized through
social relations is valued as an economic
resource, with evolving sets of control
and profitability mechanisms.
Government RTOs are challenged by
financial restrictions and procurement
roadblocks.
Open government, open science, open data
Openness is a widely established working
model in science, government, and data.
After years of experiments, learning
initiatives and the arrival of the millennial
generation in the workplace, models of
open dialogue and stakeholder engage-
ment drive evolution in science and
government. Participative user-centered,
public-private-people partnerships
innovation models such as living labs and
open labs replace tech transfer programs
and drive many R&D initiatives.
Boundaries are vanishing
Distance, frontiers and delays have
vanished - everything is local and
immediate. The push for openness,
the complexity inherent in the scientific
issues of the day, and the global nature
of business are conspiring to blur
institutional boundaries. Knowledge
flows are shifting from top-down to
multi-directional, with information
being shared freely within and between
organizations and the public. Leadership
is expected from everyone.
The connected nomads
Technology is transparent and centered on value and
knowledge creation. Processing power resides in the cloud
and fog, with augmented worker capabilities, facilitated
collaboration and social interaction. With career volatility and
instant team configurations, people are interacting and moving
from multiple organizational and social networks at a rapid
pace. These changes give rise to many new forms of virtual
interactive collaboration as knowledge products are produced
through social media-like tools. With smaller, intuitive, wear-
able and sensor-packed devices, people are immersed in
personalized information feeds, networked everywhere,
in real-time. Virtual currencies and the sharing economy
account for an even greater percent of economic activities.
Social networks and communities
Social technologies used for business
purposes are having an impact on
practice, engagement, and expertise.
They cluster functionality around
sensemaking, unleash creative forces
and foster adaptive working relationships
and group dynamics. Connecting and
sharing of information across organiza-
tional and global boundaries raises the
productivity of knowledge workers by
20-25% through streamlining commu-
nication and collaboration, breaking
down silos and extending the company’s
knowledge and expertise networks.
Mobile technology
Communication is instantaneous at
marginal costs. Advanced wireless
networks offer seamless transition
between office and home mobile
applications (apps) and data services.
Mobile apps improve productivity and
delivery of service by integrating
communication more directly into
distributed knowledge work and
providing continuous interaction with
colleagues and customers. Everyone
and everything is expected to always
be “on” and accessible. Access
permissions, data security, merging
corporate and personal content and the
use of unsanctioned IT services are the
critical parameters of operational
environments.
Virtual workers career fluidity
Global virtual workers are prevalent, with
contractors and collaborators moving
swiftly from one organization to another,
rapidly connecting and accessing the
expertise and knowledge base of
organizations. Developing a shared
language across communities replaces
knowledge capture and transfer issues
as a knowledge management focus.
Employers compete for talent as corpo-
rate values are scrutinized by candidates.
Personal resumes are replaced by web
profiles and social-based rating systems.
Interactive knowledge
Knowledge work has become interactive for more
employees who, without specialized training, engage in self-
serve intelligence gathering and routine analysis through
personalized dashboards. Advanced collaborative analytics
heightens the value of engagement with clients and partners
outside of the organization. Decision making and innovation
processes are being transformed through easier access to
collective knowledge and collaboratively created insights.
Crowdsourcing allows for the influx of new knowledge and
greater connections within and beyond corporate boundaries.
Collaborative analytics and decision making
Intelligence analysis and decision support
are performed through collaborative and
iterative data modelling with clients and
experts. With integration of visualization
and collaboration directly into knowl-
edge work tools, the focus stays on the
analysis, and the work flow and produc-
tivity are no longer interrupted as with
stand-alone collaboration tools (e.g.
chat, email). Participation facilitates
sharing and socializing of findings
and decisions.
Crowdsourced Innovation
Knowledge is widely dispersed and the
suite of expertise needed to bring new
ideas to fruition is rarely housed in a
single organization. The creation of
intelligence and innovation takes a
distributed, participatory approach,
reducing costs and accelerating time to
market for those organizations willing
to crowdsource their innovating.
Knowledge work is increasingly per-
formed via participative models as the
Work shifts from searching for contribu-
tions to synthesizing and building upon
or co-creating outputs within and for
the community.
Plugged-in agile knowledge
Data science moves from the specialist
to a range of employees. Workers make
use of self-serve, agile analytic tools and
expect flexibility and usability from their
dashboards. Analytics is embedded in
transactional systems and data usage is
plugged into day-to-day operations and
decision making, ultimately impacting
financial, organizational and personal
performance.
Augmented insights
The combination of advanced analytics, interactive
visualizations and artificial intelligence autonomously finds
new patterns in complex data and delivers personalized,
prescriptive advice. Scientific discoveries and business
efficiencies are increasingly made through automated
identification of connections in unstructured data, challenging
the role of theory and the human role in interpreting know-
ledge. Human intuition adds value to strategic decisions and
business innovation. Predictive and prescriptive analytics are
incorporated into less than 25% of business analytics projects
but deliver at least 50% of the business value.
Artificial intelligence (AI)
Advances in computational speed,
machine learning, and natural user
interfaces allow computers to aggregate
information faster than the human brain
and interpret and evaluate the results.
Machine learning excels at the complex
analytics used to monitor activities,
understand the root causes of issues as
they arise and accurately forecast future
trends on the horizon. AI increasingly
handles tactical and operational decision
making, freeing knowledge workers and
managers to focus on the strategic.
Prescriptive analytics
A rise in prescriptive analytics will
go beyond generating foresight and
anticipating the future (predictive
analytics), to define a preferred course
of action for reaching future goals.
Sophisticated analytic tools augment
knowledge workers’ skills, supporting
tasks that rely on complex analyses,
subtle human judgments and creative
problem solving.
Knowledge-work automation
Advances in AI will make it possible
to automate many knowledge workers’
tasks that have long been regarded as
impossible or impractical for machines to
perform. The greatest benefits are found
in applying knowledge work automation
to areas that boost employee productiv-
ity, creativity and allow for bottom up
innovation. Managers have learned to
let the organization run instead of con-
trolling it, and have spent the last
decades transitioning people and
departments affected by the complete
replacement of knowledge workers
by machines.
The rise of the living data
Data no longer lives in static repositories. Data has been
restructured into dynamic ecosystems with open access and
open standards to ensure accessibility and interoperability
on any platform, by anyone. Data is managed using
ontologic and semantic structures, making search and
analysis more meaningful and more effective for non-
specialists. Datasets, both big and small are routinely mined
to produce meaningful, actionable analysis and insight.
Information governance
With records being born digitally and an
ongoing redefining of what constitutes
a ‘record’, records management shifts to
information governance, focussing on
proactively managing data and informa-
tion throughout their lifecycles. Works-
in-progress, structured, unstructured and
semi-structured information all require
governance including protection from
deletion, unauthorized use, and pre-
mature disclosure. Open standards are
implemented across the board to address
preservation and interoperability issues.
Data ecosystems
As data expands in volume, variety and
velocity, data curation is moving to the
forefront, with machine readability and
findability becoming critical concerns
for records/data managers. Traditional
databases and information sources are
replaced by living data ecosystems.
Automated ontologies, semantic web
and inference technologies enhance
search relevance, real-time analytics and
data visualisation by building connections
between information/data sources into
the structure of each system.
Data ownership and access
Data access and ownership is democra-
tized as usage restrictions are minimized
for both published research and their
associated data sets. A change in corpo-
rate and scientific culture is underway
to adapt to the ideals of open knowl-
edge sharing. Competitive edge and
value creation is driven by organizational
capacities to identify, merge, analyse and
integrate findings from a multiplicity of
data sources.
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