After AI, it is time for
‘decision intelligence’
In 2018, Google defined ‘Decision
Intelligence’ as a key element of further competing in an increasingly digital
economy and society. Cassie Kozyrkov, Chief Decision Scientist at Google wrote
in the same year: “Decision intelligence is a new academic discipline concerned
with all aspects of selecting between options. It brings together the best of
applied data science, social science, and managerial science into a unified
field that helps people use data to improve their lives, their businesses, and
the world around them.”
Why do Google and other tech
companies like Alibaba, best known for their capabilities to deal with big data
and artificial intelligence, put suddenly such a strong emphasis
‘old-fashioned’ social and managerial sciences?
An answer might be that the
business environments of companies around the world are progressively becoming
more volatile, uncertain, complex and ambiguous, often denoted by the acronym
‘VUCA’.
In such a VUCA world, the business
models of all sorts of companies are increasingly challenged by developments
such as swift policy changes, fast macro-economic and social trends as well as
disruptive technological innovations and advancements. One such major
disruptive development is the shift to an increasingly knowledge-based and
digitalized society and economy, also in India.
Information Processing
In such dynamic environments, the
information requirements for senior executives and other managers to formulate
competitive strategies are exacerbated. Senior executives and other decision
makers increasingly rely on big data-driven approaches.
At the same time, the way
individuals, groups, organisations, and industries work and collaborate are
being transformed by the capacity to store, communicate, and compute
information – resulting in new and different types of digital enterprises.
However, with an almost unlimited
access to an array of information sources, senior executives and their middle
managers also face a host of challenges, such as potential information
overloads leading to biases in judgements, costs associated with managing vast
information, and the risk of being distracted from truly relevant information.
From a career perspective, any
future leader in a digital enterprise must therefore be proficient at filtering
insights that really matter from the information (over)loads they are exposed
to.
Almost 30 years ago, scholars had
already identified that a misfit between the information requirements of a
company and the way it gathers and processes information increases the
likelihood of accidentally neglecting relevant factors, filtering out important
information, or relying on misleading clues.
Recent research has further
empirically confirmed that a better fit between the various levels of
information requirements of a company and its information processing capacities
leads to superior levels of strategic insights, and subsequently to firm
performance.
The same research also confirmed
that there exist different ideal information gathering and processing profiles
depending on the VUCA environment a company operates in.
However, today’s reality for most
executives is still well described by what Prof. James G. March wrote already
25 years ago: “Decision makers and organizations (a) gather information but do
not use it, (b) ask for more and ignore it, (c) make decisions first and look
for relevant information afterwards, and (d) gather and process a great deal of
information that has little or no relevance to decisions”.
Combining social sciences with data sciences
Some years ago, we have therefore
developed the Decision Intelligence NavigatorTM to support companies in
achieving and sustaining competitive advantages in a VUCA world.
It stands for a different
perspective when thinking about strategic analyses and the creation of
competitive advantages – especially for digital enterprises which often face
“the winner-takes-it-all” markets and where any strategic mistake can ruin the
company.
Although Decision Intelligence is
pioneered by technology giants like Google or Alibaba it is less about data
analytics or artificial intelligence but about the capability to combine the
right analysis frameworks from social and managerial sciences or engineering
with the most suitable information gathering and processing solutions.
According to our experience with
multinationals, SMEs and start-ups the effective application of Decision
Intelligence in a company builds on the four elements of the Decision
Intelligence NavigatorTM:
Decision context: Do the executives in a company understand the
decision challenges they face and sufficiently reflect on what kind of
frameworks and intelligence (data, information, knowledge) they need?
Framework currency: Do executives master enough social sciences and
managerial frameworks to select the most appropriate analysis concepts for any
decision challenge they face in their organisations?
Intelligence access: Does a company provide access to the necessary
data analytics tools and databases to gather and process the necessary
intelligence?
Decision proficiency: Are executives able to turn strategic insights
into effective decisions and implementation plans?
While Decision Intelligence is
crucial to a career in any company, it is most likely to be the most important
capability of any executive in a digital enterprise where all sorts of data,
information or knowledge are basically the only resources that can create a
competitive advantage. Thus, if you aim at a career in a digital enterprise or
tech giant, do not underestimate the value of social sciences to reach the top.
Source | Hindustan
Times | 12th February 2020
Regards!
Librarian
Rizvi Institute of Management
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