The European Business Review - July-August 2019

http://www.europeanbusinessreview.com 9

• Business development executives
  provide them with stories and
  anecdotes. This lack of compu-
  tational savvy gap near the top of
  hierarchical organisations hasbeen
  a problem for every ‘IT’ wave in
  business dating back tothe 1990's
  
  
  • but the widespread use of ML
  
  
  
  

see differently, think differently and

feeldifferently:

• Machine learning programmerswant
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  canuseto traintheiralgorithms;but –


• &KLHI VWUDWHJ\RIÀFHUVDQGEXVLQHVV
  developmentexecutivessupplythem
  withaspirationalgoalsphrasedin the
  fuzzy language thatcodersroutinely
  call ‘corporatese’.


• Big data, multi-user platform
  developers want clear allocations
  of decision rights among the end
  users of the platforms that specify
  who gets access to what infor-
  mation when and who gets access
  to information about the identities
  of users having access to infor-
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  user privacy that are achievable
  given the precision and reliability of
  the statistical analyses these data are
  used for; but –


• Clients will only talk about broad
  principles of fairness, diversity and
  inclusivity that should be used to
  design the platform they are contem-
  plating purchasing, but do not specify
  these concepts to levels of precision
  that makes them amenable to
  algorithmic implementations.

We need to bridge the skills gap that

leads to this self-perpetuating commu-

nication debacle. Organisations need

people who can talk to both people

and machines and people who can

talk to people who talk to machines

to inhabit their upper echelons. Key

to competent communication and

collaboration is a common language

and pattern of reasoning that makes

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Structuring Modulating Understanding Paraphrasing Energising

Committing Asserting Inquiring

Figure 1

PEOPLEWHOCANTALKTOOTHERPEOPLE

PEOPLEWHOCAN

TALKTOOTHER

PEOPLEANDTO

MACHINES

PEOPLEWHOCAN

TALKTO(PEOPLE

WHOCANTALK

TOMACHINES)

PEOPLEWHOCANTALKTOMACHINES

algorithms working on large data

sets exacerbates this problem and

brings it to a boil.

The algorithmic skills gap arises

because people belonging to these two

groups cannot speak to one another in

productive ways. They aim differently,

The algorithmic skills gap arises because people belonging to these

two groups cannot speak to one another in productive ways. They aim

differently, see differently, think differently and feel differently.