Knowledge measures needed

Management guru Peter Drucker once said: “In the knowledge economy, everyone is a volunteer, but we have trained our managers to manage conscripts” – he might have added that managers act this way because they lack the measures and understanding needed to maximise the knowledge productivity of their teams

It’s another productivity gap afflicting most organisations and nations

Yet, according to Thomas Stewart in The Wealth of Knowledge: “Knowledge is the most important factor in production (business) and knowledge assets the most powerful producers of wealth – even your toothpaste is more the product of knowledge than any other input resource because R&D and marketing costs comprise more than 50% of its total cost”

Winning companies nowadays are not just cash-rich but corporate-knowledge-rich too – many winners are even ‘tangible-assets-poor’

Corporate knowledge (K) includes:

  • Designs, patents, formulae, copyrights, trademarks
  • Brand names
  • Customer contacts
  • Process knowhow, ‘best practice’ knowhow, learning curve experience i.e. knowing not only what works but what does not
  • People skills and experience

Corporate knowledge (K) is thus said to be the sum of everything everybody in a company knows that gives it a competitive edge (aka a large moat) – and all held either ‘in files’ or ‘in heads’

So what’s K for?

It enables organisations:

  • To invent – to add to or replace existing offerings
  • To innovate – to improve existing offerings
  • To control operations and minimise unit costs
  • To increase output volume, quality and/ or service levels

Hence it’s crazy not to be able to manage and control such a powerful and valuable input resource

To do this, managers first need some indicators which show their teams’ actual K position – at present, most have little idea of where they’re falling short, by how much or the potential they’re ignoring

The least they need are subjective assessments of the availability, utilisation and efficiency of use of the K within their walls viz:

  • KA% = K Availability % = % Actual K available/ Total K needed:
    • ‘In heads’ = % assessment of the actual skills, qualifications and experience within a team versus that needed
    • ‘In files’ = % assessment of the actual important K recorded rather than being resident solely in heads and thus liable to be lost if those heads just ‘walk out the door’ – BP (Best Practice) databases are rarely maintained yet are vital in, for example, the public sector
    • ‘Accessibility’ = An assessment of the % of K resident in heads or files which is readily accessible by others in the team
  • KU% = K Utilisation % = % K used by others/ Actual K available
    • % of K in team heads used by others – team members can be unaware of what others in-house know – they need to know who are champions in specific areas who can be spoken to – and there are few mechanisms to encourage team members to talk to each other and exchange K – some hoard specific K believing ‘knowledge is power’ but organisations need to tear down such K silos
    • % of K in team files used by others – at present, wheels keep being re-invented rather than improved upon
    • % of K outside the team but within corporate walls, whether held in heads or files, used by the team – this requires some form of taxonomy, an information classification and tagging system, to make K easily findable and facilitate sharing
  • KE% = K Efficiency % = % Operational efficiency/ Maximum 100
    • aka Kleverage = How effectively K is used
    • An overall % rating assessment of current products/ services, market share, customer satisfaction levels and key processes reflecting their scope for improvement


Such subjective measurements, if agreed by a group of internal managers rather than just the manager concerned, would not need to be deadly accurate – you don’t need to know someone’s precise weight to know if they’re fat or not

However, the K review process of peer-discussions first followed by peer-agreed results would force any manager to consider her team’s actual K position more than ever before – and thus be more likely to identify where and when big changes were needed to benefit both the team and the organisation

For example, if a team’s results were KAUE% = 80% x 50% x 70% = 28% (an indication of the waste of K ongoing) then clearly ‘something must be done’ – probably by first addressing KU% and the need to share in-house knowledge better


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