The Problem is Everywhere

The peculiar character of the problem of a rational economic order is determined precisely by the fact that the knowledge of the circumstances of which we must make use never exists in concentrated or integrated form but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess. The economic problem of society is thus not merely a problem of how to allocate “given” resources—if “given” is taken to mean given to a single mind which deliberately solves the problem set by these “data.” It is rather a problem of how to secure the best use of resources known to any of the members of society, for ends whose relative importance only these individuals know. Or, to put it briefly, it is a problem of the utilization of knowledge which is not given to anyone in its totality. – Friedrich A Hayek “The Use of Knowledge in Society”

Yesterday I met with an organisation that wanted some of my help as they sought to solve a problem. The organisation was developing a new knowledge sharing system to enable is staff to be better informed about products and processes. There was one slight issue with this problem. The organisation already had multiple systems to enable its staff to be better informed about products and processes: intranets, social networks, training, help & support tools, automation, etc.

Problems Everywhere

As we asked why these other systems didn’t work it became clearer that the project team’s issue was that it was solving a problem for others, rather than with others. The explanations for needing a new system did’t stack up and suggested there was more that needed to be learned from the users:

  • ‘Most of the learning is peer to peer. We need to give them better options’: Why do they prefer to learn from peers who might be inaccurate or unavailable? Why will they change this if you offer a new system? 
  • ‘They won’t use a collaboration system because they say they don’t have the time’ : if time is a question of priority, why isn’t it a priority? To what extent is the culture, leadership and performance management of the team driving this lack of priority? If they won’t collaborate why will they have the time to use something else? What is there time actually spent on? What do they do instead?
  • ‘Those system don’t give them the answers they need so we are building a new one’: If the last system didn’t understand what was required, how do you? What does relevance look like to each user? What does relevance look like to their customers?
  • ‘They want help with process X, but we are building something innovative for all processes’: Why do they want help with that process? What’s innovative about ignoring the demand?

The Answer is Everywhere

The answers to these questions are dispersed in a wide range of people beyond the project team. They draw in questions of culture, of practice, or rational and irrational behaviour by real human beings doing real work under the daily pressures of customers and a large organisation. There’s a lot of learning to do.

We have the tools to solve this dispersion and gather insights into what needs to be done in the practices of Big Learning:

  • we can actively collaborate with the users and other participants in the system to get under the pat answers and explore the deeper reasons and problems
  • we can use the practices of design thinking to better understand and shape employee behaviour & the systems involved in action
  • we can analyse data to understand in greater detail what is going on
  • we can experiment and iterate to ensure that proposed changes work the way that we expect
  • we can enable and empower the users to create changes to their work
  • we can accelerate the interactions and the cycles of learning to move faster to better solutions

These aren’t parallel techniques to be applied independently. The practices of Big Learning work best as an integrated system that draws together the insights from all of these approaches to help organisations learn and work. Big Learning enables organisation to work with and through its employees to deliver change. Change does not have to be done to them.

The reason organisations need to develop systems to facilitate Big Learning is elegantly described by Hayek in the conclusion to his essay “The Use of Knowledge in Society”.  Hayek was critiquing the schools of economists who thought that centrally planned interventions designed by experts would be effective. The context may differ but organisations still use forms of central planning by experts to create change. These changes fall short for a fundamental reason – experts can’t know enough alone:

The practical problem, however, arises precisely because these facts are never so given to a single mind, and because, in consequence, it is necessary that in the solution of the problem knowledge should be used that is dispersed among many people

The practices of Big Learning help bring people together to share insights, learn and work as one.

Confusion is the absence of Design

Yesterday I had to deal with an unnecessarily confusing customer experience.  All I wanted to do was pay for my parking.  It was a great reminder that in the absence of design you generate confusion.

Here are some observations on what happens when you forget to design:

  • The insides of this parking machine would fit in a shoebox, but it’s a big machine.  That means that it is actually very hard to keep all the machine in sight at one time. When the interface is confusing, having to scan the whole thing repeatedly to find your next step is hard work.
  • The screen draws your attention but it is not where the action happens. In fact the screen, tells you little of interest and mostly distracts from where the action happens.
  • Every function has a light or a sign which adds to the confusion. The signs look like later additions to improve the usability but the signage is neither consistent nor supports the process the machine requires users to follow.  The range of different coloured lights is distracting.
  • The blue P lit up is prominent, but purely decorative. 
  • The red laser light top left is for museum membership card discounts, a second process step for a small proportion of users, but it is by far the brightest light.
  • The slot for inserting a ticket to pay, the first process step, is a solid yellow light at bottom left. This is the last place anyone looks, especially when the screen shows the slot and you assume that the image shown must be near the screen.
  • The screen is below normal eye height. As there is no shade on the screen, the lights above make it unreadable unless you crouch.  This matters if you want to know what you need to pay or want a receipt and need to push a button below the screen to confirm your request.
  • The paypass reader doesn’t work though it appears to all intents that it does with a shining light. After several failed attempts, I realised that I needed to insert my card.

The odd functional arrangement and the lights create a sense that four separate divisions of Skidata all said ‘we want a bright flashing light and a sign. We want to be prominent’. Politics and engineering determined where the various bits went on the machine rather than any designed order of a customer experience.

For a simple process this is an unnecessarily confusing customer experience. That says to me Skidata and those who installed the machine weren’t designing a customer experience, they just installed a parking payment machine.