The Self-aware Self

A key part of the value of the quantified self is awareness. We know how to coach ourself when we give ourselves enough purposeful attention.

I’ve been travelling for work recently. Being away from home has put out my usual diet. I just didn’t have my usual options or as much ability to cook. Feeling the effects, I decided recently to start tracking my meals using the Fitbit app. I wanted to know what I was eating.

There were three quick lessons from this experience:
– the Hawthorne effect works: just being aware I was recording my meals ( with no cheating) helped me make better choices.
– after a few days I realised I didn’t need to know the data. If I concentrated on what my body was telling me, I knew whether I was hungry, when I had enough and what I shouldn’t eat. If I listened closely to those messages I could make better decisions without data.
– I enjoyed eating more, because I noticed what I was eating.

A big part of the value of the quantified self is helping us become more self-aware. We all benefit when we step out of busy distracted mode. There can be great value in novel insights from data. Usually, our problems are much simpler. We don’t need machines to tell us things we know but don’t do. We need to learn how to be more present and how to better coach ourselves.

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.

Actionable insight matters more than big data.

Don’t worry how big your data is. Focus on how actionable your insights are.

The only thing that delivers business value is turning insights into effective action. Big data can deliver new insights but they will only drive your business when they are put into action to create new sales, save money or create other ways delivering better value in line with your strategy.

Many companies forget to leverage the insights in their existing customer systems. Do your people remember to make a birthday call to a key client using data in your customer relationship management system? Do referrals, leads & other opportunities identified always get executed effectively? Are anniversaries, expiry dates and other retention triggers well managed? Before you launch into new insights make sure you have captured the low hanging fruit.

Big data is often celebrated with examples of counterintuitive insights. Counterintuitive insights are hard to predict and equally hard to action. People doubt the strategies that come from black boxes. Doubt is not a great enabler of action. Organizations often lack the capability to execute the counterintuitive strategy. For example, knowing that left handed plumbers are more likely to watch opera is not much use unless your opera company has a hardware partnership.

Big data is often sold as a source of new strategy. It is rare that a company changes strategy on one insight. Usually, insights enable you to better execute your current strategy. These insights will confirm the hypotheses you used to create the strategy and translate general plans into the right actions with specific customers. Start your focus on better insights with what you need to do to drive your current strategy and leverage your existing capabilities.

Before you boil the ocean in a battle of data completeness, decide what you need to know and can use to create value. Invest in the capabilities to better action insights. You might be surprised by the insights you already have that are opportunities. Focus your insights on driving your business, not the size of your data bill.