One loser in the UK election may be 'learning from failure'

Like me, if you are in the UK, you are probably thoroughly fed up with the General Election hype and puff. But bear with me on this one...

Frances Maude will be a loss when he steps down as Cabinet Office Minister. He appears to be a rare beast, a dedicated public servant who is not afraid of upsetting people to get his job done, whether you be a senior Civil Servant or MP of any party. He was the guy charged with saving £4bn per year from government spending by 2020; he is widely acknowledged to be on track to do just that.

Of greater loss will be his refreshing approach to 'learning from failure'. My goodness there has been ample opportunity to so that. Maude talked to the Sunday Times about a new civil service prize he introduced in 2014...

"I wanted to call it the Frances Maude Award for Failure. The criteria were that you had done something that had failed but you had learnt from your mistakes. In the end we had to call it the 'Innovation Award' and 70 of the 80 nominations were for things that had worked. No one felt that they could say that they had failed, but one should not have to be brave or fearless to speak out... If you are honest, voluntarily, about when things haven't done well, then you build up some trust".

That is great leadership and a good start to organisations, particularly big ones, acknowledging that when you innovate, some degree of failure is inevitable. As Einstein said 'anyone who has never failed has never tried anything new'.

Maude's leadership was well intentioned, but not enough. A client I worked with instituted high profile 'Failure Fairs'. That's a bit like inviting someone to voluntarily put their head in village green stocks. People need to feel safe that by sharing their, perhaps painful, experience they will not be humiliated or punished.

The other thing that is needed is that the learning is not just that "you had learned from your mistakes".  If you make a mess-up, it hurts and it's unlikely that you will repeat it. What's needed is for Mr Maude's successor to figure out how to differentiate between lessons identified and lessons learned. In other words, what has systematically changed as a result of that learning being applied to business processes or rules.

Roll-on May 7th.

Disclosure: I was the private sector advisor to a UK Cabinet Office review of 'Major Government Project Failures' carried out in 2000. Lessons learned anyone?

(Photo credit: Wikipedia)

Evidence-based decision making

We all have biases. It's perfectly natural for the decisions we make in life and work to be influenced by knowledge gained through prior experience and intuition. Most of the time this serves us well. However we would not expect major business decisions to be made just on on 'gut-feel'.

Using an open and transparent evidence base for decision making is now common practice in the Health and Third sectors and increasingly in Government policy making. Retail has been driving value from its customer data for years. Quality data, available in real-time is becoming an increasingly important resource for every organisation. Sources and quantity of data are proliferating and becoming almost cost-free; witness the 'internet of things' and open government data. Amazon and Facebook were built ground-up on 'big data' *.

However the analysis and facilitation skills needed are not keeping up with the huge leaps in data technology. By that I don't mean data scientists' skills, but managers' ability and confidence to make business decisions predicated on data analytics. A recent McKinsey insight highlighted the need for organisations to adapt if they are to get big results from big data. The example they give is that of an organisation geared around weekly or daily product price adjustments. Unless they adapt their business processes to leverage real-time competitor pricing and market conditions, they won't get the benefit from that data.

I am currently planning the Knowledge and Innovation Network members' Autumn quarterly workshop 'Evidence-based Decision Making'. This event is not for 'techies' or data-geeks; we will be looking at the organisational capability, processes and skills needed by KIN member organisations to improve rapid decision making. We have a great line-up of thought-leaders, case studies from members and experiential learning activities proposed. The KIN calendar will be updated as they are confirmed.

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* I'd heard the phrase 'big data' many times, but admit I didn't really know what it meant. Margaret Rouse has a useful explanation in her graphic; it is about exponential volume (Mb to PetaBytes), velocity (from batch processing to real-time) and variety (from spreadsheets to unstructured social data).

Supersonic collaboration

Bloodhounds aren't renowned for being fast. This remarkable Bloodhound will cover a standing mile in 3.5 seconds and break the 1000mph land speed record. When reading this remarkable story I was struck that it was a tale of near-perfect knowledge sharing and collaboration, as well as an engineering marvel. Listen to the audio clip of Yan Tiefenbrun, the MD of Castle talking about how he assembled the finest expertise from surprisingly diverse industries to develop completely new insights. They are not only building the fastest wheels, but a model for open-source knowledge sharing. If you face a daunting innovation challenge or knowledge-sharing problem, be inspired by this British cross-industry project.

Two for one on learning

Firstly, for those with an interest in how to effectively communicate explicit knowledge in organisations, this free MOOC (Massively Open Online Course) on 'Content Strategy for Professionals' from Northwestern University will be useful. It's a 6 week course, involving 2-4 hours work per week. I've done 2 other MOOCs and both were excellent, so I'm looking forward to this one. 

I've blogged about MOOCs in the past; they are a wonderful resource in democratising learning and knowledge.

The other item that caught my eye was a brilliant blog posting by Harold Jarche.  The title 'Adapting to perpetual beta' does not do justice to the concepts about the networked organisation that Harold discusses. His insights about the difference between Complexity and Complication and their relationship to Collaboration and Cooperation are really interesting. For example,

Collaboration & Cooperation

"Complex problems require cooperation while complicated projects need collaboration. Collaboration is working together on a common problem, while cooperation is freely sharing without any objective. Cooperation is not team work. It is helping the entire organization, as one would support a natural commons, and this requires people who are not just doing their job, but involved in the whole system. This is a major change in how business functions have been managed. Knowing what is complicated, and what is complex, can help the organization develop the appropriate work practices. Less structure and more flexibility is required for complex problems".

Metrics, schmetrics

Has anyone found a small, shiny cylinder with a pointed end? That would be the silver bullet that many are looking for to measure return on investment for intangibles, such as knowledge or learning initiatives. KIN has run a number of workshops on this topic in the past and have concluded that this is the wrong question. Return on impact is more achievable and may provide meaningful metrics.

This is the topic of a new blog post from Steve Dale.
To paraphrase Steve,

Not all changes can be measured in strictly cash value terms, which is what many people consider to be the true meaning of ROI. How do you measure the value of a conversation or some information shared?  The answer is, you don’t... measuring impact can be just as important as measuring value.  The impact might be things like improved customer satisfaction (measured using surveys), or less time to complete a task, or improved staff morale (measured using surveys). Any of these can – and potentially will – have an effect in terms of cash value to the organisation, but I firmly believe that converting impact to cash value is an exercise in futility, since more often than not, there are too many variables. 

I agree that ‘converting impact to cash value is an exercise in futility’. Too many times I have seen organisations attempt to attribute cost savings or efficiency gains to organisational learning initiatives. This is made worse when cash ‘benefit’ is scaled-up across the whole organisation. This invites a coach-and-horses to be driven through the unsupportable multi-variable extrapolation. There is one exception to this; where a repeatable process (think of well-drilling or manufacturing process) is improved through a statistically significant trial. Of course any metric must be made against a benchmark; an oft neglected imperative.

I recommend consideration of 3 categories of measurement:
1. Transactional (documents uploaded, questions posted, time of response, number of online ‘lurkers’, etc). This is the least valuable, however if you don’t have this stuff, someone senior will ask.
2. Impact (decisions made faster, mistakes avoided, connections made, effectiveness of communities of practice etc). Impact measures are often best supported by vignettes or examples.
3. Perception (improved collaboration/cooperation {different things}, goodwill for problem solving)
As Steve suggests, the latter two can be measured using survey results to turn qualitative views into qualitative metrics; a legitimate way, and perhaps the only way, of doing this.

You may notice that I have used the terms metric and measurement. At the risk of being called a pedant, it is worth considering the difference between measurement and metric.

Bill Ravensberg provides a succinct and useful definition in this LinkedIn discussion:

Measurement – A MEASURE (rating, sizing, etc.) of one thing. Examples are cost, function points, effort, time, etc.

Metric – A RESULT of taking two or more measurements to create a value. Using the measurement examples, you can get cost per function point, function points per unit of time, etc.

Basically, the measurement is something you need to create a metric that can then be used for reporting and analysing. A measurement generally will not provide much value or meaning until it is combined with other measurements. The metrics provide the value.