The Whack a Mole Model of Innovation and the Innovation Cubbyhole

 

Children have a game called Whack A Mole. In this game, a random mole comes out of a random hole and you have to push it back in.

 

Most organisations do the same to their internal innovators.

 

Playing Whack a Mole with your innovators

When an employee gets an idea, they have to take it to their manager for approval. Usually, the manager stands to gain little if the idea works, and lose much if the idea fails. Or perhaps,  its simple fear of change and they discard the idea (or promise to work on it but never get around to that).

 

Then, a few days later, another employee in another department comes up with an idea. Again, it is taken to the manager, and then his manager, until, at some level, it is properly evaluated and put down. Nothing personal, of course.  

 

And that is how organisations play whack a mole with their innovators. What happens is that the organisation ends up not seeing any noticeable innovation for years. And we all know the result of that.

 

There are 2 ways to stop playing Whack a Mole.

The first, and the more difficult, is to create a culture of innovation. That will take time.

 

So you can take a short cut. The beauty of this short cut is that its proven, and it will help  you get to a culture of innovation too.

 

The Innovation Cubbyhole

There is nothing new about the idea. The only point of this post is that you can use the Innovation Cubbyhole to circumvent the Whack a Mole culture while you are fighting it.

 

1. All employees can take a certain percetage of their time in the Innovation cubbyhole. No questions asked.

 

2. All employees have access to some company resources. Whatever is available in the cubbyhole is yours to use. If you need anything extra, read Rule 3.

 

3. All employees have a limited, preapproved budget in the cubbyhole. Once you sign into the cubbyhole, you get pocket moneys (aka the preapproved budget) to spend on your project. Its not a lot, but its theirs.

 

4. If a project fails, all you have to submit is the failed equipment, and a detailed lessons learnt document, incl. what you did, how you think it can be done better etc. Its all squared. No blame game, no questions asked.

 

5. Got a bright idea but lost interest / got transferred / dont have time? no problem. Sign into the cubbyhole, put the idea up, and leave. Someone can come to the “Wall of Abandoned Babies” and pick up an idea. Just like that. Leave all literature of work done so far in the Cubbyhole, neatly archived and all that, so that your colleagues can take it up from where you left.

 

6. Once you are ready, make a more formal presentation to the people who might benefit from the project. Explain and sell the idea to them. If they agree, it will be taken for mainstream adoption.

 

7. If you save your pocket money, you can either return it, or carry it forward to another, bigger project of yours. In short, you are rewarded for being frugal in your success.

 

The advantage of this approach is that small innovators can work to create a pilot proof of concept. it is much easier to convince people with a working model in hand.

 

The second, and the bigger advantage is that, if no approvals are necessary, it becomes a little more difficult to whack the mole. The “approval” is the hammer in the hands of the player. You cant play whack a mole without the hammer.

 

There is one key requirement, however. One must hire an absolutely insane person to head the cubby hole. Someone who truly believes that anything can be done. ANYTHING. And the person doing the hiring should have the same insane belief.

 

Endnote:

Look around you. When was the last time you saw a real employee led innovation in your organisation? Was it in the last 12 months?

Lifecycle of the Innovating Organisation

 

We have all heard that story before – a brilliant idea, led to the creation and growth of a market leader. Then, something went wrong, and within a century, or less, the pathbreaking organisation was on its way out.

Usually, the meteor that hits the organisation is , ironically, flab and lack of innovation.

 

We all know, also, about the innovation life cycle.

 

Image Courtesy: http://www.jplcreative.com/blog/index.php/2010/01/26/marketing-strategies-for-innovation/

 

But here, I am talking about the Lifecycle of the Innovative Organisation.

 

An innovative organisation is an organisation that continues to innovate – not for business success, not to get more territory, not to do things better, but just as a matter of course. As a way of BEING. Its in their DNA. All of the business benefits – quality improvement, newer products, better markets, are side effects of that way of BEING. As are product failures, millions of lost dollars, some lawsuits et al.  

 

This distinction is important – In an innovating organisation, innovation is not a means to an end – nor an end in itself. It is, quite simply, be who they are.

 

3M is the obvious example that pops up when we talk about innovative organisations. There are, of course, others. General Electric. I will not put Apple in the list. Because honestly, we have no idea what the culture is INSIDE Apple.

 

What makes these organisations Innovative Organisations? What can other organisations do to remain (or become) innovative?

 

This article here has some very sound models for organisations asking “What can we do to become innovative?” (Blackberry and Nokia come to mind) . My post is about what MAKES these organisations innovative? What keeps innovation in their DNA?

 

And the hypothesis is this: Leadership does. 

 

Impact of Leadership on Innovation Culture in an organisation

 

The previous post spoke about the hypothesis that the key thing that makes innovating organisations become what they are , is leadership.

 

While practices and organsiational models et al are positive contributors to the innovation process, if an organisation has to stay innovative – complete with the side effects of that state of mind, the most important factor is that the leadership must continue to believe – generation after generation, in innovation.

 

Here is how this works –

Organisational behavior is shaped, at the primary level, by the rewards system. When a type of behavior is rewarded, we start to value that behavior more, because it leads to a positive outcome. this relationship is a function of the thinking mind – i do this, i am appreciated/ rewarded – the cognitive level.

 

Over a period, the repetition of this reward system leads to us developing a positive attitude towards that behavior. We automatically associate that behavior with positive response. – the conative response.

 

Over time we internalise the positive attitude and start to respond with positive emotions to the behavior itself – at this level, the affective level , the behavior is internalised. Then we become “innovators” simply because of who we have become. Its the same process that works when we enter a university a fresher and exit as an alumnus. Our mental associations change to be congruent with the associations of the university.

 

This is how the innovative culture is built – but by bit – moving from the behavior-reward (cognitive) to conative to affective (internalised and emotional)

 

So, to sum up, the foundation of culture change is in the rewards and punishment factors – the rewards may be tangible and institutionalised, or it may be intangible and simple like positive feedback or even just permission to work on research during office time.

 

And who decides, in the most significant way, what this reward structure in the organisation will be? The leader. Even if the tangible organisational models and employee appreciation criteria are not immediately touched, the intangibles like positive feedback trickle down really fast and without doing anything documented or formal, leaders are able to kill the innovation culture relatively fast.

The Data Journey Diagram/Data River Diagram

Today, I am going to talk about a tool that I have just created. The tool is here for discussion, but NOT for re-use or re-distribution. Please use your conscience and do not re-use this tool without my permission, (which can be had for the asking).

The tool is called the Data Journey Diagram.

What

Graphical illustration to help users understand the usage of data generated by them, and to help BI designers see the most expected fallacies in their data.

For

Primarily, the tool is for the end users who generate data and the Analytics consultants who design the analytics solution for an organisation. The other stakeholders are the IT department, legal, security and line businesses.

How

The tool is very simple. We put the end users, the MIS team, and the data analytics team in one room.

Then we create a meandering river shape on the board. The Analytics Consultant puts a simple report at the end of the line. Then, he puts in all the data elements that go into making that report.

Slowly, each stakeholder who owns each data point, writes down where each data element comes from – all the processing points, and all the hands that the data element passes through. The river is used as a rough measure of time flow.

For each data element that comes from a different source, at the point of convergence, we draw a separate line to indicate a contributing tributary. Then the journey carries on backwards until we have a river system in place – indicating all the places where the data comes from, how it all comes together et al.

 

Ground Rules

At each stage, ONLY the stakeholder who owns the data at that point, will be allowed to create a “milestone rock” and name it. No one else.

 

Limitations

This tool does NOT:

A. Indicate dependencies.

B. Indicate And/or relationships

C. Indicate mathematical or logical operations. We know that 2 data elements come together, but we do not know if they use averages or totals.

 

What is achieved

At the end of this exercise, end users are able to appreciate, in very simple terms, how their data errors impact decision making at the CEO level.

The Analysts are able to see sources of the data being used in the high end analytics reports, and can talk to the stakeholders right there to find out if there are any anticipated data errors – time lag, fallacy, human error etc. These are VERY important inputs for the analytics design team, because GIGO is as true today as it was when computers were invented. 

So, what are your thoughts?