Data-Informed vs Data-Driven

By | AgileBusiness, Customer Exprience, Digital Disruption | No Comments

We hear a lot these days about the need to be more ‘data-driven’. And I’ve used this phrase a lot in the past to describe the heightened imperative in just about every business to put data more front-and-centre in strategies, processes and decision-making. But more recently I’ve started to use the term ‘data-informed’ instead of ‘data-driven’ because there is a meaningful difference between the two phrases and what they imply.

The latter is far more prevalent (it has 62m results on Google compared to only 7m for ‘data-driven’ – see what I did there), but there is a key distinction between them and good reason to change our terminology, expressed well here by Andrew Chen.

Metrics, says Andrew, are of-course typically based on existing or past circumstances (a reflection of a current product status or strategy, for example, and an existing audience) so they may help you to iterate towards what he calls the ‘local maximum’ (the point at which you hit the limit of the current structural, customer or design foundation) but it’s easy to take this too far.

Evidence based decision-making is great, and still under-utilised in most businesses, but there is a limit to the gains that we can derive from optimisation, and it de-prioritises the bigger picture – the broader aspects of a problem or the potential bigger solution. Data is often systematically biased and the kind of data that is most easy to collect and interrogate is often not the kind that will show us what’s possible or where our end vision could be.

Being data-informed means setting a course (creative, visionary, compelling) and then recognising that you are likely to have only a subset of the data you need at any given time to be ultimately successful. So it is important to break the work down into smaller pieces and use data to help validate and navigate towards the realisation of that end goal.

Like the visual at the top of this post suggests, data may be the new oil (or even the new coal in the sense that, like the early days of the steam engine, innovations are far more useful to those who have copious amounts of the raw material to work from) but as Andrew says, not everything is an optimisation problem and being data-driven rather than data-informed means that we are in danger of over-simplification.

As I say in the book, we need to design with vision and optimise with feedback.

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What is Digital Culture?

By | AgileBusiness, Culture | No Comments

We should know by now the importance of organisational culture in supporting digital transformation and change (it’s the people, stupid!) but what exactly do we mean by digital culture? This new McKinsey research drawing on a global survey of senior executives amply demonstrates how powerful cultural and behavioural challenges can be in blocking digital progress.

Digital-culture

Culture and behaviour are seen as greater potential barriers than knowledge and understanding, talent, structures,funding and even technology infrastructure. Selecting adjectives to describe the key characteristics of digital culture is arguably the easy part but since culture and behaviour so fundamentally inform, shape, and influence working practices, strategies, orientation, actions, values, it’s worth touching on some of these attributes to better explain what I mean. So for what it’s worth here’s my list for what digital culture really means:

Agile and Responsive:- in the book we describe how organisational agility is about more than just speed, it’s about manoeuvrability and responsiveness. This means an orientation towards greater experimentation, test and learn, a boldness and a less risk averse culture, the ability to move quickly when necessary.

Customer-centric:- customer-centricity is as wide as it is deep, and should be reflected in strategies, processes, and structures but more than anything it should be embedded in the culture. It shapes outlook and informs every decision. We talk about fast-feedback loops and data-driven decision-making but it’s better IMHO to be data-informed than it is to be data-driven – the latter may be good for incremental and continuous improvement but may also lack vision, empathy and intuition.  The former allows space to create the new, and describes a more useful balance between vision/creativity and feedback/optimisation. Data is critical but we should not be slaves to it.

Commercially focused:- digital culture is results oriented, quick to explore, determine and assess opportunity, ready to disengage from existing advantage

Visionary:- characterised by a compelling common purpose that is well understood

Technology-literate:- a culture that is founded on comprehensive technology-literacy whilst supporting an optimal balance of generalist and specialist expertise, technology as enabler, greater trust and flexibility in technology (less lock-down)

Flexible and adaptive:- a willingness to change and flex, the kind of adaptability that builds resilience and momentum (antifragile), the environment to support greater fluidity, getting the balance right between vision and iteration (as Jeff Bezos says we should be ‘stubborn on vision, flexible on details’). Avoiding managing by proxies (as Jeff Bezos also says , e.g. process as proxy, instead of genuinely looking at customer-focused outcomes just making sure that a process is followed), greater autonomy and ownership, less rigid hierarchy

Networked:- flow of fresh perspectives into the organisation, flow of data through APIs, openness to utilise external resources and build off external capabilities, willingness and ability to capitalise on platform business economics, (Amazon, for example, systematically platformising individual component parts of its business in order to gain greater efficiences and leverage)

Exploring and curious:- digital culture is externally-facing, inquisitive, lateral-thinking, quick to explore technology and customer behaviour trends

Entrepreneurial and innovative:- bias to action, restless, continuous and systematic rather than episodic innovation

Open and transparent:- a working environment characterised by high levels of trust, growth mindset, productive informality, psychological safety and openness

Collaboration and learning:-  a culture that supports knowledge flow, continuous learning and ease of multidisciplinary collaboration (digital and customer experience are horizontal, cutting right across departmental siloes), embedded reflection and retrospective, learning from successes and failure

The McKinsey research goes further than simply demonstrating how significant a potential barrier culture and behaviour can be to digital progress. Cultural factors such as risk aversion, siloed mindsets and behaviours correlate clearly to economic performance:

The McKinsey piece also makes the point that waiting for culture to change organically is simply too slow, and yet that’s what many senior leadership teams seem to do. A key place to start is to understand and map the current culture and then to and then to actively challenge, promote, reward, demonstrate and recognise the attributes that can support

This needs to happen at the most fundamental level – culture is more than posters with slogans, words on walls, and coloured beanbags (visible artefacts and behaviours), and it’s more than written values statements, strategy documents and codes of conduct (espoused values). What truly shapes culture are the basic assumptions – the underlying, often invisible assumptions and practices that really influence how stuff gets done.

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Problem Market Fit comes before Product Market Fit

By | AgileBusiness, Digital Disruption, Digital Economy, Digital Marketing, Disruptive Innovation, Strategy | No Comments

problem_market-fit

Problem market fits needs to come before product market fit.

The best piece of advice I ever got was from our first investor, Paul Graham.
He said it’s better to have 100 people love you than a million people that sort of like you, so if you can find 100 people that love your product – as long as there are more people like them in the world – then you have an idea that I believe will spread around the world. But if you can’t get 100 people who absolutely love your product, then you do have a problem.

Brian Chesky – Airbnb

I came across this short post this morning and it’s worth a read.

I believe what it’s saying applies as much to scaled businesses or large corporations with a business model, as much as it does to start-ups and even scale-ups, because almost all businesses now will be running some innovation project or product development programme.

Sometimes we just push-on. Get shit done. Believe we’re providing something that puts the customer at the centre and changes the world (ok maybe not so much, but we believe in what we’re pushing).

So if there were 3 important questions you should ask yourself if you are creating or launching something new…

1. Do customers (still) consider it one of the top 3 problems they have?
This means they’ll spend time and money with you in order to solve it.

2. Would customers immediately adopt a solution if you provided it to them right now?
This translates to them actually paying for it. As Airbnb’s Brian Chesky said above, find 100 people that love your product, but if you can’t get 100 people who absolutely love your product, then you do have a problem.

3. If you solve the problem, do you have ways to distribute your solution to the customers?
This is about potential to scale.

I think there’s one more we need to add.

4. Do you ‘really’ know your audience and where they reside?
This is about marketing and reach, brand through to sales, and it amazes me how many times this gets forgotten or the understanding of actually how difficult this is becoming due to fragmentation.

In the book, Building the Agile Business, we talk about related topics like the Organizing idea, Agile strategy and Discovery driven planning.

 

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Main Image source: Jay Mantri

On Lifelong Learning and Failure

By | AgileBusiness, Culture, Leadership, Teams | No Comments

Learning

‘To keep up with developments in their field, people must become lifelong learners, and success will belong to those who can master new skills and envision novel possibilities. Employees must absorb, and sometimes create, new knowledge while executing. Because this process typically happens among individuals working together, collective learning—that is, learning in and by smaller groups—is regarded as the primary vehicle for organizational learning.’ Amy Edmondson

Whilst on holiday I re-read Amy Edmondson’s Teaming (‘How Organizations Learn, Innovate, and Compete in the Knowledge Economy’) which I reference in a few places in my own book. Amy’s book is a rich source of insight on teaming dynamics and organisational learning. She adeptly makes the case for how teams are an organisation’s best change agents but also differentiates nicely between the more traditional orientation in business towards organising to execute, and the need to increasingly orientate towards organising to learn. The team is the unit of delivery, but it’s also the key to learning and transformation. We learn better when we learn together.

Organising to execute, she says, is about a focus on ensuring control, efficiency, managing repeatable tasks, minimising variance and rewarding conformity. Qualities that work well in stable environments with well understood contexts that change slowly but ones that work less well in rapidly changing circumstances characterised by greater uncertainty. Qualities that can bring operational cost savings and improved productivity and process efficiency but which can also act as a brake on cross-discipline collaboration and learning, and even be characterised by a fundamental distrust of the worker. Qualities that can lead to failure in complex adaptive environments.

The difference between this and organising to learn is reflected in the broadest possible set of approaches, functions and processes right across the organisation. From the type of people we look for (experimenters and problem solvers rather than conformers and rule followers), to how we train them (learning by doing rather than always learning before doing), to how we combine knowledge (integrated, not separate or siloed knowledge), empower staff (to experiment and improvise), treat variance (use it to improve rather than drive it out), and measure performance (more ‘what did we learn?’, less ‘was it done in the right way?’).

The point is that lifelong learning is something that we should pursue not only as individuals, but is also something which is critical at a team and an organisational level. We hear a lot these days about ‘failing fast’, ‘fail happy’, ’embracing failure’. This is not that helpful. Failure in isolation is a redundant standard. Far better to talk about learning, and how we can support continuous improvement from understanding, reflecting on and responding to both successes and failures. As Amy says:

‘When facing an uncertain path forward, trying something that fails, then figuring out what works instead, is the very essence of good performance. Great performance, however, is trying something that fails, figuring out what works instead, and telling your colleagues all about it—about both the success and the failure.’

Amen to that.

Image source