Why We Reject New Technology

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Thermometer

There’s a great account in the Scientific American focusing on why new technologies that can make our jobs easier are somehow often rejected, using the adoption of the thermometer as an exemplar.

At the end of the sixteenth century Galileo Galilei invented the first device that could measure temperature variations – a rudimentary water thermometer. Around 120 years later Gabriel Fahrenheit came up with the first modern mercury thermometer. The Dutch physician Herman Boerhaave thought that the device had great potential and proposed that measurements using a thermometer could be used for diagnosis and to improve treatment.

Yet despite its evident utility it took over hundred years for use of the thermometer and the discipline of thermometry to become widespread. Prior to the mercury thermometer, Doctors would largely use touch to determine whether the patient had a high temperature or was suffering from a fever. This qualitative approach was regarded as being able to capture a rich amount of information, more in depth than any tool could generate, and for many years was seen as a superior approach to using thermometry.

In spite of the prevailing inertia to adopting this new technology, a group of researchers persisted in attempting to turn the relatively idiosyncratic opinions and descriptions from Doctors into reproducible laws but it was not until 1851 when a breakthrough happened. In a transformation piece of work (published as “On the Temperature in Diseases: a manual of medical thermometry”) Carl Reinhold Wunderlich recorded temperatures in 100,000 patient cases, and successfully established not only that the average human body temperature was 37 degrees, but also that a variation of one degree above this constituted a fever, which meant that the course of illness could be better predicted than by touch alone.

Thermometry represented a giant leap towards modern medical practice. Patient expectation changed and by the 1880s it was considered medical incompetence not to use a thermometer. But why did it take so long to become widely adopted practice? The original thermometers were large, cumbersome devices and the tool developed over many iterations but this still doesn’t explain its slow advance.

The Scientific American article notes how easy it is to reject technology that we don’t understand, or technology whose successes we’ve had nothing to do with. Perhaps our fear is that in its success, new technology will detract from our own utility. More likely, slow adoption of technology comes down to what Andy Grove (of Intel) used to call the ’10x’ rule, referring to the idea that a product must be at least ten times better in order to overcome barriers to adoption and switching costs because people tend to underestimate the advantages of a new technology by a factor of 3 while simultaneously overestimating the disadvantages of giving up old technology by a factor of 3.

But as the piece also goes on to point out, the subtlety is actually in how we combine the best of the old with the best of the new – describing how a children’s hospital in Philadelphia had used quantitative algorithms to identify particularly dangerous fevers. The algorithms proved better at picking out serious infections than the judgement of an experienced doctor. But when the two were combined it outperformed either in isolation:

‘It’s true that a doctor’s eyes and hands are slower, less precise, and more biased than modern machines and algorithms. But these technologies can count only what they have been programmed to count: human perception is not so constrained.’

Similarly, at the 2016 International Symposium of Biomedical Imaging in Prague, a Harvard team developed an AI that could detect cancer cells amongst breast tissue cells with 92 percent accuracy, almost as good as the trained pathologists who could pick out 96 percent of the biopsy samples with cancer cells. Yet when artificial and human intelligence were combined 99.5 percent of cancerous biopsies were identified.

Technological change rarely means forgetting all that we know. More often it is helpful to frame it in that thought of combining the best of the old with the best of the new. Perhaps the key lesson here is that a fixed mindset (one where you believe that success happens at the expense of someone or something else) does not help the adoption of new technologies. When we can see the bigger picture, and adapt existing knowledge and skills to combine the best of the old with the best of the new, we always progress more. Growth mindsets win.

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Fixed and Growth Leadership

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Stanford Psychologist Carol Dweck’s concept of fixed and growth mindsets is just a brilliant way of expressing some of the leadership attributes that are most essential in modern, agile businesses. We touch on it briefly in the book, but it’s worth exploring further.

There are, says Carol (in ‘Mindset: The New Psychology of Success’), key differences in how we view our personality. A ‘fixed’ mindset is founded in the view that your intelligence, character and creative ability are static and so cannot change in meaningful ways. Success is an affirmation of those inherent attributes which compare favourably to other fixed standards. Avoiding failure and striving for success become critical ways to maintain the feeling of being skilled, smart or accomplished, and so pursued at all costs.

In contrast a ‘growth’ mindset relishes challenges and sees them as an opportunity to learn, and failure as an opportunity to grow and improve.

The context of Dweck’s research is mainly focused on children, students and how they learn but I think there are strong parallels to defining what successful organisational cultures and leadership look like in the modern world.

In Dweck’s research (summarised in the video above), children with fixed and growth mindsets demonstrated very different approaches and goals. Those with the latter recognised the need for effort, work and practice in order to improve. Their goal was to learn at all times and at all costs. Conversely those students with fixed mindsets were afraid to try new things in case it made them look dumb. Their goal was to look smart at all costs and to avoid tasks that might show deficiency.

More than this, there was a key dynamic in the relationship between ability and effort. Those with fixed mindsets believed that if you have the inherent ability then you don’t need to put in the real effort. Any setbacks simply reveal their limitations and so they try to avoid deficiencies or failure at all costs, and have no way to effectively handle real difficulty. Growth mindset children however, believe in improvement through effort and practice, and relish hard challenges as an opportunity to learn.

Dweck says that this difference is a fundamental reason behind so many students not reaching their full potential. With the universal need for continuous improvement, and more than ever for rapid and constant learning, I believe that these different mindsets and cultures are a fundamental reason why so many organisations fail to reach their full potential. An organisational mindset that rewards leaders for looking smart and never admitting when they don’t know the answer or have made a mistake, is a culture that will not support learning. A business that is too focused on outputs to the detriment of how those results will be achieved is one that will struggle to find new and potentially exceptional alternatives.

Dweck has shown that these mindsets can be transmitted through words and actions. If we are to embed a culture of continuous learning in an organisation we need to be very attuned to the behaviours that we support and those that we discourage. With children, Dweck has shown that praising intelligence rather than effort encourages a fixed mindset from a very early age. It can turn students off from learning. Instead, praising the process, the strategies, or the effort leads to a desire to persist, to experiment, and to learn at all times. We need to take the same approaches with our teams.

These behaviours are very subtle and yet the value system that we create within organisations are hugely powerful determinants of success and failure. In the modern world we need to regard every initiative as an opportunity to learn and we need to recognise the importance of behaviours that support a growth mindset and culture.

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Placing data at the heart of your business – data centric

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data centric

Being data centric, placing data at the heart of your business.

At the end of 2016 McKinsey Global Institute released – The Age of Analytics report.
In it they revealed:

  • The biggest barriers companies face in extracting value from data and analytics are organizational; many struggle to incorporate data-driven insights into day-to-day business processes
  • Leading companies are using their capabilities not only to improve their core operations but to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most dynamic in some markets.
  • Data and analytics underpin several disruptive models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets
  • Above all, data and analytics can enable faster and more evidence- based decision making.
  • MOST COMPANIES ARE CAPTURING ONLY A FRACTION OF THE POTENTIAL VALUE OF DATA AND ANALYTICS

Data academy

So it was great to see Marks & Spencer (UK retailer) launching the first Data Academy for retail, upskilling 1,000 staff as part of its data skills initiative.

Steve Rowe, chief executive of Marks & Spencer said:

“Transformation of our business is key to survival and a huge part of this lies with our colleagues,”

“We need to change their digital behaviours, mindsets and our culture to make the business fit for the digital age..”

What we’re witnessing is the rise in the data haves and the data have-nots, not just acquiring and reporting on data, but the empowering of staff, letting them ask questions of the data in order to move at pace, create better and more fluid ‘experiences’ for customers, new business models exposed by data, and enabling them to drive limited resources to the right areas of the business in order to change and grow.

Note how Steve Row mentions “…digital behaviours, mindsets and our culture…”

Mindset. This is a key differentiator, he’s not saying it’s a tech led initiative and it’s not just having the data, it’s about the people and their attitude and aptitude toward data.

Data architecture

Data architecture has been consistently identified by CXOs as a top challenge to preparing for digitising business as was stated in McKinsey’s Why you need a digital data architecture to build a sustainable digital business

“Leveraging our experience across industries, we have consistently found that the difference between companies that use data effectively and those that do not translates to a 1 percent margin improvement for leaders. In the apparel sector, for instance, data-driven companies have doubled their EBIT margin as compared to their more traditional peers.”

“Using data effectively requires the right data architecture, built on a foundation of business requirements. However, most companies take a technology-first approach, building major platforms while focusing too little on killer use cases. Many businesses, seeing digital opportunities (and digital competition) in their sectors, rush to invest without a considered, holistic data strategy.”

Maybe using data effectively is as much about the breadth and depth of how it’s utilised by people across the organisation, especially as machine learning and AI accelerate.

Which segways nicely into data strategy…

Data strategy

No action without insight.

An HBR post entitled What’s your data strategy provided some insights into what’s typically missing when organisations consider data. They said:

“Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all.”

“More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data”.

They break down what they term as The Elements of Data Strategy into two parts; Defense and Offense. A company will migrate across the spectrum of Defense and Offense dependent on things such as the company’s overall strategy, its regulatory environment, the data capabilities of its competitors, the maturity of its data-management practices, and the size of its data budget.

I’d also add that it should also look at the capability and empowerment of it’s staff in this regard, after all having the tools is one thing but knowing how to use them and the questions needed before deploying them has to be key. Critical thinking from a data perspective if you like.

Data is cheap, strategy still matters. So does education.

data mindset ven diagram

Data ‘Mindset’ at the heart of your business

To most people data is boring, cold, clinical and at times intrusive, but if you embed a culture of ‘data first’ as we have to date with ‘digital first’ how might that change your business?

What if your staff already know what data is important and naturally or instinctively use it in the right way, then data surely becomes the beating heart of your organisation. Action the data in a fluid way not just report on it, flip the 80% discovering and preparing into ‘doing’, ‘learning’ and ‘doing’ …rinse and repeat.

 

 

 

If you’re running innovation or business model workshops in your organisation and looking for a canvas to help you put data at the centre take a look at our canvas at Crank. Email me if you want to know more about how it’s used.

Data centric canvas

Data centric business model canvas

You can read more about  data and business agility in the book.

agile_business_book

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In Order to Scale You Have to do Things That Don’t Scale

By | AgileBusiness, Culture, Customer Exprience, Leadership | No Comments

Airbnb and scale

Large organisations are pretty obsessed with scale, and naturally so in many instances. Ideas need to have big potential impact, returns need to be sizeable, programmes and projects need to work robustly across large numbers of employees or customers. It’s an inherent part of the mindset.

But what happens when you need to start small? And what happens when you need to do something that doesn’t scale? I loved the story that AirBnb founder Brian Chesky tells (in this Masters of Scale podcast) from the earliest days of the service when they were still part of the Y-Combinator programme. Paul Graham (founder of Y-Combinator) had asked him where they were getting traction with their idea. Brian told him that they didn’t actually have a lot of traction at the time, but they did have a few people in New York who had started using the service:

Graham: “So your users are in New York and you’re still inMountain View.”

Chesky: “Yeah.”

Graham: “What are you still doing here?”

Chesky: “What do you mean?”

Graham: “Go to your users. Get to know them. Get your customers one by one.”

Chesky: “But that won’t scale. If we’re huge and we have millions of customers we can’t meet every customer.”

Graham: “That’s exactly why you should do it now because this is the only time you’ll ever be small enough that you can meet all your customers, get to know them, and make something directly for them.”

So the founders literally commuted from Mountain View to New York, and went door-to-door meeting the Airbnb hosts in person. To give them a reason for visiting they personally offered to photograph the host places for the site. When they’d talk to the hosts they would get direct feedback that enabled them to start designing ‘touchpoint by touchpoint’ in order to handcraft the user experience and feed directly into their product roadmap (‘the roadmap often exists in the minds of the users you’re designing things for’). Almost all of the early features that would become critical to Airbnb’s success came from that early feedback.

As Airbnb grew, that habit of handcrafting the user experience stayed with them as they visualised what a truly exceptional experience might look like in order to challenge thinking, and work back from there to deliver a service that is truly remarkable:

‘The core thesis is if you want to build a massively successful company, you need to build something that people love so much they tell each other. Which means that you must build something worth talking about.’ (Chesky)

It’s too easy in large organisations to dismiss doing things that don’t scale. Like talking face-to-face with your earliest users to craft remarkable experiences. It’s too common for leaders, as they progress higher up the hierarchy, to become more and more distant from actual customers.

We need to challenge these conventions.

And that’s as much about culture and mindset as it is about process and practice.

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