Hi ,
Back in 1975, Kodak engineer Steven Sasson invented the world's first digital camera... an invention that Kodak management subsequently rejected and buried, for fear it might cannibalise their existing business.
At the time, Kodak had few rivals and sold 90% of all film and 85% of all cameras in the United States alone. And for the next 20+ years, Kodak's profits continued to grow.
That is, until they didn't.
In 2012, Kodak filed for bankruptcy, largely due to them leaving the transition to digital photography far too late.
There's no doubt Steven Sasson had a great idea. But the audience for his idea was completely wrong.
Here's the thing...
Last week, I wrote of the importance of data scientists matching their skills to the business problems best suited to making use of them.
These problems can vary by business - a digital camera manufacturer is likely to be more
interested in solving computer vision problems than an insurer or bank, for example. But, they can also vary by individual within a business.
Sasson's invention directly addressed a key business problem faced by Kodak. That is, ensuring its long term financial sustainability. But what it didn't address were the problems of the key
decision-makers within Kodak’s leadership team.
These problems likely centred on maximising Kodak's short- to medium-term profits. And from that perspective, Sasson's invention was seen as a threat.
Data science can also be viewed this way by individuals within an organisation.
A machine learning model built to automate a manual process might address a business's problem of minimising costs, but if pitched to the individuals whose jobs are put at risk by the deployment of such a model, the model is more likely to be
buried than praised.
Steven Sasson's story has a happy ending. On November 17, 2009, US President Barack Obama awarded Sasson the National Medal of Technology and Innovation - the highest honour awarded by the
US government to scientists, engineers and inventors.
But who wants to wait almost 35 years for their work to be valued and recognised?
Data science and AI can be seen as either an
opportunity or a threat, depending on the problems of the beholder.
To see success in your work sooner than 35 years from now, it pays to target the right audience.
Talk again
soon,
Dr Genevieve Hayes.
p.s. I recently spoke to CX consultant Dasun Premadasa about finding the right audience for your work. You can listen to our conversation HERE.
