“What these have in common, of course, is the direct involvement of the consumer and the connectivity that enables that. From the business point of view, that means we can service customers very quickly and we also collect lots of data. But there is some general feeling that all of this innovation and disruption is coming from businesses born on the Web — and it’s not true. One of our EY clients internationally is General Electric. As a 124-year old conglomerate it is hardly a Wwb star. Yet GE is transforming its entire range of medical and hospital equipment by using IoT technology. Instead of the scheduled maintenance routines of the past, with technicians visiting hospitals periodically, the machines now literally ‘call home’ at intervals as frequent as every 30 seconds or when something needs to be reported. That completely transforms the medical equipment industry as well as incorporating an active service element that connects GE more closely to its customer base.”
No one tech
There is no single technology that governs digitisation in the enterprise, what we now call digital transformation, O’Reilly believes. “There web businesses that are almost virtual and major organisations that have had bruising decade-plus encounters with ERP systems or whatever and made massive investments. But that is really good, because they have standardised processes and have captured data — although not all of them recognise the extent or the value of that information. The next level to add on top of that investment is analytics. There is a wealth of data already collected that can be analysed and enriched in many ways.”
Not so long ago we had a real challenge with data volumes and analytics performance, says O’Reilly. “We did not have the power to crunch large volumes quickly enough to be really useful. Today, the advances in processing power, storage and cloud mean that the analytics capabilities are nearly commoditised. One key result is that data analytics and data science are now in the mainstream but we need a set of things to make it all work well for the smart organisation.
“It starts with the talented data science people, who can really make the data sing. They need the support of data engineers, who understand how to move and store and handle large volumes of data. But then, and most importantly, you need the domain and business knowledge of experienced people. Dare I say it: the pure tech stuff is easy. It is the human skill sets that ensure and enhance the value.”
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