Post #86: All about data
In case you missed it, Big Data LDN took place earlier this week. For the past two years your author has trekked to the Olympia conference centre in west London to listen to a plethora of presentations, network with interesting people and learn more about data. With conferences a thing of the past, this year’s event took place exclusively online, as pictured below. It proved equally fulfilling and was certainly highly time efficient.
Presenters from IBM and Microsoft shared their views along with numerous start-ups. The tone from this year’s event was markedly more humane and less hubristic than in previous years – perhaps a sign of the times. There was a notable focus on the quality of data over the quantity. Put another way, if you put garbage in (to algorithms), then you may get garbage out. Additionally, data governance is assuming an increasingly significant profile. Beyond data rights (who owns your data), training algorithms appropriately – to avoid biases – was considered crucial by most participants.
Another prominent take-away from this year’s event was that the current pandemic has underscored the relative immaturity of many companies’ levels of digitalisation. Many businesses have therefore put in place accelerated digitalisation plans as a result of the changes wrought by COVID-19. If the world is moving more digital – our contention – then it is crucial to lever data effectively. Some 90% of enterprise data currently goes unanalysed (per IBM), creating a clear opportunity. Data can help fuel automation and artificial intelligence (AI) can help unlock the value of this data. Consider the case study of PayPal: some 60% of its inquiries are now AI-automated, resulting in clear cost savings. Blockchain solutions may also play a crucial role in the future. As one Chief Executive put it, “paperwork shouldn’t even exist.”
If anything was clear, then it is that the future will comprise more data, most likely in a multi-cloud, multi-vendor environment. AI can make data better but needs to be deployed judiciously. All the above requires more silicon (i.e. more semiconductor chips) too. Watch this space: October will see the publication of our next detailed theme piece, an updated version of our original “data deluge” thesis, first developed close on a decade ago.
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