As we enter what is widely billed as another generational shift in computing, we are adopting the cloud-computing model of networked datacentre IT to drive the way all our software applications work. From this wider, deeper, thicker and altogether more algorithmically intelligent backbone, we can build crowdsourced knowledge bases that far outstrip any previous notions of learning and intelligence, however encyclopaedic they may have been.
Crowdsourcing in the cloud era, or cloudsourcing, gives us the chance to connect mass information streams that come together in new ways. How we now start to apply the sound of the crowd in the cloud to our operational business models is crucial.
What crowdsourced knowledge can do in life and business
Applications designed to exploit crowdsourced knowledge in the cloud are many and multifarious. Take soil erosion issues for want of a seemingly random but extremely relevant example.
If walkers, trekkers and other outdoor types are suitably targeted and engaged, their collective presence can represent great environment value. Where cliff edges are crumbling away, local authorities can post signs encouraging walkers to snap pictures with their smartphones and upload them to a website.
That website isn’t just a page on the internet; it’s just how it renders graphically. It is actually a cloud database sitting on a server in a datacentre that can be programmed with image analytics software to track where the landscape is changing. In this scenario, the crowd and the cloud are helping to protect your weekend walks and picnics.
The cloud-based collective hive-mind concept has an extremely logical application in business too. Major software vendors are now working to create what we could call templates for business decision-making. The knowledge distilled into these architectural reference models represents a new type of playbook for other customers to use and apply to their own business models.
One customer doesn’t actually get to physically see the customer details or individual data values that other users will have fed into these templates, all that is appropriately anonymised and obfuscated in the interests of governance and compliance. The theory is that one clothing manufacturer or retailing specialist, for example, should be able to learn from the cloud-based crowdsourced data flows that another manufacturer or retailer has experienced.
Cross-pollination of crowdsourced data can revolutionise business
Where it becomes even more interesting is when clothing retailers start to apply operational model efficiencies learnt from oil rig operators, cake bakers, holiday companies and so on. There are many levels to business and cross-pollination through the cloud-crowd technique can be equally multi-tiered.
Financial trading firms are embracing this cross-pollination by employing crowdsourced talent in their investment strategies. “A crowd of insights and ideas gives investments an advantage,” says Jared Broad, founder and chief executive of QuantConnect. “Through our cloud-coding environment, we see an evolving and growing trend of the asset management industry leveraging quantitative multi-factor models to generate higher returns.”
Independent technology analyst Theo Priestley agrees that the ability to learn from crowdsourced data across multiple sectors is an unparalleled opportunity for businesses to finally break out of their industry-based silos.
“There’s no reason why a utilities company cannot combine cloud-based datasets from medical records to understand energy demands from home-care patients recovering from injury for example,” says Mr Priestley. “What’s more, the costs of such a collaboration on crowdsourcing in the cloud can be spread across all organisations taking part. There’s no downside here.”
How crowdsourcing can help with resources and talent
The same theory extends to analysing cloud computing usage itself. We can globally crowdsource cloud-data workload trends for different types of applications. When we collate and analyse those patterns, we can create predictive models that enable us to manage cloud resources more effectively and at better price points.
“Companies using cloud computing are faced with the challenge of guessing what level of datacentre resources to allocate to their new connected apps. Many firms will tend to ‘super-size’ how much cloud they sign up for to mitigate risk. Stopping to examine a crowdsourced library of similar apps can help them to better size their cloud consumption at the point of initial deployment” says Ayman Gabarin, senior vice president for Europe, the Middle East and Africa at public cloud optimisation analytics company Densify.
Mr Gabarin says typical application resource requirements evolve over time, so cementing cloud resource intake is far from ideal. Making apps aware of changing requirements and self-optimise by using machine-learning to right size themselves continuously and predictively makes them run better, and reduces waste of cloud resources.
These moves to encourage collective, collaborative and co-operative attitudes across cloud-computing frameworks are still largely new territory for the business world. Indeed, as much as it is widely discussed in technology circles, cloud computing itself is still comparatively new territory for many businesses.