Professional enterprise businesspeople don’t always like to get their hands too dirty with crowdsourcing. It’s just too close to crowdfunding for comfort and they sometimes confuse the two terms.
The very use of the term crowdfunding has been defined by the act of startup entrepreneurs looking to obtain funds and investment for a particular task, project or product launch.
Why technology vendors co-create
Crowdsourcing, on the other hand, is markedly different and has been around for centuries.
In 1783, King Louis XVI of France offered an award to anyone who could ‘make alkalis’ by decomposing sea salt through the simplest and most economic method.
That was pretty much the first documented act of crowdsourcing. The term of course simply means ‘asking the crowd’ and it has been applied in areas from public policy to linguistics to ornithology and yes, even to journalism as a fact-checking mechanism.
A crowdsourcing renaissance
The use of crowdsourcing at the enterprise level is due for a renaissance and every business in every vertical stands to benefit from the techniques being refined here if they understand what’s happening at the lower substrate level.
Driven by advances in big data analytics, cloud network connectivity and Artificial Intelligence (AI), we are now able to build new layers of intelligent automation to execute business decisions.
That’s all just great and fabulous. Automation intelligence power is here. But where do we get the intelligence in the first place? Answer: we crowdsource for it.
As we seek crowdsourced intelligence, we need to define look deeper into the box to understand what types of data we are ingesting.
Crowdsourced intelligence for automation awareness comes from ‘other companies’. Appropriately anonymized and obfuscated, large-scale enterprise technology services vendors can now define types of work into data workflows and processes.
Those data workflows and processes have size (amount of data throughput), shape (connectivity to certain types of applications) and supply needs (the amount of processing, storage and analytics needed to perform).
They also have a success factor i.e. the number of times they worked effectively to increase profits for the organization using the process.
Smarter algorithmic business models
If we take all those sizes, shapes, supplies and successes into account, then we can build an algorithmic model that other companies can use to make more intelligent automated decisions. We feed that model with crowdsourced data
We can break these models down into different work process for different types of companies. We can even break them down into different types of teams with different skillsets. We can go as granular as we need to, based on the requirements of the organization and the tasks at hand.
Ultimately, we can start to use these new levels of intelligence to bring forward prescriptive analytics into the organization. This ‘forward thinking-ness’ will allow us to pinpoint problems before they occur and anticipate market change before our competitors.
Data privacy worries
Some firms will still be worried about data privacy when attempting to apply anonymized crowdsourced information sets to their business models.
While these doubts may be unfounded, these firms always have the bimodal IT option of just using automation intelligence derived from crowdsourced intelligence in experimental arms of the business.
Whether you know it (or indeed like it) or not, your organization’s ability to tap into the next generation of intelligence services requires that you embrace crowdsourcing in a very data-centric kind of way. Let’s be all-inclusive going forward.