Self-Driving Cars are all the rage. Mary Barra, CEO of General Motors, said recently in the German Newspaper, Handelsblatt, that the automotive industry will change more radically in the next 5 years than in the past 50 years.
The key drivers she says are the e-cars and autonomous driving. Vehicles driven by data, that autonomously move you from your home to work and back again. With this technology comes an implicit shift in our expectations: Autonomous vehicles will have
little value unless they deliver us to the right destination at the right time.
What about if the same would hold for data?
What about if data gets to its required destination right on time – automatically?
Data’s Long and Winding Road
For the most part, this is not how it works these days. Today we we need to go to data, i.e. we need to search for information. The information doesn’t find us. For this we need search engines like Google. We need to know what we are looking for on Google, or else we wont find anything. We need to open countless files on a filer to find the document that we know is there… somewhere. It’s small wonder then, that most data is never used beyond its primary use. The fact is, it’s simply too complicated and too time consuming to get to that information.
This needs to change. Data, and our expectations of it, need to catch up to the world we’ve created with it. Imagine this: what if data ‘knew’ about our informational needs and could find its way to us? For business, this will be game changing.
Let’s say you’re about to call an important customer and their contact details are stored in a CRM system. The context of your call, and whether or not it’s even optimal or opportune timing to call at that moment, would be changed and challenged if you also had the data inform you about the last three product discussions between that customer and the company, or the breaking news of a critical new partnership; or even if that the customer was currently experiencing a major service incident. You’d no longer be one step behind your customer’s experience; instead you’d be travelling their real-time journey right alongside them.
Setting a new course for Data: Relevance, Timing and Destination
For this future to be the new norm, data needs to know about its own meaning and its contextual relevance. It needs to be enabled to find its way to its best possible destination – automatically. It needs to become a self-aware and self-operating piece of data that finds its way around the informational universe it lives in, and on the way, collate with other data to form new types of insights that are highly relevant to the end-user, which might be a human operator, or a machine driven by software requiring this input to make its next smart move.
While truly sentient data remains the stuff of science fiction, our data-driven world of today still demands the convenience and value such scenarios offer. Thankfully, there’s an intermediate step in the form of enabling software to add additional dimensions to existing data. By enriching the data in this way, it becomes computable. That’s the first condition.
The second condition is context: Within an existing enterprise software setup the context is driven by the user’s access control list and the work process within the current application. Add to this individual user activity parameters and you’ve got a got first approximation of the context.
Conclusion? By combining enriched data & user context we’re able to make data autonomous today: delivering the right information at the right moment in context.
PS: If you want a chance to find out more about the first such end-to-end context intelligence platform and how we’re already helping leading companies to elevate their data-driven enterprise, join us for one of our upcoming webinars focusing on Service Insights or Customer Insights.
PPS: Picture credits: Internet Archive Images: Coast watch, ca. 1979.
