Data: humans are generating more and more of it and at an increasing rate. So much in fact, that 90% of the data that exists today was generated in the last two years alone.1 Big corporations have taken advantage of the huge value this data represents by focusing their efforts on collecting it - including intimate details of our lives. From our likes, dislikes, location, and political leanings, to our “private” conversations, data mining has stimulated a buzz around privacy concerns.
An infinite resource
I recently read two articles which provided an interesting perspective on how BIG “big data” has really become. The Economist makes the bold claim that “The world’s most valuable resource is no longer oil, but data”, and draws many analogies between the two industries. Both oil and data fuel our way of life and are responsible for countless products and services that have become indispensable, reliance upon which has become mission-critical in some cases. The sheer dominance of the biggest tech companies over the data streams they control has even prompted some to ask for their breakup, similar to the perceived threat of powerful monopolies like Standard Oil over a century ago.
Both articles are well researched and well worth the read (links at the end of this article), but they got me thinking about one important difference between O&G and the data economy. Oil is a finite resource. It exists in limited reserves and deposits, and the process of accessing and deriving value from it is expensive and complicated. Data, on the other hand, is a non-rivalrous resource and in some cases also non-excludable. The quantity of data, unlike oil, is actually increasing. Companies of all kinds are sitting on vast amounts of historic data, while also generating new. So why aren’t more non-tech companies taking advantage of this?
A competitive advantage and a moat
Data can be used to gain insights that were previously difficult or impossible to discover. These realizations help companies improve their products, services, and delivery, and provide advantage over competitors. Facebook and Google started out by using basic demographic data to target advertising at a subset of consumers. But now, based on our likes, and in combination with countless other interactions through the range of their “free product” suites, they’ve fine-tuned ad targeting and improved their personalized newsfeeds.
One of the best and more recent examples of this type of data-based competitive advantage is the rise of “digital assistants.” Amazon’s Echo device and Alexa assistant has been runaway successes for that company. Google has followed suit with the Google Home, and Apple is set to introduce their standalone device, Sire. The race is on to collect the most raw data. The more data collected, the more intelligent these digital assistants become at performing tasks. The advantage comes from having a more capable device operating off of well-informed insights, which can then better monetize their system through accessory products, services or third party marketplaces.
These product improvements draw more customers, increase device sales and fuel an even greater engagement that feeds the flow of data. This effect has now been coined as the “data-network effect.”
Not only can data help with product and service improvement, it can also provide important insight into the current state of your industry. By helping you to spot trends early, data can allow you to preemptively pivot your direction, and maybe even prevent you from getting blindsided by the competition. Used effectively it can surface opportunities for interesting partnerships. Or for larger companies, it can help form the basis of an M&A strategy, in a similar fashion to how Facebook scooped up startups like Instagram and WhatsApp.
He who has the data wins
It’s a matter of introspection and looking at one’s own data to draw analogous comparisons to your own business and industries. What’s clear is that data will continue to play an increasing role in business. A significant amount of value will be placed on data as a core asset. Tesla sold only 25,000 cars in Q1 compare to GM’s 2.3 million, and yet Tesla is valued more than GM. This is in no small part due to the 1.3 billion miles of self-driving data Tesla has accumulated.2
Thanks to the advent of machine learning and other AI techniques, companies no longer need a small army of analysts combing over the data. Further, there are third party platforms such as IBM’s Watson that apply machine learning and other AI principles to extract more value and new insight from your data.
Unlike O&G, every company has data and can find even more data sources, both internal and external. It is imperative that companies have a data strategy. Think about the data points that your company is or isn’t collecting. More importantly, this is about what decisions are currently based on data and what you might be able to do if you just had more insight into X, or data on Y. This can form the beginning of your data strategy. And of course, give us a call. We would love to talk data with you.