Big data analytics is big now in the digital world. Take a moment to think about all the data being collected somewhere every minute about people’s transactions, purchases, complaints, bookings, cancellations, preferences and requests. The chance to harness all this information to “make sense of things” is an opportunity too promising to ignore. This explains why data mining and analytics gurus (the whole big data industry) are in high demand.

But it’s important to note that data, alone, skews toward the quantitative; and what that data “means” and/or what it predicts is heavily dependent on human insight. That is, the fact that millions of people have switched to some kind of smartphone has certainly helped those in the communications industry lay out some fairly sound predictions about how many more smartphones will be sold in the future. And the data related to current users can also make a strong case for the type of features people will be looking for months from now.

It’s critical to note, however, that a large measure of “beyond the numbers” insight is required to move beyond bits and bytes to understand the deeply human dimensions of emerging behaviours. That people are moving to smartphones is something that can be supported by lots of data. What a nation of smartphone users means for telephony and a lot of other industries requires a more humanistic understanding — because it requires a deeper understanding of why people are paying more, and happily, to use smartphones.

Collecting data is a science; adding insight is more of an art. Put them together and you have what I call “opportunity foresense”, or the ability to sense shifts in meaning (and even identity) that can then be examined and projected. Big data modelling only makes the process and outcomes from an opportunity foresense exercise that much more powerful. What do you need to achieve opportunity foresense?

First, you need reliability. For years, those in the business of designing products and services have sought to have data collected that proves that an innovative idea can become a reliable innovation. In other words, something new must not be successful once or a few times, or just in a lab: it must be reliable everywhere it is introduced. Only reliable processes make for worthy investments. Reliable processes have long been endearing to finance, engineering and operations professionals because they generate predictable results. Fedex didn’t grow its business because it could deliver one package “absolutely, positively overnight”. Over time, it demonstrated the ability to deliver lots of packages. How many? Go to the Fedex website, and you’ll find the answer: “more than 3.5 million packages and 11 million pounds of freight” (on average, daily) to “more than 220 countries and territories….”

Global delivery mapYet, if you were involved in a new business somehow tied to parcel deliveries, all the data in the world about package sizes, cargo weight and delivery vans would not be enough to fully understand the critical role that human needs and desires serve as the cornerstone of new business opportunities. For that, organisations must also embrace design tenets that include “validity” as an alternative metric to “reliability” for decision-making, especially in early stage initiatives and other circumstances where shifting paradigms disrupt conventional wisdom.

Validity derives from a deep understanding of an opportunity (often in the form of a problem) and its context. Validity is driven by a perception of how things could be and a corresponding hunch which cannot yet be proven as reliable. Validity therefore thrives in an unpredictable world, and is informed by human senses such as sight, touch, taste, smell and hearing as critical perceptions that frame experimentation in search of concept validity. When pursuing validity, less emphasis is placed on replicating a process and more attention is given to confirming intuitions. By focussing on the intuitive properties of validity, organisations are empowered to explore new sources of value with rigour while managing both the downside risk and preserving the upside potential — a critical balancing act in most risk-averse organisations.

The story of how Fedex came to be and the trials of its founder, Frederick Smith, is captured here. But one anecdote, from that source, helps me make this important point. “While attending Yale University, Fred Smith wrote a paper on the need for reliable overnight delivery in a computerised information age. His professor found the premise improbable, and to the best of Smith’s recollection, he only received a grade of C for this effort, but the idea remained with him.”

Although many have probably laughed at that professor’s lack of opportunity foresense, I would just say that he was right in probably wanting some reliable data about the then-innovative idea of an overnight delivery service. But, if he had wanted to really be helpful to Smith, he would have probed hard about the foundation for Smith’s perception that anyone actually needed the service — and would pay for it. In other words, the professor should have graded Smith’s idea on reliability and on validity.

Only by understanding the difference between a reliability question and one of validity can entrepreneurial thinkers make informed decisions about the nature of a problem. And, only by this dual-focus approach can they be sure that they have the the corresponding tools needed to gather the insights needed to design a new product, service or business and then think through how to perform against the promise of the innovation.