When Big Data began its emergence into the discussions about the future, two elements stood out for the field of strategic exploration – what you do before you plan. The first was futurist Daniel Burrus, who labeled Big Data as the #1 trend that will transform 'how we sell, market, communicate, collaborate, educate, train, innovate, and much more:'
Rapid Growth of Big Data.
Big Data is a term used to describe the technologies and techniques used to capture and utilize the exponentially increasing streams of data with the goal of bringing enterprise-wide visibility and insights to make rapid critical decisions. This new level of data integration and analytics will require many new skills and cross-functional buy-in in order to break down the many data and organizational silos that still exist. The rapid increase in data makes this a fast growing hard trend that cannot be ignored.
The second element, in the introduction to fascinating examples presented by Rick Smolan, in The Human Face of Big Data:
- “The world is about to change forever because of this sudden ability to measure and sense the world in real time.”
- “We need to have the smartest people on earth aware of, and talking about this.”
- “…the potential and consequences of which few have even started to contemplate.”
- “Like all new tools, Big Data carries the potential for unintended consequences.”
Strategic exploration, “what you do before you plan,” is the focus of futurist Joel Barker’s work. Because the work is focused on the future, much of the work, and the tools he’s brought to the process, focus on identifying the consequences, both positive and negative, of an innovation, a trend, a new strategic objective, or any change.
A key part of this work is the focus on identifying those potential “unintended consequences,” which in many cases can be identified before they occur.
'You’re a lawyer, Victoria, and you know about foreseeable consequences. If you can foresee the consequences, you are charged with intending them, are you not?' (Lisa Scottoline, Devil’s Corner)
When the wagon trains headed west in the 19th Century, the Wagon Master sent out scouts to look over the horizon and discover safe passageway for the journey, a passageway that had smooth trails, easy water crossings, and resources for families and animals. The scouts worked quickly, explored multiple options, mapped the information discovered, and provided qualitative decision-enhancing information for the Wagon Masters.
Today’s leaders need similar information for a safe journey through the world of Big Data. They need scouts with the same characteristics, in the 21st Century, to scout over the horizon of time.
Joel Barker’s Implications Wheel®
The Implications Wheel® is a strategic exploration tool that provides decision-enhancing information to leaders on a key issue from the world of Big Data. Working as “scouting teams,” with the same characteristics of speed, multiple directions, and qualitative information, the scouting teams, made of diverse individuals, provide a “Wisdom of Crowds” approach to the positive and negative consequences.
Exploring the possibilities of “Big Data” is too broad and would be similar to the 19th Century scouts being directed to search “the world.” These scouts were working within a clearly defined vision – going west to California. Our scouts for “Big Data” must also start with a more clearly defined direction. There are many, many possibilities because of the huge scope of Big Data. We started by exploring some specific “directions:”
- What are the possible implications of “Big Data” for college education?
- What are the possible implications of starting a “Big Data” initiative?
- What are the possible implications of the growing trend of collecting and using "Big Data" to influence consumer behavior?
For these explorations, we used our trained Implications Wheel facilitators as scouts. The Implications Wheel works by identifying “first-order” implications, what might happen, in this case, if this trend continues? Then, for each ‘first order” implication, five “second-order” implications. And for each “second-order” implications five “third-order” implications.
For the “Big Data in College Education” exploration, the scouts generated 620 specific implications, both positive and negative, scored for desirability and likelihood from the point of view of “College Freshman or Sophomore.” The exploration was also scored from a second perspective, “Business Community.”
Key “First-Order” Implications
There were 18 “first-order” implications. Based on the “first-order” scoring for desirability and likelihood, the following key implications emerged:
- Universities begin to develop curriculum on "Big Data" (courses, certificates, majors, etc.) [Scored “Extremely Desirable – Extremely Likely”]
- Students who manage "Big Data" effectively begin to receive better job offers [Scored “Extremely Desirable – Very Likely”]
- College feels even more intimidating for students on the underside of the digital divide[Scored “Very Undesirable – Very Likely”]
- University tech resources become overwhelmed with demand for services/support [Scored “Very Undesirable – Likely”]
- Simulations and experiential learning becomes significantly more important [Scored “Extremely Desirable – Extremely Likely]
Second- and Third-Order Implications
Most individuals are pretty good at identifying “what’s next?” – “first-order” implications. In one of our experiments using MBA students, 85% of the implications they identified in a free-form process were “first-orders.” The “second- and third-order” implications came from only three participants, one with “black belt” Six Sigma training including the classic “5-Whys” and another with extensive experience installing networks who always thought about “multiple connections.”
The Implications Wheel process flips this natural way of thinking “What’s just around the corner?” and expands looking at “What happens after that?” and again “What happens after that?”
With just the five “first-order” implications listed above, there would be 25 “second-order” and 125 “third-order” implications. The full display of these five “arcs” is shown here. A sampling of some of the most significant, and most interesting, implications follows. The display shows the connections, the pathways, and the scores (all samples are both likely and significant) of the implications.
Sample “Second-Order” Implications
- Professors required to spend more time with individual students as guides
- Universities apply for grant funding to develop next-generation simulation and experiential learning technology
- Businesses that benefit from the new curriculum make heavy endowments to the University
- Some black-outs occur because of overloaded IT systems (i.e., electrical demands)
- Student stress levels increase for students vying for limited openings in Big Data major
Sample “Third-Order” Implications
- Employees work in self-imposed silos (= no team spirit)
- The number of digitally challenged workers increases significantly
- The field of developing learning techniques for students with disabilities increases exponentially
- Discrimination lawsuits based on learning disabilities become rampant
- Companies start on-line tutoring programs to meet the demand for B.D. tutors
Just these disconnected implications reveal the power of exploring the consequences of an innovation or trend. With the full “map” of implications as shown above, the power of the Implications Wheel exploration is exponentially greater.
“When the Scouts Return”
When the scouts returned to the Wagon Master in the 19th Century, they provided information for decisions to be made about the next day’s journey. They allowed the Wagon Master to prepare for the advantages of information from a scout who had seen a clear path with sufficient resources and easily conquered obstacles. Similarly, the Wagon Master used the information from the scouts to avoid serious obstacles on another pathway or be better prepared for a dangerous river crossing that was the only alternative.
In the same way, the Implications Wheel offers leaders the opportunity to “construct barriers” to implications that are significantly undesirable and likely to be better prepared, to minimize the negative impact, or avoid the hazard completely. The sample above, “black-outs occur,” is one that can be addressed in a variety of ways – before it occurs! Similarly, the Implications Wheel provides opportunities to “create bridges” for implications that are significantly desirable but unlikely to occur.
From a sample that’s not included above, “Some universities merge with K-12 schools to transfer technology skills downward,” identifies an opportunity for a leadership team craft a strategy to make this more likely to happen. And a final element, also in common with the Wagon Trains, is the need to send out a scout with special insights on a part of the terrain, to obtain more information.
With the Implications Wheel, it is important to identify implications where information is needed. The connections of the Implications Wheel show more clearly what information is needed. If “grant funding” is likely an important implication down the road, a valuable step would be to alert the grant department on the increasing importance of Big Data and have them start monitoring the issue as it relates to possible funding sources.
The 21st Century Conversation
There are questions posed daily about “the possibilities of Big Data” for the Internet of Things, for Health Care, for Google’s analyzing the Ancestry.com database, for multiple sports applications, and the list goes on. As Rick Smolan was quoted above: “…the potential and consequences of which few have even started to contemplate. Like all new tools, Big Data carries the potential for unintended consequences.”
Adding to this, there’s a flood of criticism about the tendency for thinking in the short term. At the World Economic Forum Italy’s Prime Minister Mario Monti stated “leadership is the opposite of short termism.”
The Implications Wheel® is a 21st Century conversion of Big Data. It provides a new kind of vision, a new responsibility, and the beginning of the conversation for everyone interested in Big Data, not the end.
Note: Complete results from the exploration on Big Data for College Education are available from the author.