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How much should the stakeholders budget for the unknown?

Posted By Administration, Monday, March 9, 2015
Updated: Saturday, February 23, 2019

Alireza Hejazi shares his thoughts with us about “budget for the unknown” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

Attending a summit on the investment in R&D, I found the majority of R&D outputs discussed in the summit were professionally polished secondary research. A panel of experts was tasked to evaluate a strategic framework documenting a baseline, as well as alternative futures for a number of stakeholders active in the construction industry. An interesting debate was ignited in the panel when I suggested three points to be considered in their appraisal: originality, quality and timeliness. Coming back home from the summit, I asked myself how much the stakeholders should really budget for the unknown—the future. To answer that question I wrote this post and I assume those three points may make general criteria in budgeting foresight projects.

Primary or Secondary?

How much should the stakeholders pay for insights offered by futurists? In my view, a criterion can be made based on the primary or secondary nature of research. Secondary research means using other researchers’ data rather than generating one’s own statistics. Using data produced by well-known institutes such as ILO, WTO, UNESCO, Gallup, and etc. a futurist can conduct secondary research. Futurists do more secondary research than primary explorations and most of scanning jobs are based on secondary sources of information.

While secondary research can be precious in the right place, like many other researchers, futurists are expected to create their own data. Normally, primary research offers a better taste of trustworthiness to stakeholders. Governments and NGOs collect and publish statistics, researchers and authors write books and articles based on their observations, speakers write speeches according to their ideas and information, but what do futurists produce? Generally speaking, futurists find, interpret and represent the results of all that data for their clients, books and articles and also their speeches.

The missing point in judging research outputs produced by futurists is that primary data does not interpret itself. A dexterous interpreter is needed to make sense of that data. The collection of the data from various sources can be done by every researcher, but futurists enliven the collected data by suggesting alternative futures. Collecting and interpreting are both necessary, but what is the best data in foresight profession?

According to Gordon (2009), “The best data is primary data—data researched and presented by the original researcher—and the best use is primary use” (p. 14). Results from scientific research which are based on primary data are usually published in top research journals and are sometimes delayed for publishing due to the sensitivity of issues for investors who sponsored the research project and perhaps never published.

The value of primary data can be also revealed in the light of inherent limitations of using secondary data. Those limitations are identified by Burnett (2008) in this manner: “First, the information is frequently dated. Second, seldom are secondary data collected for precisely the same reasons that the information is sought to solve the current marketing problem” (p. 61). The stakeholders want fidelity and they prefer the primary source. The futurists can lead that sense of preference skillfully towards original authentic foresight outputs produced by their own reliable and valid research.

Quantitative or Qualitative?

Potent futurists are expected to organize and conduct both quantitative and qualitative researches. A noteworthy foresight output is expected to open up a window through which readers may peer into the world of foresight to learn more from the findings. Strong foresight works engage the audience by displaying and discussing correlations, values, and other details both quantitatively and qualitatively.

The choice of using a qualitative or quantitative design (or both), for a given research problem is mainly related to the nature of problem. Basically, quantitative methods are appropriate when: “(1) measurement can offer a useful description of whatever you are studying, (2) when you may wish to make certain descriptive generalizations about the measures, and (3) when you wish to calculate probabilities that certain generalizations are beyond simple, chance occurrences” (Williams & Monge, 2001, p. 5).

While most quantitative researches create generalizations that transcend the immediate situation or particular setting, qualitative researches often do not try to generalize beyond the particular situation, but may leave it to the reader to assess applicability (Fraenkel, 2009, p. 15). The history of futures research shows that the majority of studies have been conducted through qualitative approaches. The main reason is that the future is unknown and less quantitative data are normally available compared to other fields of study.

The research perspective, approach, and method should be determined as a consequence of deciding upon the objectives of the investigation. Thus, one particular perspective, approach, or method is neither better nor worse than another, just simply more or less appropriate within the specific circumstances and objectives of a foresight project. What matters for a fair payment are time, fund, knowledge, skill and energy that are devoted by a futurist or a team of futurists to a foresight project through both quantitative and qualitative approaches.

On time or Late?

The importance of each foresight output at any given time depends on aspects of the situation, such as the type of industry and the amount of volatility in the external environment. The consequent is the timeliness of a foresight report that is set up for submission to related stakeholders. The futurists are not the only ones who need time to accomplish research; the stakeholders also need enough time to devise their companies with foresight insights or new strategies proposed by the futurists.

The amount of budget that investors offer to know the unknown is tightly related to available time for decision making or change management. Firms that consistently establish a management reserve for foresight projects can tell us how much time is needed and how valuable a foresight output will be over time. Certainly a specific percentage of the performance budget should emerge as the right amount, but it is directly related to timeliness, potential risks, and the degree of predictability of the industry. As observed by Verzuh (2005, p. 106), “high-risk industries such as software development may add as much as 30 percent to the budget. More predictable projects will use an amount closer to 5 percent of the performance budget.”

The factor of time determines how much should be paid for a foresight output. Over multiple foresight projects, a normal range will appear for both futurist and client. Imagine an alternative scenario like this: A construction company is interested in a particular topic and the CEO decides to hire a futurist to research the topic for them, but time is a determining factor in the success of company. The futurist spends six months researching the issue, and six months doing and writing up the research. How much do you think the futurist could charge for this report? If the CEO needs the final report six months earlier, then how quickly should the job get done? How about the quality of research and how about the payment? Many clients pay considerable outlays for private research reports. They pay not just because of the worthiness of information, but because of its timeliness. Quick and qualified futurists are brilliant gems in every company.

In my view, the budgetary value of a foresight output depends on its originality, quality and timeliness, but its intellectual value and the contribution that it will make to building better corporate futures may not be determined by such means of assessment easily.



References

Burnett, J. (2008). Core concepts of marketing. Zurich: Jacobs Foundation.

Fraenkel, J. R. (2009). How to design and evaluate research in education. New York, NY: McGraw-Hill.

Gordon, A. (2009). Future savvy: Identifying trends to make better decisions, manage uncertainty, and profit from change. New York: American Management Association.

Verzuh, E. (2005). The fast forward MBA in project management. Hoboken, New Jersey: John Wiley & Sons.

Williams, F., & Monge, P. (2001). Reasoning with statistics: How to read quantitative research (5th ed.). Fort Worth: Harcourt College.

Tags:  budget  foresight  futurist 

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What Makes a Futurist “Good”?

Posted By Administration, Monday, February 23, 2015
Updated: Saturday, February 23, 2019

Jason Swanson  shares his thoughts with us about “What Makes a Futurist Good?” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

A few weeks ago I had the good pleasure of hosting my friend Jacob for a visit. Jacob is a quantum physicist and research group leader at the Quantum Network, making him one of the few people whose job might take more explaining than mine when asked what I do.

Over the course of his visit, he asked me a question that has stuck with me. The question was a simple one; what makes a futurist “good”? The question, while on the surface seemed straight forward, however the more I sought an answer, the more lost I became.

We might judge a good futurist by credentials and training. Have they learned methods for looking at the future from an academic institution? Did they take a seminar or some manner of formal training? This training might have some manner of correlation with a “good” futurist, but the credentials themselves are third party verification of certain competencies in methods that a futurist might employ. Even more problematic is that many enter the field from other industries, with years of outside knowledge and expertise and little or no formal training or “futures” credentials, yet put out well-regarded work.

With the idea of credentials and training no longer an option for figuring out who might be good, I started to think about output. Is it possible to objectively judge a forecast? Could one be a poor futurist but an excellent writer and create vivid images of the future? Sure. Could one be great at mastering the methods in a futurist’s tool box but not articulate the images of the future? Certainly. There is also the issue of bias; we may favor a particular writing style, or image, or method, thus gravitating towards a piece of work over others based more so on style than on content.

Ultimately my line of thinking has led me to this; a good futurist is one that creates good forecasts, in whichever form they are presented. A good forecast is one in which action is taken. Thus, a good forecast could potentially be created by anyone, with any form of credentials. It could be articulated in any way. As long as a stakeholder takes action, it may be considered good. Admittedly this is a very simplistic view. As the field continues to work towards professionalizing, there may be a time when there will have to be some criteria for what makes a futurist “good”. There is no easy answer to this. That is the rub with trying to rate a futurist. At best we create a standard for what we view to be good work. At worst we risk narrowing the field and creating a status quo, creating groups that are “in” and “out”, good and bad. If we base being “good” on forecasts that produce action, how do we define action? Is it creating actionable strategies? What about simply asking better questions about the future?

What makes a futurist “good” to you? Is it even possible to objectively call someone a good futurist?

Tags:  futurist  good  work 

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Benefits of Big Data – predictions vs. foresight

Posted By Administration, Monday, February 16, 2015
Updated: Saturday, February 23, 2019

Julian Valkieser  shares his thoughts with us about “Benefits of Big Data” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my last articles, I have already mentioned the power of Big Data. My blog colleague Jason adopted it and expressed his own thoughts. In his last article, he has shown wonderfully how technology has already overturned business models and efficiency in other sectors and renewed them. In comparison to this, it could happen in the area of futurist and industry’s foresight as well.

Now, there are foresight methods that work well or best with uncertainty. Indeed, Delphi-Interviews are planned preciously, e.g. interviewees are pre-selected. But this does not mean that the statements can be processed for hard facts of future reality. And, they should not. That’s the exciting thing about scenarios. They give a way to stimulate the imagination and to derive recommendations for action.

But again, you try to keep the “cone of plausibility” as narrow as possible. (See Jason’s blog). You are looking for certain experts. You force certain issues. This is done in order to build the scenario reasonably.

Now you can imagine how neutral subjective responses and subjective questions are. Anyone who read “Thinking Fast and Slow“ from Daniel Kahneman knows what I mean. And right here data comes into play. Information could passively express motives and interests of groups. I have already indicated this in my last article.

In this article, I already referred to the fact that you can only get the most out of Big Data, if one applies the prediction to a trigger event. One extracts motives and interests out of big data for one or more so-called, trigger events. These are events that can be relatively easily predicted in the near future based on data, because the circumstances are (should be) less complex. Based on these trigger events you can create a scenario. In principle, this is nothing new. Just the basic information is extracted out of big data instead of interviews and subjective insights.

Let’s take an example. A major mobile phone company has 50 million customers. Each customer has a phone and moves every day with this turned-on phone – in this case between different radio towers (See Triangulation). Let’s suppose further that the company receives 20-100 motion information’s by any customer. Provided the company may cache this information for a longer period of time, the result is a huge amount of data information, how people move, how long they stay in which locations, etc. Of course, each individual could now be afraid of privacy. But the individual is not of interest. It’s about the mass.

Imagine what you can do with this information now available. Road offices could optimize the logistics. Infrastructure projects could be optimized. Where should the new stadium be built? How is the highway to be calculated? How many trains must be set on this track?

In a rising urban environment, where sheer masses of people are moving, all these data are exciting as the basis for trigger events and scenarios.

And finally, I have another wonderful example for these ideas. Eric Fischer has evaluated geo-tagging data from photo cameras. He compared where locals and tourists take pictures in certain cities in the world and displayed this information on maps.

Tags:  big data  foresight  scenarios 

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Agency and imagination

Posted By Administration, Tuesday, February 10, 2015
Updated: Saturday, February 23, 2019

Bridgette Engeler Newbury  shares her thoughts with us about “the empires of mind” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

The empires of the future are the empires of the mind. ~Winston Churchill

Design and its outputs may reflect our individual and collective imaginations, but design is first and foremost a philosophy, based on a system of values, which seeks to solve problems. What are we creating? Why and for whom? These are questions, in no particular order, to which answers are manifested tangibly and most often in the form of a new product or service or organisational or business model.

Designers are practical agents of visual imagination, both anticipating futures and creating the sensory blueprints for the objects and experiences to come. The images, objects and technologies that surround us are rich with desirable images and symbolism; they’re powerful and persuasive, well-crafted and covetable, and often very well made. Designers can turn abstract futures-oriented concepts and ideals into visible or tangible form. Designers and design thinkers are agents in articulating futures, and therefore have individual and collective agency for humanity more broadly to sense, see and negotiate (or refuse) the transition.

Not all design is good (by any definition). So I’m contemplating what something like long-range design – ‘design with foresight’ – could be. AKA prospective design, it’s what I suggest is design that emerges when futures thinking and design thinking are used together, in a structured manner, to develop an idea that may not exist until sometime in a long-range future, or which will not be to the detriment of preferred futures.

  • Prospective design relies not on technology but on human interaction, deep thought and reflection
  • Prospective design embraces design’s potential to shape conversations, to (re-)frame problems, and to drive participation by understanding the needs and resources of all the differing functions in a consuming world
  • Prospective design is inherently good and not just because it’s always intentional and sociological
  • Prospective design does not produce novelty for the sake of novelty
  • Prospective design makes a product, service or organisation truly useful. Things are purchased, used, adopted and recommended because they serve a purpose and deliver value: value that improves people’s lives and makes them happier. This is the real measurable value people desire. Prospective design optimises the feelings and experiences of customers, while being responsible to community, planet and what is yet to come
  • Prospective design satisfies form and aesthetics, without compromising usage or need. Designed artefacts do not simply fulfil desire or need; they can actualise and reflect wants.
  • The look and feel of something, its materiality and substance, ethereality and intangibility, ephemera and sensation are all part of the feelings it arouses – which are in turn a strategic and integral part of the user’s realisation of value
  • Prospective design helps us to make sense of things. ‘Value’ (as perceived by the user) creates engagement. Good design creates curiosity and engages its audience in meaningful, valuable ways. It also conveys the intentions and trustworthiness of the organisation behind the design and helps people make informed choices
  • Prospective design can be a catalyst or guide, a means for people to create their unique and evolving stories, and their own individual meaning
  • Prospective design is durable and enduring. It increases the value of something over time. It remains relevant as its users, community or culture develops and matures. It may not exert influence or manipulate buyers, but it often takes risks to provoke worthwhile change.

Prospective design is concerned with context and environment. It’s unobtrusive and meaningful, enhancing people’s experience; it’s not about dominating strategic decisions. Prospective design draws together futures thinking with the principles and practices of design to frame a strategic conversation without an elitist position. Design may be part of a complex, living ecosystem, but prospective design can strive to be a positive agent of transformation that contributes to better-being.

Tags:  design  imagination  value 

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To facilitate better futures?

Posted By Administration, Monday, February 2, 2015
Updated: Saturday, February 23, 2019

Sandra Geitz  shares her thoughts with us about the possibility of “facilitating better futures” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

 

“We cannot solve our problems with the same thinking we used when we created them.”

  – Albert Einstein

 

How to facilitate teams for generating and enacting?

 

There seems to be a growing acceptance that diverse experiences and perspectives correlate with better outcomes: greater financial performance results, improved risk management, greater innovation and employee satisfaction and engagement, see the recent McKinsey article, Why diversity matters.

 

And yet, neither diversity of experience, gender, ethnicity, gender, nor age guarantees that the best ideas will be shared, genuinely heard nor be accepted and implemented on their merits. In many recent experiences, I have seen culturally diverse teams conforming to expectations of the loudest, most senior team-member just like the best homogenous teams. Is this your experience, too?

 

What is really happening to the diverse potential of rich ideas?

 

For a long time, I’ve been interested in processes and methods that generate ideas and solve complex problems. I’m particularly interested in understanding ways to facilitate and encourage teams to examine issues or problems with an open mind, and help them reach beyond their own cognitive biases.

 

So, a recent Stowe Boyd blog grabbed my attention, Phil Gilbert on sidestepping cognitive biases in group design activities: When you give voice to more people, the best ideas win, not the loudest ones. Interesting. Boyd explained two key ways that information processing is disrupted by a team’s culture and psychology. Firstly, effective ideation can be impaired by sharedness bias:

 

Groups communicate predominantly about information, which all or most group members share before entering the discussion, and neglect unshared information, which only one or few members have initially. …  group members individually judge shared information as more important, relevant, accurate, and influential than unshared information. This bias seems to have two reasons: First, shared information can be confirmed by more than one group member. Second, individuals evaluate their own information as more valid than information from other members. Thus, unshared information, even if mentioned in the discussion, is not seriously considered by other group members and therefore has less impact on the final decision than shared information.

 

Hence, with established sources of team knowledge and shared experience, groups tend to discuss, share and privileged information that is held in common. Novelty is rarely introduced within team meetings themselves. New ideas tend to be socialised with team members prior to any decision-making in meetings.

The second is preference bias:

Even when all information necessary to identify the correct solution is exchanged during discussion, individual group members often stick to their initially preferred wrong solution. People bias their information processing to favor an initially preferred alternative. Other studies show the same phenomenon at the group level: Group decisions can often be predicted by the initial preferences of its members. If a majority favors a certain alternative before the discussion, the group seldom decides to chose another alternative. Thus, frequently, group discussions are superfluous, and groups would be better off using a decision shortcut like an immediate vote or averaging procedure.

We preference our own preconceived views and information over others. In spite of new valid information, we tend to conform to initial opinions we have of an issue. We tend to be closed to other possibilities, rarely are we convinced of others’ arguments, and we privilege our own ‘objectivity’. This sounds familiar…

Boyd interviewed Phil Gilbert, IBM general manager of design, on how he applies design thinking, diversity and inclusion to team product ideation. Gilbert believes that the major issue to generating future possibilities, is exposing everybody’s ideas to the whole team: both encouraging all to contribute and hearing each idea.

At IBM, team workshops are designed to include a wide diversity of experience and backgrounds. Gilbert’s method is sticky notes and silence, as depicted in the diagram above. Everyone present is encouraged to write down all their ideas on separate sticky notes and post them on a wall, without judgement, comment or self-censure. Team leader(s) sort, group and arrange like ideas on the wall, while everyone observes and reflects in silence. Then, individuals may leave the room to discuss ideas, in person, by phone or by a team social media tool. The group returns after an agreed time brainstorming and socialising their ideas (minutes, hours, or days). Gilbert explains that the process usually generates a few dozen new ideas.

Phil Gilbert’s approach also aligns with Alex Pentland’s research, Social Physics, that I discussed in earlier post. Peak idea flow occurs in teams that iteratively work as individual’s generating novelty and team collaborators discussing, building and socialising these new ideas into practice, summarised in this diagram:

 

“Any useful idea about the futures should appear to be ridiculous.”  – Jim Dator

 

Profound words. Futures requires a healthy amount of personal resilience in ourselves. What of the teams that are thinking about their future? Have we designed our methods so that participants, as individuals and teams, are able to bypass cultural and psychological biases to see and accept issues and information anew.

 

How can we promote genuine exploration, engagement and reflection with new ideas?

How can we design experiences that suspend judgement, cynicism and criticism?

How can we facilitate better futures?

Tags:  diversity  future  idea 

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Is a Ranking System Feasible for Futurists?

Posted By Administration, Monday, January 26, 2015
Updated: Saturday, February 23, 2019

Alireza Hejazi shares his thoughts with us about the feasibility of establishing a "Ranking System” for Futurists in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

Attending an international exhibition on a marketing mission recently, I was asked to score service and product providers there and nominate my preferred candidate at that expo. After reviewing many pavilions, I made up my top ten list and scored them according to my checklist. I voted for a European company that met most of my desired factors of presenting their services in a client-friendly manner. On the final day, my nominee won the cup not just because of my vote, but due to many other votes that other evaluators had given in favor of them. What looked nice in my eyes was also fine in the eyes of others. I asked myself whether such scorings and rankings could be also made for professional futurists. The idea made me write this blog post.

I think that ranking the futurists can be a challenging task due to a number of reasons. First of all, there is no universally agreed system of scoring for futurists. Secondly, futurists normally come from different fields of expertise and they cannot be ranked similarly. And thirdly, ranking the futurists may be done validly by institutions that might be authorized for such rankings. I would like to share some of my assumptions and questions about the feasibility of such a scoring system in this post. I should remind that the goal of ranking is not to drive low scores away but to claim them as candidates of high rank through professional development.

The first question that comes to mind is this: “What is the benefit of ranking?” or “Why should the futurists be ranked?” In my view, futurists can benefit easily from their own personal branding without ranking; but if they are going to be entitled to the merits of professional recognition, they should be identified by the degree of excellence they provide with their services. In other words, ranking is a means of qualification in terms of knowledge, skill and the quality of service that professional futurists provide for their clients. In my view, professional recognition and related merits logically belong to those who provide high-quality foresight outputs. Fortunately, the APF’s Most Significant Futures Works program has been serving this idea since 2013.

Another question that will arise concerning a ranking system is this: “Can the futurists be ranked according to their academic degrees, the number of their published or referenced works, the number of their students, the efficiency of methods and techniques they have developed or the number of their daily Tweets?” or “Should they be judged according to the values they bring to their own nations and the entire humanity?” Conventional methods of ranking may sound useful for scoring the futurists who live in societies where thinking and acting about the future is respectful, but how about futurists who live in regions where futurism is nonsense in the eyes of local decision-makers who are positioned based on aristocracy, not a meritocracy?

Any conceivable scoring system for futurists should recognize the fact that futurists are various in their talents and capabilities. While many of them are competent in applying qualitative methods of research, there are some who are brilliant in using quantitative methods of inquiry. Many futurists are good communicators and some of them are skillful in communicating what is ahead in innovative ways. Most of them are open-minded and lifelong learners, but what makes them valuable for themselves and the societies they serve? What are the social impacts of futurists and how can a ranking system measure them in national and international scales?

The first step that should be taken in this line is to provide a clear and detailed description of the knowledge, skills and attributes expected of a competent futurist or foresight practitioner. A competency framework like what is developed by the International Manipulative Physical Therapy Federation (Rushton, 2013) can be also made for professional futurists based on these components:

(1) Dimensions: The dimensions are the major functions for foresight performance at postgraduate level. The functioning of strategic foresight and futures studies graduates should be evaluated after their graduation in practice.

(2) Competencies: The competencies are the components of each dimension stated as a performance outcome. The competencies linked to a dimension indicate the standardized requirements to enable a professional futurist to demonstrate each major function for performance at postgraduate level.

Competencies can be divided into competencies related to knowledge, skills, and attributes.

(a) Knowledge: Encompasses the theoretical and practical understanding, use of evidence, principles, and procedures.

(b) Skills: Encompasses the cognitive, psychomotor and social skills needed to carry out pre-determined actions.

(c) Attributes: Encompasses the personal qualities, characteristics, and behavior in relation to the environment.

There are other concerns in the workplace that should be addressed. Research shows that ranking systems are often viewed negatively by people. However, many major corporations such as General Electric (GE), Intel, and Yahoo! use relative rankings and believe in their advantages. For example, Jack Welch, the former CEO of General Electric, instituted a forced ranking system at GE in which 20% of employees would be in the top category, 70% would be in the middle, and 10% would be at the bottom rank. Employees who were repeatedly ranked at the lowest rank would be terminated (Ryan, 2007). Corporate futurists or foresight practitioners might be ranked internally within the corporations they work, but how should they be ranked externally in a larger scale within the global community of futurists?

Relative rankings may create a culture of performance at corporation level by making it clear that low performance is not tolerated, but how about rankings that might be made by scoring futurists at a professional level? Should a low scorer be expelled out of international futurist communities? Or should he/she be prohibited from practicing the foresight profession without receiving required certifications? More importantly, what are the potential downsides to such rankings? Should a ranking encourage the futurists to upgrade their academic education in foresight and develop their professional skills, or conversely discourage them and deprive them from professional recognition?

There are many other questions and assumptions like what are mentioned above that make a long list. They highlight a special attention that should be paid to all the details of any effort that would be likely made towards ranking the futurists. Until the completion of a standardized ranking system, conducting self-other rating agreement surveys can be the easiest way to capture a better understanding of futurists’ standing in companies and organizations they serve.



References

Rushton, A. (2013). Educational Standards in Orthopaedic Manipulative Therapy, Part A: Educational Standards. International Manipulative Physical Therapy Federation.

Ryan, L. (2007, January 17). Coping with performance-review anxiety. Business Week Online, 6.

Tags:  foresight  futurist  professionalization 

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Towards Disintermediation

Posted By Administration, Monday, January 12, 2015
Updated: Saturday, February 23, 2019

Jason Swanson shares his thoughts with us about the “Disintermediation” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my post last month I explored a few ideas of how big data might affect the futures field in terms of both practice and business. This post will continue that exploration, this time focusing more on the potential implications for the business side of things as big data tools such as R and Python come to fore, as well as academic programs such Udacity’s nanodegree in Data Analysis come to market.

I would like to explore the implications of big data on the futures field using a lens of scarcity, abundance, and disintermediation. There are quite a few examples of industries that have experienced line of development. The retail industry comes to mind with consumers once relegated to seemingly few retail outlets, then an abundance of options, and now retail is becoming increasingly disintermediated as the internet has allowed for increased opportunities for peer to peer transactions. The music industry is has followed this path, and even public education here in the United States is dealing the changes that a system and its stakeholders have to contend with as it moves from the abundance period in terms of information access into disintermediation.

Apply this lens to the futures field, one might make the argument that thinking about the future is indeed disintermediated. Everyone person alive thinks about the future in some capacity. It is part and parcel of living. When we define the futures field as the professional practice of studying the future using a defined methodology, an argument can be made that the field is still at the scarcity stage. The number of professional futurists is tiny in comparison to other professions. I can’t help but reflect on how many times I have given my futurist elevator speech during my brief time in this profession whenever I am asked what it is I do.

What might it take to move the futures field from scarcity to abundance or even all the way to disintermediation? If we are to consider the examples of retail, music, and education, the key additive was technology, particularly when moving from the abundance stage to the disintermediation phase. Concerning the futures field, there are signs that the field may be beginning to move from the scarcity stage to the abundance stage. Among those signals are the small but growing numbers of academic programs offering courses and degrees on the topic of foresight, growing interest in existing foresight courses and programs, and even growing interest in the application of foresight methodology in disciplines such as design.

If these signals might signify a slow move to the abundance stage for the foresight field, what might that look like? Let’s use the retail industry at the abundance stage as an example once more. At the stage of abundance, the consumer had a high degree of choices in retail outlets, with a high degree of choice in terms of items, and many price points for those items. The foresight field at the abundance stage may look similar to the retail industry; a high degree of choice in terms of foresight services, many practitioners, an increase in organizational or internal futurists, more choices in training programs, in short, more. Of course a critical uncertainty here is will there be demand to account for all this “more” beyond simply an interest in methods.

As I mentioned in my last post, big data has the potential to change our practice and our industry. As big data tools continue to simplify and improve, there will be an effect on the futures field. One of those effects might be speeding futures through the abundance stage into disintermediation. As these tools develop, become easier to use and more accurate, the by product may be a growing interest in what’s next. Using predictive analytics and modeling to give a client, company, or organization accurate peaks into the future could push the field into adopting these tools (Again, please check out Julian’s blog post for an excellent use case). The move towards incorporating this type of data into our work may have the effect of “legitimizing” the work in the eyes of clients who in the past may have been standoffish, or who may shy away from more qualitative pursuits.

As big data tools continue to develop over time, these tools have the potential to be a factor in moving the futures field into the disintermediation phase. We can expect over time that big data tools, like nearly all forms of technology, will become cheaper and easier to use. As the ease of use increases and price points fall there is the potential for new users. Given enough time, a user might be able to utilize these tools through apps on a phone or other personal devices. A person might be able to one day run predictive models with the same ease of sending a text message. This sort of breakthrough could be compared to having a futurist in your pocket, crunching massive sets of data and giving highly plausible scenarios back to the end user. At this point the futures field may be considered disintermediated, with users being able to directly use methods and tools in developing images of what the future may be like.

Could big data and big data tools be a catalyst to push the futures field towards disintermediation?

Tags:  disintermediation  foresight  futurist 

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R and Python for the evaluation of trigger events

Posted By Administration, Monday, January 5, 2015
Updated: Saturday, February 23, 2019

Julian Valkieser shares his thoughts with us about the “R and Python” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my last article, I referred to the importance of Big Data as it has become more and more important for decisions in medium-term periods. Big Data is an often used buzzword – especially by large corporations and middle management levels.

I have mentioned R programming, claiming that everyone in the area of Foresight should learn it in the near future. Now we have to add the programming language Python. For people with a lot of self-discipline I would like to recommend a Google search and a good book. For myself, I have gone the way of Coursera, a Massive Open Online Course (MOOC), which I can highly recommend.

It is not so much about being a programmer. After all, it is not our field of interest. Rather, it’s about using these programming languages to play with a large amount of data so that you can develop an understanding of the benefits. Of course, there are also tools that require no programming skills. Maybe you have heard of NeuroBayes or RapidMiner? But someone who wants to sell a car should also know how a car works.

Especially the tool RapidMiner shows very clearly what makes this kind of tools and what Big Data is all about: The visual presentation or summary of large amounts of data. Only a good representation and summary can be a benefit from Big Data.

Beautiful examples of where data analysis for short-term forecasts are used are as follows:

http://edition.cnn.com/2012/07/09/tech/innovation/police-tech/

http://www.popsci.com/science/article/2011-10/santa-cruz-experiment

http://www.skyhookwireless.com

http://firstmonday.org/article/view/3663/3040

http://www.slate.com/blogs/how_not_to_be_wrong/2014/06/09/big_data_what_s_even_creepier_than_target_guessing_that_you_re_pregnant.html

Of course, these examples are not transferable or all reality based. But – to get back to the metaphor of the car – in terms of data analysis, we find ourselves status quo in the early days of the Ford Model T.

There are certainly countless more of such examples. All more or less well understood and scientifically correct. Another example: Nate Silver Predicting an election.

One thing you can say now: Forecasts based in the past are less reliable, or partially obsolete, for example, if you are emanating from seasonal recurring events, such as the flu or the purchase of heaters in winter time. If you can analyze data in terms of motives and interests (See also Computing and Intuiting futures from Sandra Geitz), then it gains a different picture. Motives and interests provide information representing “we are going to…,”, situations such as “I’ll buy a car if I get a raise.”

This could be transmitted at the macro level, e.g. if the Democrats are elected in 2020, they will finally put through a specific law, because we all know that they are still working on this. It is very likely that they will do it if external circumstances allow it. This is when Big Data comes into play. The Democrats re-election depends in turn on the people’s interests which can be reflected, e.g. on Google queries.

All of this relates only to medium-term time horizons and Foresight is less about making a prediction, rather likedepicting a scenario. However, a scenario could be represented more closely or exactly, as already hinted by Jason with his, “A Shrinking Cone of Plausibility” blog. Big Data could serve to draw the “so called trigger events” in this case to create scenarios based on these trigger events. For example: The next US president election, Jason used a Cone of Plausibility in a familiar example. I like this approach. But for me, Big Data is used for the representation of starting points or trigger events with which you can create scenarios in the distant future.

Existing Scenarios are mostly based on the current day or status quo. At this point, let’s go back to the Big Data analysis where Democrats will be re-elected. Based on this forecast with a certain probability we can build a scenario that is not mirrored from today’s point of view, but from the status of the so-called trigger event that a particular party is elected. Of course, this should not be the only factor for our scenario. Other trigger events could be used such as other interests and motives. What are the media interests? In what way have the most protests been expressed? Which governments were overthrown and which companies enjoy continuously high investments in the market? How have prices developed for this and that? This information be more precisely reflected in the near future with Big Data analytics. Of course, not 100% accurately – but more accurately than if not used, or only subjectively evaluated.

THE RECOMMENDATION
Try to engage in R and Python. Look at tools above with which you can analyze data and represent it visually, even without programming skills. The former and the latter tend to be the same.

A pretty manageable article on R and Python in terms of big data is from the DC data community.

But finally – why R and Python? R is primarily used for visual analysis of structured data sets, such as you already know from an Excel spreadsheet. Corresponding programming packages could complement R. Python is a little more powerful, albeit with the appropriate packages the functionality of both languages overlap. The scene will still argue which tool is more appropriate. Using Python for the analysis of texts are getting really exciting. Essentially, it is mostly a matter of counting words. How often is a corresponding keyword mentioned in a particular text or even more interesting, how often is it mentioned in a specific timetable in the whole web? Since most of the texts can be classified according to one author, and date etc., it is exciting here to see who mentioned what, when, where and why. And that’s what makes the data analysis so exciting: text analysis. As mentioned above, interests and motives are the valuable insights as they represent a target of individuals and groups. I might tend to buy more bio in the future or try to travel without a car? Of course, most of us won’t write it down digitally. But who else is active in clubs, google-searching, mailing and shopping online? It’s all about your interests!

Have a nice easy entry case in R and Python offered by Beautiful Data Blog.

Tags:  foresight  future  Python 

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Looking at next year’s list

Posted By Administration, Monday, December 29, 2014
Updated: Saturday, February 23, 2019

Bridgette Engeler Newbury  shares her thoughts with us about the “future possibilities” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

It’s that time of year. Celebrations and traditions. Endings and beginnings. Promises and provocations. Reflections and resolutions. And now that the tinsel, incandescent holly and Santa-shaped shortbread are on sale, the flurry of ‘top ten’ lists will appear as quickly as the hot cross buns do (across supermarkets in the UK and Australia at least).

As Jim Carroll says here it’s relatively easy to extrapolate current trends into a ‘Top Ten for 2015’; it’s quite a different matter to look further ahead, as he does to 2025.

Some of those lists will posit that we’re in an era of innovation, entrepreneurship and technology to transform cities, economies and lives. Spurred on by wearables, rapid urbanisation, smart cities and rising popular demand for access to high-quality (and sometimes sustainable) infrastructure, it all leads to seemingly ‘good’ growth that is assumed to follow globally.

So I want to highlight Mashable’s list of notable innovations in 2014.

Few of the innovations that improved the world in 2014 will make onto the top tens for greatness in 2015 or beyond, and only a couple might be considered trend-setters. Why, I wonder? Compare it to a list of tech predictions like this one – just who are the incredible innovations on this list intended for? What worldview or model of subjectivity is inscribed in the scenarios and technologies offered by the developers of such marvellous wearables and other remarkable tech wizardry? And who stands to benefit? When you compare this with the Mashable list, it’s pretty obvious that most espouse a pronounced way of thinking about the world and civil society, with rather limited implications for people, planet and participation.

It is one thing to reinforce the beliefs, value systems and infrastructures that underpin particular ways of life; quite another to expound the importance of technologies that privilege a few when reliable access to electricity, clean drinking water, somewhere safe to sleep or sanitary facilities are not part of everyday life for too many. I’m not denying the need for or value of innovation, invention or experimentation (that Mashable list embraces all of those) but I am questioning the way value and need are prioritised, and by whom, based on what, and the kinds of futures that are being shaped by the infrastructure, innovation and technology these choices deliver.

As Andy Hines notes in his latest blog, maybe we could take some time to explore the ‘why’ of values, not just the ‘what’. Because there’s more to life in 2015 than networked information technology. Lasting change has to come from within, whether it’s individual, community or organisation. It won’t come from an app alone or something we plug in.

Tags:  future  technology  value 

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Computing / Intuiting futures?

Posted By Administration, Monday, December 22, 2014
Updated: Saturday, February 23, 2019

Sandra Geitz shares her thoughts with us about “intuiting futures” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

 

Do you synthesise opinions and judgements to develop potential futures?
Alternatively, do you conduct wide-ranging data analysis for potential futures?


Recently I’ve been reflecting upon the various ways it is possible to source potential views about our futures. How there are multitudes of opinions and judgements that contest what are valid and plausible futures. How various sets of data are either universally relevant, hotly debated or ignored, depending on one’s interest of the specific issue studied. Is it ever possible to completely separate facts and opinion from one another?

This led to the diagram below, which is a synthesis of Sohail Inayatullah’s Causal Layered Analysis: litany, facts, values and myth, discussed in an earlier post, and Otto Scharmer’s Theory U process: downloading (judgement), open mind (analysis), open heart (connection), open will (insight).

Judging issues increasingly involves contested opinions, ranging from expert judgements to social media flaming. Analysis may include or exclude publicly and privately available data, especially as huge volumes of big-data are generated. How we view the world, our values and deep stories, shape which data we view as valid and relevant to an issue. Similarly, others with different perspectives will connect with alternate data and opinions for this issue. Hence, the preference for a depth method like Causal layered Analysis (CLA) in contested views of our futures. And, what issues are not contested nowadays…

Rarely, are judgements or analysis sufficient alone. Underlying assumptions, biases, or beliefs which can influence or determine either of these inputs remain hidden and unknown. Even, combining judgement and analysis, gives a similar shallow and limited future view.

Connecting with the people, understanding their outlook and values, generates a critical view of the input data and opinions. This illuminates what parts may have been included or excluded from final result. In this way, greater depth and breadth to potential future options may be perceived, enabling one to imagine interactions and potential responses by appreciating the values of each participant.

Developing an insight into the deep stories or myths of each participant, can provide the richest potential futures options. The effort to distil and synthesise participant’s values into succinct story headlines, appears to make them memorable. And then, quite often, after some time germinating, ruminating… combinations of these insights, and interactions form new stories, resolutions and potential futures… In this way, Causal Layered Analysis can be used as a prospective method, beyond analysis.

What are your experiences using judgement, data, values and stories for futures?

Does this compute or intuit with your experience?

Tags:  analysis  future  judgement 

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