Wayne Gretzky used to say, “I skate to where the puck is going to be.” This philosophy applies equally to government programs and policies which can only succeed if they reflect and understand where the players and environment are going to be.
An SME (small- and medium-size enterprises) investment tax credit program appears in a developed country and fails because the intended targets – the small and medium sized firms – do not use it. Worse, the program is used by larger companies spinning off smaller divisions. Billions of dollars later, the government closes down the program.
A department launches a clean-tech program, a rich loan guarantee for eligible clean technology investments. The program fails because no bank is willing to honour the loan guarantee. Net result, the key companies that the program was intended to help are now either bankrupt or have moved out of the country, and program money intended to help the sector grow is tied up for three years.
Meanwhile, a third program emerges to provide tax money for nanotechnology. However, the technology direction identified by the government is no longer commercially viable when the products are ready for commercialization. The government has encouraged and funded the wrong research.
All three cases are real, all involve governments, and all were preventable based on information and techniques readily available.
In the case of the nanotechnology program. it was designed to change investment behavior of companies toward the governments target’s. Unfortunately, in Wayne Gretzky’s terms, the program skated to where the puck was; that is, when the products emerged, the market had long since passed.
In the case of the other two programs, the governments did not understand the players and created policy instruments ill-suited to the intended stakeholders. The nanotechnology problem could have been solved with foresight and technical intelligence, the other two programs through an intelligence technique called profiling.
Foresight, intelligence and analytics collectively can help understand and manage the external environmental risks that can impact program success by using a policy dashboard, an early warning system for policymakers about whether design or criteria changes to the industrial policy are needed.
What follows is a simplified example of these three ideas.
The program: Government funding for eligible alternative fuel R&D and product development.
Program intent: Job creation through the development of commercially viable alternative fuel technology in the areas of windmill and cellulose-based biofuel.
Intended and measurable outcome: The creation of new jobs in the long-term within the targeted industry.
Challenge: Investment to commercialization in these industries can take upward of five years before the full job effects from commercialization occur. Further, the technology environment assumptions underlying the program can change during this time. How do we get early warning on program impact and environmental changes to help manage these risks? Do we have to wait three years for program review or five or more years for commercialization and job creation before we know if the program is going to work or has to be retooled? How do we manage the external environmental risks?
Dashboard development
When a government department or agency develops a program it should be based on a long-term analysis of the environment. With technology development taking five or more years, it is not unusual for a department to start with scenarios or a roadmap of the industry and look at what it will take for Canadian companies to reach the apex of the roadmap, to develop the technology needed for the future.
However, even with the best forecasts, foresight and initial intelligence, two external risks must be understood and addressed for a program to succeed:
1. Companies may not use the program, or they may use it in a way not intended; and
2. Changes in the environment may render the initial technology intent of the program no longer appropriate. For example, many companies were finally getting the bugs worked out for producing food-based biofuel just as governments started banning it.
Unfortunately, these environmental changes are beyond the control of government, but knowing about them early enough can allow governments to re-target the program. For example early warning on the food-based biofuel problem was used by one organization to retarget R&D toward non-food based biofuel. Those companies in the country that followed that approach are still in business – the others died.
Similarly, governments cannot control company uptake of a program, nor can they control if the company succeeds in the designated industrial policy area. But the earlier a government knows of problems, the sooner it can make necessary changes to better target the program.
Two intelligence concepts drive the development of the dashboard and program design: profiling and time-lining. Profiling refers to a detailed description of the stakeholders, including in-depth evaluation of their decision-making structures, strategies and goals. Time-lining involves sequentially laying out all activities that must occur before the end event is realized. For our R&D program, the end event is jobs. The timeline, however, would include all the events that must first occur. These events can be conceptualized as points within the timeline and measured to ensure that progression is occurring.
Mitigating the risks of unintended consequences
Where does a program/policy timeline start? In Canada, this often begins with the targeted companies making inquiries about the program to their local IRAP or regional economic development (e.g., ACOA) officers. Companies ask these sources about program access, program intent, terms, etc. Intelligence gained by integrating the information would reveal at an early stage whether companies in the targeted sector are inquiring about the program and if their questions indicate potential success – are companies asking the right questions?
The next step on the timeline is applications and their associated proposals. Data mining of these applications should reveal in real-time if the intended product development requests match what the government intends the program to support. Again, is the program going to be used for the intended purpose?
This early warning data comes from those departments and agencies that interface with the program’s intended targets and will indicate whether the program needs to be modified.
Moving along the timeline, after inquiry and proposals comes actual company activity. If the program is truly working and jobs and commercialization are to happen, what are the next steps? For one, international potential patents need to be filed. And if the R&D is truly leading to commercially viable products, companies should be making requests for commercialization funding, which can be tracked through companies themselves or through government commercialization granting programs. In this case, integrating information from multiple departments will be required as the department providing the R&D funding may not be the same as the department(s) providing commercialization funding.
In summary, the short-term and intermediate elements of this intelligence program, tied with the policy dashboard, provide early warning on potential program use and intermediate program outputs before the final program objective is realized.
Technology intelligence
The program started with an intended longer-term technology direction based on foresight studies, but as we all know, the world can change. Technologies can appear that make the program targets obsolete, societal attitudes can shift away from the program targets. This is the true “Wayne Gretzky” part of the dashboard as it looks to where the competitive and technological landscape is going to be.
How can this be systematized and managed in our policy dashboard? If social trends and attitudes are critical, then a technique called sentiment analysis can be used. For example, sentiment analysis of social networks would have identified significant negative discourse on food-based biofuel long before government and market authorities started turning against it.
Longer-term, technical trends can also be studied in a systematic manner. In this case, social media, supplemented by information from government employees, can be examined. For example, when employees attend industry events they gather intelligence that is used to update profiles on the technology roadmap analysis. Poster sessions present research that could be 10 years away from commercialization; presentations by companies on their current research programs and tradeshow displays of new products provide great information for testing the underlying program assumptions.
Conclusion
Existing information can be used to better understand and manage two types of external risks: appropriate use of the program by stakeholders and change to underlying technology assumptions. Fail on either and the program will be mis-targeted – the intended targets will not be skating to where the puck will be.
External risks must be understood, systematically tracked and managed if programs are going to succeed. Few programs and policies can succeed without managing this risk. Foresight, intelligence and business analytics, fed into a policy dashboard, can help address this risk.
