Do you know your CI strategy when it comes to technology?
Terry Thiele of the Lubrizol Corporation and Ed Allison of Compelligence engaged in a lively debate surrounding tactical vs strategic approaches in CI and the impact of technology on the profession.
In light of technological advances, will CI remain a viable function within our organizations?
Discussion covered how information systems and technology will impact ourselves (our role) and the industry overall.
Terry Thiele’s view:
“This presentation is intended to be provocative, and one delivered by a curmudgeon.”
The thesis: Everything you have learned about intel is going to have to change. The good news is, CI professionals will still be required.
During the industrial revolution, production sat in an economic sphere. Towns were spread out, and products were not far-reaching. With maturation of the industrial revolution, the economic sphere grew.
In the second industrial revolution (today) we now have a global economic sphere.
Given that, how are we to do a profile for mass produced goods? In judging economic performance, we look at number of sites, customers, competitors, chain links, SKUs, and product cycle times.
The third industrial revolution will be impacted by energy. Steam engine and combustible engine arrivals yielded larger capacity to produce. Consider that, on average, given energy output, we currently have the equivalent of 1,300 laborers per person per day meeting our energy demands.
“Cheap, ubiquitous off grid energy is the future,” noted Thiele.
Other advancements that will prove influential in the next revolution include:
- 3D printing creates things from the inside out rather than outside in, and will impact production. It enables the impossible—it has already been declared the third revolution by some.
- Nanotechnology applies to nearly everything and opportunity for scale with it is rapidly increasing.
- Automation and robotics.
- New materials: Materials will change the future of manufacturing—plastics, crystals, thermoelectrics fungal foam and others—a broad array of new material will be used in new manufacturing processes.
- The Internet of Things creates a circumstance of many sensors and their adoption in a variety of products—and bring data analysis about in a way leading to knowledge management.
- Individually made to mass produced evolution will take us back to “individually made” once again. Manufacturers assess their vulnerability to home crafted items.
- The emergence of digital tools for design manufacturing and rise of factory for hire has important implications. Mass customization eliminates need for sites, customers, chain links, product cycles—how is analysis completed when manufacturers produce specialized and customized products for customers? As an example, consider how military intelligence has changed as individual terrorists are at the center of warfare, as opposed to armies.
The rate of change is accelerating; imagine what change might look like.
The analyst will be challenged in that current tools used to collect data will differ; a fracturing of competitive groupings will change and technology will not be able to bridge the gap. Therefore, CI teams will need to do more “on the ground” work given the multiplicity of competition.
The rate of change is going to accelerate at such a pace that no one will be able to catch up with it; and a larger number of “agents” will require a new approach.
Consequently, the CI analyst will be in greater demand, both to meet growing need for multiplying products and services but also because a “human touch” will be sought in technological onslaught.
Ed Allison’s view:
The thesis: CI was about knowing the unknown. Perhaps now it is about acting faster than others on the same available information.
“Technological Trend’s Influence on Competitive Intelligence and Analysts”
Allison led CI teams in military and then in corporations—his constraints are that this is his experience.
The path to increasing revenue is to take share from competitors, so a focus on sales is imperative.
CI is becoming less important; how is “intelligence” making us dumb? Traditional constructs no longer work for us.
Deals support revenue
Sales interaction with CI is an unusual collaboration, and one that has been unfortunately overlooked. The prevailing sense is that working with the sales department is difficult because staffers know everything, and sales staff do not want CI help.
Secondly, granularity of data is challenging and frequency and collection of data is sparse.
In reality, the sales staff can be a source of data that is currently untapped, says Allison. They are a low-cost, easy access data source in the intel world that is frequently overlooked. One challenge is how to collect information at scale with a large sales force?
Allison says CI is getting an “F” due to the CI cycle—the process takes too long, and includes many steps:
- Planning and direction
- Information processing and storage
- Analysis and reporting
“The best intelligence in the world is of little use if it is not presented in a manner that makes it credible, compelling and relevant for senior executives.”
How did people in CI get lucky in the past? What worked?
Allison’s department was created to help sales—his experience was he never had to fight to see executives, and he tapped the sales team. He had a unique piece of information (technical analysis) and tested it against products. With it, the sales team could develop very distinct and granular sales points.
What are you offering that is absolutely unique to the process for your sales group?
The next step was to engage with the sales group in impactful ways, measure it, and make determination of what was winning and what was losing. Trust gained in the company was “incredible” when sales staff demonstrated buy-in to an intelligence role.
Tactics of sales engagement can lead to strategic impact, based on internal analysis, and that become an intel model that grew from their own experiences.
Organizational competitive assessment
Allison says organizations of tomorrow need to utilize all sources of input including their own internal sensing. This introduces problems of dealing with bias.
Problems arose for Allison when the frequency of CI interaction was low, despite the success rate with those with whom they did interact. How to solve this frequency problem? Enter technology…CI staff had issue filling the increasing number of requests and went to systems and technology to meet needs.
Tactical vs strategic—where is growth opportunity?
Determine where opportunity exists to make value and assess your cost.
Debate surrounding CI value is common, and strategic value is more difficult to quantify; an integrated approach between determining tactical and strategic value is required.
- Free tools
- CI portals
- Enterprise tools
- Competitive sales tools
- Competitive platforms
What is your information system’s strategy for intelligence?
What is the role of the analyst as things become more automated? You do not want to invest in tools that do not get used; you need to find the tool to do what you want to accomplish.
When do you do win/loss research?
Ideally, at the close of the deal. In doing so, know how to illuminate internal bias of sales team: If price is the issue, the deal should be won. Speed in doing research early as the deal progresses avoids price as an excuse (bias). A system can do that.
Have you before thought “What is my IT strategy as intel leader?” It is an important consideration. To the industry and analyst this means pros can rely on technology assessments.
Thiele agreed technology is an integral part of CI work, but argues that the rate and degree of change as viewed from an economic standpoint will be so complex that it cannot all be automated. Technology will help with data gathering; people will be required to make sense of fractured data.
Allison rebutted that business is changing and the need for intel increases, but it is impossible to serve all intel needs with humans—we are too slow. The implication for CI is that we might not be the traditional analysts. Our jobs will change, and be very information-centric. And, technology “does not get sick and bring a bias.”
Who won the debate?
- Systems have bias that is built into code—humans can change bias, machines can’t.
- IT systems need to be taught by humans how to make decisions—it is a cognitive decision making process that still has human involvement. The plus is that machines make more accurate and consistent decisions.
- The premise of the presenters is too simplistic—neither presenter addressed reality for a regulated industry (pharmaceuticals) in which regulatory bias dictates whether or not a drug can come to market. There is an overlooked mosaic situation that varies by industry.
- Managers need to consider 3-5 year intelligence-specific strategies.
- As investments are made in technology, consider the long term consequences so you build intelligence systems that address changes that will be made. You must tailor systems to address changes you anticipate.
- Different entities within a company look at different data. Sales people are biased, external sources are objective, data sets are “wrong”. Until leaders are more focused in one corporate view, challenges persist.
- “Culture eats strategy for breakfast.”
- The situation is not as black and white as presenters describe—it depends on the industry. The analysis piece will play a big role.
The ultimate question brought forth today in this debate is: Are we even thinking about these technology issues?
Takeaways from the session
- Plea to invest more in technology well-received
- Forces of change are layered and will eventually come together (Thiele’s talk)
- CI people forget to do the CI on ourselves
- We need to educate internal people about how valuable we are
- Technology could be used as a crutch. Blind spots are still blind spots