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If uncertainty is a fact of life in business, it’s a glaring challenge when it comes to innovation and new product development. Innovation is critical for success in today’s fast-moving, competitive market — yet 96 percent of all innovations fail to return their cost of capital. Studies from leading research institutions show that companies are increasingly relying on innovation for growth, but they aren’t very good at it.
Big data has rightly been seen as a formidable tool to manage uncertainty in this challenging climate: Harvard Business Review has called big data a “management revolution,” and McKinsey published a report touting it as “the next frontier for innovation, competition and productivity.” Yet, innovators continue to struggle with big data and its implementation.
Long before the business world jumped on the term, big data was a big part of intelligence operations. In a sphere of many unknowns, special forces are taught to reduce risk and uncertainty through intelligence — to take data and map against what they know for sure, what they don’t know and decide if and when they have enough certainty to make a decision.
Here are four surprising connections between military intelligence and innovation and lessons product development teams can learn from military training.
1. Not-so secret ops
Despite popular belief, the bulk of data collected and analyzed in intelligence work isn’t top-secret intelligence sourced through covert operations. In actuality, the vast majority of military intelligence comes from mining open-source data. That is, the majority of data collected is publicly available information as inconspicuous as transactional records and real estate registries that are next to meaningless on their own but can be cross-referenced against other data to follow the flow of money — eventually all the way back to a smoking gun.
Open-source, or external, big data, can be just as powerful a resource for new product development teams seeking intelligence on unmet consumer needs, technology developments or even competitor movement. From disparate sources such as social media and patient forums to academic journals and competitor hiring patterns, so much critical intelligence can be found hidden out in the open. Yet what we primarily see today are so many companies that have kept their gaze and data strategy inward, buried in their own mass internal, proprietary data in search of clues, either unaware or overwhelmed by the prospect of tackling and connecting such disparate data.
2. Action-oriented intelligence
In the military, every piece of intelligence is centered around action. There must always be an action at the end of each process, planning, training and exercise — or it’s simply not part of the operation. This makes sense. What good is data if you can’t get your hands on any actual intelligence in time to make decisions? If missions were built around pulling in every single related data source intelligence officers could reach and trying to identify patterns from the bottom-up, they would be just as buried and slow-moving as many product managers are with data today.
In the innovation process, managers are often drowning in data and a cycle of indecision. While actionability is becoming a buzzword now, up until recently, most big data was treated as the domain of analytics teams pulling in mass amounts of data and working backward to try to understand what the data is telling them. It was also kept in silos where most decision makers on the ground had no access.
In other words, data science has remained in the proverbial lab and has not yet made actionable status for decision-makers. With these huge piles of data and no real connection to decision-making, it’s no surprise that most managers are disenchanted with big data, and 55 percent of big data projects have failed and remain unfinished.
3. Deciding how to decide
When you can never have 100 percent certainty, a top-down decision-making approach in military intelligence helps limit crippling distractions and decide whether you at least have enough certainty to move to the next level. For example, six months before an operation, intelligence might pick up weak signals from monitoring their sources that something was happening. Some change in pattern or spike in activity hinted at potential targets.
Time for decision one: Do they want to look more closely? If yes, they would move to the next stage and begin gathering more focused information to see what’s going on. At each stage, intelligence analysts would follow a pre-determined pattern of questions that would inform the next stage of gathering intelligence based on the response.
Decision-making can be similarly daunting and uncertain for new product development teams facing limited time, limited budget and diminishing resources. McKinsey Global Innovation Survey found that 57 percent of companies say they don’t have the resources to keep information up to date or distributed. Have they found an unmet need? Do their competitors have a head start on solving for it? Is there cutting-edge research or technology they can incorporate? When will it be available? Who can they partner with?
These are all uncertainties that could be met with data and analyzed by automated tools, freeing up product teams and innovators to interpret the findings and make better decisions quickly. Rather than leaving managers drowning in data, this is a highly actionable and strategic approach that ensures the intelligence they are collecting can translate into an actual decision. This improves efficiency by enabling decision-makers to add the greatest value in conducting their work.
4. Agility for innovation
With real time intelligence comes responsibility to move quickly — to change direction or even stop all-together if new information comes to light, no matter how much time or energy was put in planning your current course of action. Intelligence operations are designed around this agility. During an operation, there will always be a remote command in communication with the field command, a practice that has tremendously improved the success of operations by giving visibility to data feeds the field on the ground can’t access.
This principle can help businesses and decision makers play with intelligence in real time. By giving product innovators tools to visualize and connect the dots between sources and connect to the mission and action in a timely manner, they can set up their own war rooms to come back with instant decisions. They can change direction in the light of new information instead of chasing an outdated plan to completion or failure. Live dashboards and real-time monitoring are now a reality, thanks to new technologies.
Whether you’re in the military or launching a new product, making decisions in uncertain situations is difficult — you don’t know what you don’t know, and you don’t know how a new innovation will be accepted by the market until it happens. Companies have typically tried to overcome these challenges in innovation research by engaging in resource-heavy niche research and consulting projects, or by winging it based off of gut feel.
But the big-data age and new tools changes that reality. Military intelligence training taught me how data can lessen the margin of error throughout the process and instilled principles that can help companies navigate the uncertainty of new product development and break ahead of the competition.