New Data can Revolutionize R&D
Updated: Jan 22, 2019
In 2007 Boeing introduced the 787 Dreamliner, which promised to revolutionize commercial aerospace. The aircraft could generate half a terabyte of data per flight, more than 100X that of its predecessor. This would generate new revenue for airlines from new applications and save millions by enabling predictive maintenance.
However, a survey in 2008 found that most airlines had disabled the Dreamliner's data generation. Why was this happening? According to airlines, the aircraft was generating too much data for them to use. They admitted the data COULD be useful, but the volume of data overwhelmed their ability to derive effective insights and make decisions.
Much like airlines 10 years ago, companies today fail to make data-driven IP management decisions, despite having access to more data than ever before.
Data paralysis drives billions in unnecessary IP costs
IP has quickly become companies’ most valuable asset. The right IP strategy can enable companies to generate new revenue, save millions of dollars in unnecessary IP costs, and create more innovative, differentiated offerings. And yet decisions to generate, manage, and deploy IP rely more on anecdotes than data. The lack of data in IP strategies has become so acute that the head of patents for a Fortune 50 company recently complained that: “No other asset but IP has so much value to the enterprise and such low use of data in decision making.”
Companies can now access a full digital record of every patent ever filed – over 100 million in total. That translates to billions of pages of text, drawings, and data points, all available for analysis. As Nate Silver aptly warns in The Signal and The Noise, “we face danger whenever information growth outpaces our understanding of how to process it.” Because few have figured out to process patent data into meaningful insights, IP strategies struggle to achieve meaningful top-line or bottom-line impact.
Data paralysis is compounded by the view – widely held among IP practitioners – that IP decisions are too complex and too ambiguous to solve with data. For anyone who has read a patent, this argument may seem plausible. Patent lawyers, brokers, and consultants have turned this ambiguity into a billion-dollar business. But as we’ll see, a data-driven approach to IP strategy can actually yield more accurate, repeatable results. Douglass Hubbard, inventor of applied information economics, devotes the entirety of How to Measure Anything to cases in which businesses solved intractable problems through simple measurement and modeling.
For IP, this applies to problems like assigning value, deciding where to invest, and finding other companies interested in buying or licensing IP. Once we accept that these problems CAN be modeled, the enormous amount of IP data available to us becomes increasingly valuable if we can develop the right models to process and understand the data.
Organizations that benchmark against IP metrics don’t just make better one-off decisions, they also improve their overall decision-making.
Two metrics that have the most significant impact on cost and revenue: IP Lifecycle Management and Commercialization.
IP Lifecycle Management: The hidden cost of IP
Few companies measure the cost of generating and maintaining IP. And although a properly managed IP portfolio can generate disproportionate revenue relative to its cost, a poorly managed portfolio quickly becomes a cost center. Take the example of a $5B technology company. The company, which we’ll call TechnologyCo, employs some of the world’s leading engineers, spends nearly $800M on R&D, but generates less than $3M from its IP portfolio. That statistic alone is staggering, but it becomes even more concerning when we account for the direct costs of generating and maintaining their IP.
The primary cost driver is TechnologyCo’s patents – over 1,300 applications and grants. Not only is there a cost to file and maintain each patent, but there is also extensive interaction between TechnologyCo’s lawyers and the Patent Office that drives costs. As the charts below show, the cost for filing and maintaining a single patent can be $50K for moderately complex technology such as a computer chip or cell phone component.
TechnologyCo spent nearly $6M for a portfolio that generated less than $3M in 2018.
This is where data analytics becomes valuable. At Tradespace, we predict the chances a technology will be licensed by looking at global patenting behavior across 200,000 market segments and by assessing a patent’s importance versus all other patents for a given technology. By deciding not to pay fees on 50% of its oldest patents with low likelihoods of commercialization TechnologyCo would save $500K in 2018 alone.
Furthermore, TechnologyCo could be more selective in patenting new technologies. By using market and patent data to ensure it only patents technologies in growth markets that align with its core strategy, TechnologyCo could reduce costs by an additional $1M in 2018 without sacrificing revenue potential. Over 10 years, this strategy would save TechnologyCo over $15M while freeing up resources to generate new commercialization revenue.
Commercialization: Re-defining Success
In addition to cost savings, smart IP management can generate significant revenue potential. However, IP commercialization success rates for companies and research institutions are staggeringly low – less than 5%. Since most organizations don’t measure IP costs, there is little pressure to change this dynamic. When you fully consider these costs; however, the need for a better approach is clear. As with IP lifecycle management, data-driven strategies can improve commercialization outcomes. Using a standardized approach to measure which technologies have the highest economic value can significantly increase a company’s success rate. With the data available today, we can model the economic value of any patent or technology disclosure using patenting trends, pro forma financials, and capital markets data.
At Tradespace, we’ve found that these same metrics can identify the companies most likely to invest in or license a technology. Companies can than feed these metrics back into their R&D strategies to ensure they are developing technologies with the highest economic impact and that meet the requirements of their current or future customers. This data-driven approach also allows us to automate the most labor-intensive parts of technology transfer. With more time to spend on strategic pursuits, companies can better pursue “big bang” opportunities – larger, strategic co-development projects with much higher returns.
We have more data available to us today than ever before. While that can be overwhelming, we have tools to make sense of that data. With the speed of technology development continuing to grow, forward-leaning companies are realizing that they can no longer afford to mismanage their IP. Only by building data into their IP decision can they take control of their IP costs and begin realizing significant revenue from their IP portfolio.
About the Author
A former management consultant to Fortune 500 companies, Alec founded Tradespace to speed innovation and revolutionize the way companies get value from their IP. Using data on 100M patents and advanced analytics, Tradespace makes it faster and easier for companies to source new technology, get innovations to market, and make smarter technology decisions.
Before launching Tradespace, Alec spent five years with Avascent, the top defense and aerospace consulting firm. He led engagements focused on strategic growth, commercialization and M&A. In addition to his corporate work, Alec worked closely with the Canadian Government to reshape their IP policy to drive innovation. For more information, contact firstname.lastname@example.org