Gain Competitive Insight with Intellectual Property Data

by   |   August 5, 2016 5:30 am   |   0 Comments

John F. Martin, Chief Revenue Officer of CPA Global and CEO, Innography

John F. Martin, Chief Revenue Officer of CPA Global and CEO, Innography

A revolution is underway for Intellectual Property (IP). Never before has the IP organization had so many tools at its disposal, launching it from an essential back-office function into an indispensable business partner.

At the forefront of this revolution are tools such as big data, predictive analytics, and machine learning. Providing new kinds of insights and augmenting intuition, these tools are transforming the ideas economy by arming businesses with unprecedented amounts of market intelligence.

Because companies typically file for patent protection years before they release new products, IP analytics can provide a window into the future, allowing organizations to learn what their competitors are doing long in advance.

Smart companies are seizing on this wealth of data to shape their strategy and gain a competitive edge, elevating the role of IP in the enterprise and setting the stage for more informed planning and decision making.

Just some of the insights that big-data IP analytics can yield include the following:

    • Competitor actions and intentions – both current and emerging new competitors


    • Technology trends and future scenarios


    • Predictions for future product and market developments


    • Risk identification and profiling


  • Identification of shifts in market importance for a technology



As with any revolution, there is evolution. Over the past decade or so, analytics and big data have been driving keener, more honed business insight. Among the greater advantages big data technologies offer is the role they play in combining public and private IP-related data into one cohesive storyline. Showing their IP in the context of the full universe of global patent filings and actions offers businesses a holistic picture of the market and competitor landscape, ultimately guiding better business decisions.

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Launched in the early-to-mid 2000s, traditional patent search tools tend to reflect the myriad of data errors prevalent in the Patent and Trademark Office data sets, requiring hours or days of data cleanup to be able to analyze a competitive situation. They are adept at providing long lists of patents, but aren’t helpful in providing guidance about which patents are more important to review or the relative strength of portfolios in a technology. Traditional intellectual property management systems (IPMS) are used primarily as back-office clerical tools, detached from the broader business and unable to provide answers about IP relative to competitors or markets.

But through big data analytics and the combination of private and improved public data, the next wave of IPMS will help inform decisions at every step of the patent lifecycle, rather than simply automating and transacting decisions made elsewhere within the business.

Next-generation analytics are connecting information, providing guidance, and generating insights to answer business questions proactively and provide competitive insights at a fraction of the time and cost of traditional approaches.

Business Intelligence, Competitive Edge

Today, IP analytics represents an exceptional source of insights into both current situation analysis and future planning, tapping into the heart of industry signals. Imagine, for example, what the auto industry would look like if Ford knew what models GM would be designing three years down the road. Or if Burger King had some insight into McDonald’s possible new product line years in advance. The potential is staggering.

While data analytics is propelling IP from the back office to the boardroom, automation and machine learning are minimizing rote work and freeing up resources so that IP insights can be used to inform business decisions.

Indeed, internal IP specialists often have a unique business perspective and depth of understanding of their company’s situation and technologies, making them the natural experts on current technologies and competitors. Allowing them the room to flex their well-honed skills can provide proactive input into business strategy. When needed, external experts can augment the capacity of the internal team and provide expertise in less-well-known technologies and market domains.

As new technologies blossom and cross over into new industries, Chief IP Officers are pushing competitor intelligence to the front of the invention-review process. Some are even requiring that a specific competitor be identified that may be blocked before moving forward with investing in protecting the invention.

With an eye on what the competition is investing in – and protecting – years before it’s released to the market, companies can use analytics to inform product innovation and development, all while positioning themselves to secure strong market share and margins. This proactive maneuvering becomes even more critical with the ever-increasing pace of technology crossover. To be sure, megatrends such as the Internet of Things and robotics are driving a wide variety of technologies – from wireless communications, software to sensors and mobile connectivity – into entirely new products and industries.

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Meanwhile, companies that fail to take advantage of new analytics technologies risk substantial competitive disadvantage, as their strategies likely will be suboptimal and their inventions less well protected. These companies also lose opportunities to capitalize on the future direction of their competitors and the larger industry.

The Road Ahead

Companies that want to take advantage of analytics technologies will be better positioned to optimize business strategy, recognize competitive threats early, proactively identify and manage risks, and stay relevant and competitive in today’s market.

To get there, enterprises can start by taking four critical steps in their IP management and analysis processes:

  • Automate the full end-to-end lifecycle. Gaps in process automation lead to duplicate effort, data errors, and suboptimal decisions. From ideas to conversations to decisions, a next-generation IPMS can reach beyond the typical process end points and coordinate all actions (and actors) end-to-end.


  • Provide guiding analytics.A key part of big data is not just access to the information, but also predictive analytics and metrics that provide guidance about what to examine in more detail. A critical part of the data explosion, analytics, when done effectively, can actually help maximize the value of time and point directly to areas that require focus.


  • Ensure landscapes are actionable.Pretty graphs or isolated factoids are not enough. To optimize decision making, the analysis must communicate insight or guidance that’s relevant to a decision in front of the business person, synthesizing the full context of the situation into terms that the decision maker can understand and act on.


  • Consistently advance. High-value software advancement is all about investment and client input. Next-generation IPMS require not only new capabilities but also constant and consistent development to infuse nonstop innovation and match the ever-evolving IP environment.


Burgeoning technology such as big data, predictive analytics, and machine learning are dramatically transforming business at large and the IP function in particular. Making the most of data can help you craft your company’s strategy and increase your profits.

John F. Martin is CRO of CPA Global and CEO of Innography, a patent analytics software company and a CPA Global company. He is responsible for CPA Global’s worldwide sales, field marketing, and growth strategies. He is also Chief Executive Officer of Innography, where in recent years he has tripled the company’s growth rate and led major product advances and global expansion. At IQNavigator, John served in C-level roles, helping drive 50-fold revenue growth. He was head of products at Saba Software and CSG Systems, and a management consultant at McKinsey & Co. John earned an MBA from Stanford and two engineering degrees from MIT.

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