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Does Your Innovation Plan Make You Disruption-Ready?

Can you create a bulletproof plan for innovation in a rapidly changing world? Most executives recognize that “planned, structured and repeatable processes” have enabled sustained and successful innovations at companies like Apple, Google, Samsung, P&G, and Nike.

But can you pull off an efficient innovation planning process while still maintaining the creativity and push for disruptive ideas? Most of the global executives surveyed for GE’s 2014 Innovation Barometer answered yes to that question; however, more than 60 percent are also “convinced that their businesses have to encourage creative behaviors and disrupt their processes more,” according to the survey. More strikingly, these executives report that “the inability to generate disruptive ideas is killing their ability to innovate.”

Another key question \ emerges from the survey: How can Big Data, potentially a driver of prediction, support creativity and the push for disruptive ideas? Only 25 percent of respondents feel their companies are prepared for it.

A gap exists between these complimentary ideas.

In other words, the desire for internal disruption is not deeply reflected in the current implementation rate of Big Data best practices, which would usher in the predictive analytics that drive creativity.

When we look at the challenges of harnessing Big Data inside the innovation planning process here’s what we see:

Awareness – how exactly can Big Data make your company disruption-ready?
Eighty percent of those surveyed realize that analytics can help boost a company’s efficiency, but only 53 percent of respondents realize the critical value predictive analytics can bring to consumer and market trends.

Unsurprisingly, there is an education and adoption challenge. The relationship between innovation and Big Data is emergent. One senior innovation leader summed it up this way: “We talk a lot about Big Data, but we don’t really know how to use it.”

Advances in applied business intelligence are occurring an at exponential rate and there is tremendous experimentation—business leaders, analysts, research teams, innovation teams and internal venture groups all have a seat at this table. All are simultaneously learning two new languages (disruption and Big Data) while they look for competitive advantage. It’s like changing the tires on the car while it’s speeding down the Autobahn.

Decision-makers recognize the need to incorporate a constantly growing knowledge and information stream into their business operations. Another key educational issue we’ve found: Big Data must be understood to include external data, not just client data or internal data.

The “internet of things” has produced external or open data like never before. The trick is using this data in a smart way. Taken at scale and used properly, open source information can reveal powerful, unbiased business findings that challenge traditional conceptions of research, strategic consulting and competitive intelligence.

Open data can “signal” potential disruption. The same tools that the global intelligence community has used for decades to prepare for “threats” can be used by the business community to prepare for disruption. Processing external available data through a structured, processed approach is what leads to innovation processes that allow for agility and greater creativity.


What specific information makes a company “disruption ready” in its innovation processes? Applying intelligence gathered on patents, publications, company statements, pricing fluctuations, hiring patterns, or even tracking a consumer shift away from a brand resulting from a Super Bowl commercial, can make a difference if it is deployed in real time in the product creation process.

Here’s a hypothetical: based off open source data—patents, publications, research efforts, funding events and conference activities—a particular early-stage sensor technology is identified as highly promising according to performance metrics, but the intelligence suggests that its timeline to commercialization is three years prior to regulatory approval. Meanwhile, other technologies come to light that, although they have scored lower performance standards, have commercialization timelines closer to 1-2 years. This information empowers an executive team to make a go or no-go decision on a particular technology type according to strategic priorities, and while having the visibility of potential “disruption” in the pipeline.

A company’s strategic choice could be further informed by the advertising of a job posting for specialist in a high-performance technology, suggesting that a competitor has indeed decided to invest in commercialization.

This intelligence can be cross-correlated with demand indicators, such as “signals” from consumer buzz over social media, to assess whether this particular technology delivers a benefit or feature that is currently unmet in the market.

One of the greatest process disruptions executives are faced with is framed by the question: How do you create a process that anticipates and handles a dynamic, live, information-flow and feedback loop?

The question changes much:instead of worrying about processes that drain and stall creativity, companies can create innovation processes that allow for distinct decision points; allow for those decision points to be informed by a dynamic influx of information (agility); and are action oriented.

How does one create innovation processes that enable agility but don’t endanger current product lines or profitability? This process breaks down into four steps:

  • Being agile: innovation processes anticipate the influx of new information in making decisions. Influx of information is anticipated in weighing in the information process: (for example, a new early stage technology received additional funding from a university that will speed up its time to market.)
  • Risk Awareness: Innovation processes alert you to “early warnings” that might indicate your company’s disruption by a competitor; the risks and costs associated with redundant innovation (betting on an innovation that wasn’t meant to be).
  • Action oriented: This is about creating innovation processes with results that are actionable—at the end of the day, we want to answer key questions at any point in the innovation process.
  • Sustainable: Real-time, and complete innovation processes to enable ongoing product development, with a process for factoring in the most current, real-time information into the product models

Sustained awareness, actionability and agility can only be achieved when structures are created that stay in place to continually update and refresh the innovation processes. Ongoing monitoring on all early stage technological innovations to track potential disruptions is now possible. It requires disrupting the current innovation process by incorporating intelligence at every stage.

By incorporating Big, Open Data in the form of intelligence, you can shift the process of innovation to one that allows for creativity, agility, and the influx of all the most current information by integrating predictive analytics into your capability set. This will enable businesses to be both disruption-proof (watching the competition/market conditions for early warnings and signals) and get “disruption ready”—bringing the newest, most innovative ideas to market.

Ariel Avitan is VP Marketing and Strategic Accounts for Signals Intelligence Group. Prior to joining Signals, Ariel led the information security practice in Europe for Frost & Sullivan.  

Does Your Innovation Plan Make You Disruption-Ready? was originally published on Ideas Lab

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