Rashik: Using pattern matching to drive business innovation

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Rashik Parmar
IBM Distinguished Engineer

As former president of IBM’s Academy of Technology and a Distinguished Engineer, Rashik Parmar has spent the better part of three decades working at the intersection of innovation and technology. An advisor to public and private sector organizations around the world, he has helped municipalities put data to work in new ways to increase citizen access to a broad range of government services. His private sector work has taken him to the frontiers of banking and deep inside the retail and manufacturing sectors where he has helped clients develop new business models that take advantage of digitization and the cloud. A lifelong student of technology, Parmar is also adjunct professor of innovation and entrepreneurship at the Imperial College, London.

Despite all the money invested and the pressure to grow revenue, why is it so hard for established organizations to innovate?

Given current competitive and economic pressures, organizations are often so busy keeping the current business going that innovation becomes something that happens in sporadic moments. A c-level executive will tell a senior manager to take their team offsite for a day or two and “go innovate.” But that effort generally yields little real result because teams come into such sessions without the built-in process or time to think about it productively.

Because the fear of failure is so great and market punishments for missteps so severe, most organizations prefer innovation that comes in bite-sized increments versus bold breakthroughs, improvements they can drive up the ladder through venture funds or acquisitions that minimize the total risk. By putting a little process around the innovation cycle, organizations can overcome some of that fear and accelerate growth.

When people think of the creative process, most think of something unbounded and free-flowing. But you argue innovation is more likely to bear fruit when tackled in a systematic way. Why?

Injecting structure into the innovation cycle gives people the means to channel their thinking in a way that is more likely to improve the odds of success than pure open-ended sessions can do.

It offers organizations a way to de-risk innovation and make new approaches more palatable by removing some of the abstraction and fear that can undermine big picture ideas. Because upper management is always predisposed to home in on the downside potential, the ability to isolate the real deal-breaker issues and problem-solve around them allows teams to come into c-level discussions with a far more thoughtful and persuasive business case.

You see data and analytical tools, in particular, as key to unlocking new sources of value. Why?

For the last 50 or so years IT has been all about reducing cost and boosting productivity. But big data and robust analytics are changing all that. We’re seeing data-enabled innovations expand topline revenues by as much as 30-60 percent.

Organizations are doing this in a variety of ways. Rolls Royce, for instance, saw a 35 percent revenue jump in its airplane engine business after it introduced a line of sensor-equipped engines capable of diagnosing maintenance issues long before they developed into costly problems, allowing airlines to reduce expenses and improve safety.

You’re big into the notion of pattern matching. Why are patterns so intrinsic to innovation?

Most organizations know they’re sitting on a trove of potentially valuable data. Where they struggle is figuring out how to extract the value from that data and pair it with the right opportunity. That’s where pattern matching comes in.

Because humans are naturally attuned to look for interrelationships—that’s how we draw connections and derive insights—pattern matches are a new yet powerful way to frame the innovation process. In business settings, a well defined set of innovation patterns can act as a sieve, helping managers more quickly identify key customer insights and align them against their own organizational competencies.

How can you tell what patterns to use?

Our work with a broad range of IBM clients over the past several years reveals five distinct, overlapping patterns. By examining them methodically, managers can speed the hunt for new business value.

The first pattern is incredibly simple. It involves using data that physical objects generate to improve an existing product or create a new one. A wind turbine manufacturer, for instance, can use the ball bearings in its propellers to alert the company to anything from weather events to mechanical problems. A washing machine can automatically recognize which clothes have been put into them and adjust the wash cycle accordingly. Such applications are already driving a significant uptick in revenue for leading operators.

The second pattern involves digitizing physical assets. We’ve already seen this in books and music, but the disruption is only just starting. With 3D printing, for instance, it’s possible to produce the same part anywhere. That will continue to spawn new business models and markets.

The third pattern looks for distinctive service capabilities that can be codified and sold. At IBM, for instance, our Global Expense Reporting Solutions tool proved so successful at lowering administrative costs internally—by 60 to 75 percent—that we realized our clients would value the same, so we turned the system into a service. Companies across industry have a similar opportunity to take processes they’ve perfected, standardize them then sell them to other companies, often through the cloud.

The fourth pattern looks at ways to combine data within and across industries. Getting trusted partners in the same value chain to connect and streamline key processes can add immense value. Cities from Istanbul to Rio de Janeiro, for instance, have integrated systems across a host of departments to improve public safety coordination and relieve congestion on city streets by providing real-time information to bus drivers, dispatchers and commuters that optimize travel planning and trip time.

The fifth pattern concerns trading data. A payments company, for instance, sits on vast swaths of customers’ purchasing information. It can package and sell that data to retailers and consumer products companies to help them get a jump on buying trends and tailor promotional offers.

It’s important to know that most patterns are used in combination and different players may handle different aspects of the data lifecycle. In the above example, for instance, the payments provider may have the data, but the retailer has the context. If the retailer can pair that context with an actionable solution, that’s where the money is.

What are the key success factors?

Organizations need to be really clear on the issue they’re trying to solve. They need to be really clear on where the investment is going to come from. And they need an inspiring leader, one with the capabilities and track record to sustain the commitment.

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