Why cognitive sustainability makes good business sense

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As a leader, you constantly have your eyes peeled for ideas to accelerate growth, reduce cost and identify ways to improve customer satisfaction. These are common goals across every industry, whether in the private or public sector, and they’re becoming more challenging to achieve in a changing world. As inequality, poverty, and environmental pressures rise, there’s a growing expectation that businesses and government use their resources to address these societal issues.

Consumers are becoming increasingly more willing to pay a premium for sustainable brands, investors are beginning to shift capital toward more sustainable business models and the brightest young leaders entering the workforce are gravitating to organizations with sustainable practices (even at lower starting salaries). Add to that the fact that there will be increasing cost pressure as governments begin to respond to environmental risks, and it becomes clear that a sustainability focus is good for business.

So how can cognitive computing and the Internet of Things help?

The opportunities that cognitive IoT solutions can deliver in the sustainability space are enormous, and early adopters are gaining a competitive advantage.

One example in a particularly competitive market is this automobile manufacturer who saw pollution avoidance as an opportunity to differentiate their product portfolio to gain market advantage. Air quality monitors in vehicles weren’t new but lacked a predictive capability, which means the best they could do is provide “in the moment” readings. This company sought to combine a much broader array of factors that could predict pollution conditions and actively help drivers reduce their exposure. Data from sources such as IoT sensors and weather maps was overlaid on road network topology. From there, cognitive analytics were applied to discover patterns across the data sources and enable predictive services. These cognitively derived air quality readings provided the basis for specific driver recommendations.

Moving from air quality to water conservation, this winery had long sought a way to fine-tune its irrigation practices to become more sustainable and help address the water crisis, but massive data complexity and prohibitive costs had been roadblocks in the past. Traditional irrigation practices require winemakers to water uniformly across an entire vineyard, and to manually monitor the growth of, and trim back, leaf canopy as needed. Their new cognitive IoT solution automatically provides different vines with the optimal amount of water based on remotely sensed moisture data. The solution automatically detects when plants don’t need water, which slows the growth of leaf canopy and reduces the need to manually trim it back. By applying cognitive IoT technologies to help achieve their water conservation goals, they achieved better business outcomes as well – increased crop yields of 36% – and enhanced their long-term growth prospects.

As sustainability moves into the mainstream on Wall Street, particularly with millennials, organizations that are able to harness the power of cognitive technologies to advance their goals while helping the planet will lead the way.

Want to learn more?

To read more case studies and discover how cognitive solutions can address your individual goals, solve problems, and drive innovation, chat with the IBM Watson Business Coach.

To learn more about how early adopters are driving business value with cognitive technology, read the latest market study, The Cognitive Advantage.

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Robert Waters

My interest in Watson began during a job search in the Gr Recession when, during overwhelming job market changes, HR turned to analytics and deep personalized data. To that experience and point – am I correct to state that cognitive tech needs vastly deeper penetration into personal human data to advance – where the unstructured increases over structured data?


    Lynne Slowey

    Hi, thanks for your comment. I think the greatest potential of cognitive technologies lies in their ability to access, use and learn from all types of data, from inside and outside the organization’s firewalls. So whereas most organizations today have the ability to tap into their own structured data for deeper insight, such as customer or HR records, transactions, etc., there are vast pools of unstructured data that still remain beyond the reach of most. For example, social media, weather, news, events, research reports, images, video, sensory data etc. – by some estimates, these account for nearly 90% of all data. Organizations that are able to tap into these other sources of unstructured data and marry it with the insights they already have from their own data will gain competitive advantage. The type of data that is used (HR/personnel, transactional, operational, etc.) will depend on the nature of the problem the organization is trying to solve. The organizations in this post were primarily tapping into IoT sensory and product data, for example. Organizations that are focused on solving problems in the HR or sales/marketing domains may leverage employee/user data in their solutions.

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