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Cognitive Computing Can Help Organizations Leverage Data Insights

cognitive computing

To some, cognitive computing and artificial intelligence (AI) have a gee-whiz component. Articles effuse over how the technology can truly automate automated vehicles, create movie trailers and even write (self-reflexive) articles on the topic.

But practical business uses for cognitive computing and AI are far more than gee-whizery. In fact, nearly every major tech firm is working on AI to assist in decision-making processes, streamline operations and even automate some tasks.

IBM is a leader in this burgeoning field, especially after it unveiled its natural-language “Jeopardy!”-playing IBM Watson* cognitive-computing system. (It had been working in this area even years before that TV debut.) Its designers could have sat back and rested on their laurels, but they saw this as more of a test case than an end unto itself. Thanks to this forward-looking insight, Watson (as well as some publicly available data) is now available around the world as part of the cloud-computing environment.

“The biggest opportunity for many enterprises lies in connecting these pieces. It becomes an orchestration of some of the infrastructure and systems that sit on-premises and the unique internal data nobody else has access to.”
—Roger Bales, manager, Worldwide Business Development and executive consultant, IBM Systems Lab Services

In addition to that, IBM has developed on-premises cognitive tools, including IBM PowerAI running on IBM Power Systems*. This software package allows data scientists to quickly deploy deep-learning models and developers to more easily integrate them into their applications, leveraging information only they have access to.

Putting the Pieces Together

As Roger Bales, manager, Worldwide Business Development and executive consultant, IBM Systems Lab Services, points out, these solutions—cloud-based and on-premises—aren’t mutually exclusive. Indeed, they play very well together.

“The biggest opportunity for many enterprises lies in connecting these pieces,” he says. “It becomes an orchestration of some of the infrastructure and systems that sit on-premises and the unique internal data nobody else has access to. And then they’re tapping into the power of Watson and some of its publicly available data to enhance their overall in-house information experience, with some new cutting-edge techniques that allow for the inclusion of real-time data.”

One real-world example Bales is well aware of involved competing Raleigh, North Carolina, grocery stores that were facing the possibility of 2017’s Hurricane Irma making landfall and directly hitting the area in which they operated. As with many potential natural disasters, anxious consumers were stocking up on water and clearing the shelves.

When Bales, who lives near both stores, arrived at one, no water was available, as was also the case with the second store. The difference between the two was when they expected to restock their shelves. The first store indicated it would have more water the next day, with trucks from different parts of the state already on the road. The second store had received a shipment the day before the run on water began and wasn’t expecting another shipment until the following week.

“This is an example about how powerful integrating internal data about a supply chain with information from the Watson-based data sources such as The Weather Company can be. In this case, it allowed grocery store A to tap into the ‘weather data’ to derive better insight and drive better/more informed decision making, based on integrated external weather forecasts and its own historical analysis,” Bales notes. “As a result, they had already made early and necessary logistics changes to start the resupply process, while grocery store B only had empty shelves to look forward to. It didn’t have the cognitive insight store A had.”

(Thankfully, Hurricane Irma skirted the area.)

Fast and Agile

Another, perhaps more advanced example involves a large financial institution that had developed a cognitive system that helps its members take better advantage of their benefits. They can call the institution and say things such as “I’m getting ready to transition from one life stage to the next. What sort of recommendations would you make in terms of your products and services or things I need to be concerned about?” The system will then respond accordingly. Parts of a cognitive-computing system like this might involve data and deep analytics, AI, natural language translation and advanced methods for interacting with humans.

Smaller companies also can leverage cognitive computing, using Watson to try possible cognitive tasks on a tiny—and affordable—scale and then expand and grow them. IBM Cloud* with Watson is fast and agile, allowing users to get started and move forward quickly.

“Many of these organizations want to expand, and this is a great way to start, knowing that as they’re growing, IBM has the leading-edge cognitive computing, infrastructure, software solutions, and the proven expertise from the Systems Lab Services team to fit all of those pieces together,” Bales remarks. Large or small, organizations should begin their forays into cognitive computing by clearly defining the outcomes they’re expecting to achieve, which Bales says is “a key indicator of success.” This is when they should start considering issues such as the user experience they’re ultimately attempting to deliver.

Actionable Insight

One technique to assist in this is design thinking, which is a framework IBM uses to solve its clients’ problems at the speed and scale expected in the modern digital enterprise.

A holistic roadmap highlighting the techniques and tools needed to reach the outcome is essential, especially as it pertains to design thinking. This may include changes in processes, skills and people, as well as deploying technology such as Watson, on-premise IBM Power Systems with PowerAI and IBM Z*, depending on technological requirements and existing IT infrastructures.

Another essential element to this involves which data should be included in the roadmap. Returning to his grocery store example, Bales explains that, depending on the outcome, different information and information sources may be required.

As he explains, “The parent entity packs up a truck every so many days and sends it to store #1220 in Raleigh, North Carolina. They know, based on inventory, what’s sold in that store, and forecast how many cases of water they need versus how many cans of green beans. So they have an understanding about what they need based on years of logistics and analytics about product mix and inventory levels.

“But that’s only part of the story. Hurricanes do happen, so why not throw that into the mix? They could add a piece using Watson and leveraging the data coming from The Weather Company to add on to what they’re already doing in terms of logistics and enhance that. So a forecasting feature of weather dynamics could impact the basic assumptions around their inventory management.”

The same holds with the Internet of Things, which refers to devices out in the world that capture near-real time data such as traffic or security cameras, meters on toll roads and weather sensors. Depending on how they’re integrated with an overall cognitive computing system, the data they gather can make a difference in business operations on the fly—much faster than data analysts can account for them.

As more and more of this data becomes available, the volume can become so large that Watson-like systems and the capabilities of IBM data storage solutions and technologies are needed to manage the data, as well as designating which data is needed to provide valuable, actionable insight.

Starting Small

“I think one of the big challenges and opportunities in 2018 is to be able to understand the cognitive-computing landscape, which includes Power Systems, PowerAI, IBM Z, Watson and IBM Cloud*, to determine which technologies organizations can get the most business benefit out of,” Bales remarks. “And my team is putting a lot of focus around the notion of what we call ‘cognitive discovery,’ which involves using techniques like design thinking to help clients discover and identify pilot projects, opportunities to engage in cognitive computing, cognitive systems, Watson, big data and analytics, and associated areas of integration.”

According to Bales, the C-suite can play a pivotal role in early success by focusing on three key areas:

  1. What do I need to do as the leader to set a vision and demonstrate the importance of these new initiatives?
  2. What should the team reporting directly to me be doing to build capability and competence?
  3. And how do I enable my organization to embrace cognitive computing?

An executive should be continuously learning about the topic, even in a general way. She can then promote the idea that the latest emerging cognitive computing technologies and capabilities can be leveraged to enhance organization operations.

The team directly reporting to the CXO should then be encouraged to think of this as a risk-free zone to explore some of these cognitive technologies and techniques. They may want to begin with a small Watson pilot, picking a simple idea to embrace and experiment with, and then applying that to test cases and reporting the results to executive leadership. Leadership should then communicate suggested cognitive computing enhancements to managers, who can express to their employees why the company is heading in a particular direction.

As Bales simply puts it, “Most people look to their leaders for direction and guidance—What are they focused on? What’s important to them?—and then follow that lead.”

Knowledgeable Creativity

Cognitive computing isn’t just a gee-whiz technology that will drive cars, create movie trailers or write articles about cognitive computing. Indeed, current and upcoming uses of the technology—whether on-premises, cloud-based or a hybrid of the two—are poised to change the way organizations handle their logistics, interact with customers and improve their bottom lines.

Organizations small and large can begin their foray into cognitive computing in several ways. Just as no two organizations are alike, neither are their approaches to and the uses of the technology. It simply takes some knowledgeable creativity and minor risk taking to make it happen.

Jim Utsler, IBM Systems Magazine senior writer, has been covering the technology field for more than a decade. Jim can be reached at jjutsler@provide.net.


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