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by Mathews Thomas, Janki Vora, Julio Wong, Satish Sadagopan | Published October 12, 2016
In Part 1 of this tutorial series, we gave an overview of cognitive computing and provided examples of how cognitive computing is being used to create industry solutions. We also determined a need for a cognitive platform to implement these solutions. The benefits of a cognitive solution include the reuse of components, faster development of the solution, and reduced costs. The key to creating such a platform includes the ability to run multiple use cases on the platform. Therefore, it is critical to identify these use cases and determine how a platform can provide benefits when implemented. In this series, we are focusing on the Telecommunication and Media & Entertainment industries for our use cases. We chose multiple industries to demonstrate that the platform is not industry-specific, although certain components must be industry-specific depending on the use case.
We begin our journey by focusing on the following use cases:
Note: We use IBM Watson synonymously with cognitive computing throughout rest of the tutorial.
A self-service agent lets a customer pose questions on various topics to a CSP. Watson can analyze the customer’s questions and provide a response. Sample question areas include:
Our use case focuses on device troubleshooting and device-related issues such as:
The benefits of using Watson in a self-service agent to resolve device-related issues include:
Let’s explore how a user can interact with a self-service agent to resolve a device-related issue.
In this image, you can see how the interaction might occur with the Device Doctor:
Watson then continues to work with the customer to update the iOS and confirms that the update was successful.
Let’s briefly review what Watson has done.
We will provide the details on how we achieved this interaction in subsequent tutorials in the series.
Resolving networking issues can be a complex problem. Finding the right resources is difficult, and not resolving the issues quickly can have a huge impact on the service provider, especially if negative buzz is generated. Cognitive computing can assist here by enabling a level 1 network engineer to resolve issues faster. In certain cases, the resolution can be fully automated without any human interaction needed so that the engineer can stay focused on more important issues. Due to the complexity of networking problems, we do not focus on specific networking issues. Instead, we focus on how Watson provides the tools to help resolve the issue.
Some of the key benefits of network operations include:
Let’s consider a situation where a network engineer is attempting to solve a Border Gateway Protocol (BGP) issue.
The engineer can also view a dashboard that highlights key issues that need to be resolved.
The question the engineer now faces is which specific node should be examined to resolve issues. Watson cognitive services can take the different parameters and optimize them to find the best solution, as shown in the following image.
Under normal circumstances, you would simply address the highest severity issues. With Watson, parameters such as severity, number of customers who are impacted, and revenue impact can be set to identify the key nodes that need to be addressed first. The engineer can then select these nodes and begin a dialog with Watson similar to the BGP scenario shown previously.
You have seen how users can interact with Watson in the digital agent use case by using natural language and how Watson can provide simple 1-2 sentence responses or more complex responses from a larger corpus of documents. However, Watson can also get additional information from back-end systems such as CRM systems to ensure that it provides the best response.
The network operations use case highlights the following cognitive functions of Watson:
In this era of digitized content, the consumer’s expectation is to consume relevant content where, when, and how they want it. This raises various issues for the content provider, the largest of which is customer retention. If the consumers feel the content provider does not understand their needs or it is difficult for them to find the content they like, they will likely look elsewhere. Therefore, it is essential that the right content be provided to the customer based on customer interest and content availability. To do this, you must understand the customer and the content you have that might be relevant for the customer.
Some of the key benefits of Personalized TV include:
The use cases can be split into two primary areas: understanding the content and understanding the customer.
To understand the content, you must be able to perform a deep analysis of the content and identify the appropriate metadata that can be associated with the content. This metadata can then be correlated with user interest. You can use Watson to identify the video tags that are associated with the content.
In this instance:
An extra piece to understanding the content is to understand the audio. Watson can convert the speech to text and then analyze the content. Let’s assume that we would like to identify scenes in our movie where the actors appear to be angry. A conversational dialogue that occurs between the actors might be:
Watson is able to determine the tone of these dialogues by processing the information and identifying key tones.
A statement such as “How can I delete this account?” by itself appears to be neutral. However, if you examine this statement in the context of the previous statements, Watson is able to determine that the tone is an angry one.
Understanding the customer includes various aspects. Much of this understanding falls into the traditional realm of analytics where data is sourced, transformed, stored, and then analyzed by various descriptive and predictive analytics tools. Watson can extend these aspects by providing various cognitive services. For example, the Watson Personality Insights service can provide a person’s psychological profile with as little as 2000 words that are written by that person — one way to get this information is with a person’s tweets. The Watson services can be combined with the traditional analytics to provide an even more comprehensive view of the customer.
Let’s briefly recap the use case and show how it benefitted from the cognitive capabilities of Watson.
We provided a brief overview of some key use cases where cognitive computing might be used in the Telecommunication and Media & Entertainment industries. Identifying such use cases, including the business problems that are addressed and business benefits that are derived by implementing the use cases, is an important first step in the cognitive journey. It is also important that these solutions are not developed in silos, but instead use a platform where the data, components, and use cases can be used across the organization. We have not yet mentioned any of the underlying technologies behind the use cases or provided any implementation details. In the next set of tutorials in the series, we will take a deeper look into these areas.
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