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Cognitive Computing and Analytics

May 10, 2017

Cognitive computing is commonly seen as the next step past analytics—the future of big data, in a way. Cognitive computing is used in a large number of artificial intelligence applications, including virtual reality and robotics. It is intended to mimic human thought processes, offering insights as to how real people would react and respond in different scenarios. In the business world, cognitive computing can offer great benefits in terms of predicting outcomes involving customers and business partners, and can be applied to problems that traditional analytics just can’t solve. Here’s our guide on how to get started with cognitive computing:

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  1. Change your view: The way that your organization looks at and uses data needs to change before you can successfully incorporate cognitive computing. Take unconventional or non-traditional sources of data into account, and incorporate more automation and machine learning methods into your overall analytics and data strategy.
  2. Know its purpose for your organization: You should begin your introduction to cognitive computing and cognitive systems by identifying an area or a problem within your organization that requires a cognitive approach to resolve it. This could be making retail marketing approaches more effective and intuitive, for example, or any other issue that you are facing that requires the application of previously inaccessible knowledge.
  3. Know its role: You should use cognitive computing to enhance your organization’s analytical and machine learning capabilities, not use it as a replacement for analytics entirely. You also shouldn’t consider cognitive computing to be a replacement for any of your workforce or operational arms (at least not yet). It can help improve how humans work and what information is available to us, but it is not a substitute. This is an especially important point for retail organizations. The customer experience of a shopper in a clothing store, for example, is highly influenced by their interactions with in-store staff. Human interaction is important for in-person sales, and cognitive computing cannot generate the same results at this time.
  4. Combine traditional and new approaches: Combining traditional analytics with cognitive computing is currently a more effective approach than just using one or the other alone. Traditional data analytics and machine learning can make cognitive computing smarter, and can allow humans to work with it with greater ease, allowing both to gain new skills and insights.
  5. Know what makes a real cognitive system: Though useful, Apple’s Siri—and similar digital assistants—aren’t true cognitive systems. Though they can offer responses, they’re pre-programmed and have limits. Real cognitive systems use natural language processing and machine learning, making it easier for humans and machines to interact naturally and improving human expertise and cognition. IBM’s Watson for Oncology, for example, helps oncologists to analyze patient data and individualize treatment options. Watson can respond to requests and questions in natural language and increases the accuracy and efficiency of data analysis.

Cognitive computing and analysis is the future of business intelligence and data analytics—as long as we use it correctly. Organizations should know the capabilities and limits of these cognitive systems in order to use them effectively and derive beneficial, actionable insights from them.

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