Closing the data quantity – data insights gap

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Digital technologies are rapidly emerging as disruptive forces for businesses across a range of industries. In retail, for example, digital is fundamentally changing the way consumers communicate, collaborate and choose products and services. To win in their respective marketplaces, IT leaders need to find fresh ways to be relevant with tech-savvy customers, partners and employees and to compete with rivals. And they need a partner who invests in and reinvents IT infrastructure to help customers unlock the next wave of business value.

Business leaders have long used IT to improve productivity and efficiency, expand into other markets and optimize supply chains. But customer expectations have evolved. Many consumers expect brands to know them individually and deliver personalized interactions and self-service options.

How can organizations bridge the gap between untapped opportunities and current capabilities? How can hidden insights that reside in data ― structured and unstructured ― be more fully harnessed for discovery, decision support, dialogue and action?

The answer is cognitive computing. Cognitive systems can access a vast store of historical data and then apply machine learning algorithms to discover the connections and correlations across all these information nuggets. They aid in decision making and help reduce human bias by offering evidence-based recommendations. And they continually evolve based on new information, outcomes and actions. Because they can engage in dialogue with humans, these systems can understand customers based on past communication and behavior and bring context- and evidence-based reasoning to interactions.

Enabling highly informed and timely decisions

While the digital age has brought a massive amount of data brimming with potentially useful insights, organizations continue to find unlocking the full value of that data challenging. Cognitive-based systems can meet this challenge by helping bridge the gap between data quantity and data insights.

Intelligent machines simulate human brain capabilities to discern patterns and distill actionable business intelligence. They can build knowledge, understand natural language and provide confidence-weighted responses.

Cognitive systems can essentially change the way humans and systems interact and significantly extend the capabilities of humans by providing expert assistance. They provide advice by developing deep domain insights and bringing this information to people in a timely, natural and usable way. In this manner, cognitive systems can play the role of an assistant ― albeit one who does not require sleep. They can consume vast amounts of structured and unstructured information, can reconcile ambiguous and even self-contradictory data, and can learn.

Automatically taking actions based on insights from data is becoming an increasingly important aspect of modern applications. Cognitive data in conjunction with the Internet of Things takes an initial step toward enabling intelligent decision making based on the insights that software applications can generate. And the importance of cognitive applications is only going to expand across industries and grow in a world of vast data streams from the Internet of Things and other sources.

In the insurance sector, for example, cognitive systems are helping underwriters assess the individual risk of each customer in a highly personalized manner by combining weather data, geolocation data and data from other sources through mobile and other technologies. In healthcare, cognitive technology is helping doctors and other medical professionals detect serious illnesses and take expedient action to diagnose, treat and administer care. One way in which cognitive systems can assist the healthcare industry is through learning many aspects of medicine by absorbing vast volumes of medical literature in highly compressed time frames that would be impossible for humans to keep pace with.

Elevating the customer experience

Although data scientists have an important role in using analytics to mine insight from data sets, cognitive systems excel at analysis of a wider range of data types, such as real-time vision analysis, image recognition, speech analysis, video analysis and other fundamental aspects of cognitive systems. Cognitive platforms are expected to help expand the capabilities of traditional data science to provide increasingly sophisticated intelligence compared to traditional data sources.

Tedious customer experiences such as answering a battery of questions to report a product or service problem through a call center can be replaced with natural language processing. These experiences draw from a spectrum of customer insights, communication histories and behaviors — even those hidden within unstructured data. The result can be compelling, user-centered experiences that are intuitive, functional and easy.

Establishing a cognitive vision

The benefits of cognitive computing are not realized in a single “big bang” when they are initially rolled out. Instead, cognitive systems require an evolutionary track that provides increasing value over time. Across industries, cognitive systems are expected to enable businesses to be highly agile and responsive and more connected than ever to their customers.

Customer engagement is a natural fit for cognitive computing. Companies that recognize and adapt early to this reality can not only gain greater operational efficiencies but also use this cognitive disruption to uniquely enhance the customer experience and drive business growth that fosters true brand differentiation.

Get an inside track on cognitive computing.

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