Top Trends to Watch in Cognitive Analytics in 2016

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Learn more about James Kobielus.

Winter has arrived, and that can only mean one thing: every day is brighter than the one before.

As we move into the new year, the world will shimmer with fresh sources of cognitive illumination. Data scientists everywhere are championing high-impact business applications of cognitive analytics, which refers to a new generation of solutions that use advanced algorithms to learn directly and automatically from data.

Truly intelligent systems are mainstream now in most industries. As a business asset, cognitive analytics—which represent the backbone of these systems–build on enterprise investments in artificial intelligence, natural language processing, machine learning, artificial neural networks, streaming analytics, unstructured data, Internet of Things, and decision automation.

With that in mind, I’d like to call attention to some trends we foresee for cognitive analytics in the coming year:

  • It will become inescapable: Cognitive analytics are transforming life and work in the 21st century. Key trends include the adoption of cognitively enriched intelligent machines, including mobile devices and Internet of Things (IoT) devices. These are the emerging best practices in organizations everywhere. Survey research by the Economist Intelligence Unit shows that cognitive applications are being built and deployed by a growing range of organizations in many industries. Before long, there will be a market place of millions of cognitive agents or avatars driven in part by the explosive adoption of mobile devices, IoT, and the upsurge of machine-to-machine interaction Examples of such agents would be personal virtual assistants who would be with the people helping in many different facets of life. The cognitive computing fabric will be interwoven into technology (e.g., social media) and touch our daily lives.
  • It will become a seedbed of innovation: Cognitive analytics are the focus of disruptive innovation in the Insight Economy.       In order to drive business success, developers everywhere are building are building cognitive applications for every conceivable use, and data scientists with cognitive skills are at the heart of this fast-growing developer ecosystem. In a recent enterprise survey by IBM Institute for Business Value, 39% of respondents stated that they currently use cognitive analytics, while an impressive 61% reported that they either use it now or have short-term plans to implement cognitive computing, of which analytics is a core capability
  • It will become the convergence point for all big data: Cognitive analytics are ingesting new types of information. The cognitive revolution runs on fresh feeds of an ever-growing pool of disparate data, including media streams, IoT sensor data, and other nontraditional sources. Open-source data is integral to many cognitive analytic applications. In addition, crowdsourced data initiatives supply human-curated trustworthy data that enables cognitive algorithms to perform with astonishing precision and agility. Furthermore, cognitive applications such as IBM Watson can learn automatically from fresh data sources, including from the data generated in Q&A sessions with users. Here’s an excellent whitepaper from IBM Research on how cognitive computing is accelerating learning in the big data era.
  • It will become the hottest specialty in data science: Cognitive application development is demanding new types of skills. A new generation of data scientists adept at cognitive application development in Spark, Hadoop, R, and other open platforms/tools is coming of age. These professionals are combining statistical modeling, subject matter domain knowledge, and programming skills. Increasingly, many are self-taught and self-directed citizen data scientists focused on projects for the public good.
  • It will drive governance initiatives around the world. Cognitive systems are calling forth new legal, regulatory, and policy initiatives. As cognitive applications drive more business processes and automated more decisions, society is calling for more comprehensive legal, regulatory, and policy frameworks to manage compliance, risks, and ramifications. We are seeing a stepped-up demand for more consistent frameworks for cognitive data sharing, decision lineage tracking, and algorithmic accountability. Likewise, societies everywhere are demanding stronger safeguards over security, privacy, identity, and intellectual property protection in the era of pervasive cognitive systems.
  • It will become the key ingredient of personalization. The future of cognitive applications in our lives is pervasive personalization. Cognitive systems are becoming active, context-aware personalized-interaction agents, as illustrated by the Watson-enabled apps presented in this gallery. Future cognitive systems will enable more natural interaction with users including voice and visualization. In addition, our personal cognitive agents will interact with each other, and develop a collective intelligence. Cognitive systems will increasingly understand the geospatial and temporal context of everything we do, and deliver responses that consistently fit for those contexts.
  • It will automate the lion’s share of data analysis. Cognitive systems are adopting unsupervised learning to automate sensing and detection of deep patterns and nuances without complex data sets. Machine learning is enabling automated systems to understand new concepts on their own and train themselves to be experts. This automation capability is fundamental to machine learning’s value in a world where big data just keeps pushing into higher volumes, more heterogeneous varieties and faster velocities than ever.
  • It will accelerate initiatives to scale cloud data services. Cognitive analytic systems are scaling inexorably and indefinitely. Increasingly, cognitive systems will be offered as a fabric, exposed to the outer world as an API. As cognition-as-a-service offerings such as Watson gain adoption, the scalability will expand in the volume, velocity, and variety of data and algorithms being executed, as well as the resource requirements. Advanced cognitive endpoint devices/apps, new architectures for cognitive scalability, and real-time, in-memory, distributed cloud architectures will emerge. Implementation of cognitive analytics in the IoT will drive much of this scaling.

Cognitive analytics is the core investment that organizations everywhere should make to stay relevant in today’s insight economy. As 2020 approaches, cognitive systems are the primary lens that we’ll use to gain 20-20 vision into otherwise unfathomable clouds of big data.

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