RESEARCH & RESOURCES

Convergence of Big Data, Cognitive Analytics, and the Internet of Things

The generation of big data may be growing exponentially and advancing technology may allow the global economy to store and process ever greater quantities of data, but there may be limits to our innate human ability—our sensory and cognitive faculties—to process this data leveraging Internet of Things.

By Raghu Sowmyanarayanan

Cognitive refers to the mental process of knowing, including aspects such as awareness, perception, reasoning, and judgment. Cognitive computing involves self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works. Cognitive computing systems use machine learning algorithms. The goal of cognitive computing is to create automated IT systems capable of solving problems without requiring human assistance.

The Evolution of Cognitive Analytics

Both analytics and big data are on unstoppable trajectories right now, moving toward destinations that are beyond the greatest dreams of the first database (RDBMS) builders of the 1960s and 1970s.

The familiar business output from traditional data warehouses systems is a report containing a quantitative description of what was captured through historical transactions. This is extracted from the data via descriptive analytics, which provides hindsight (from past data) and oversight (from current data).

Modern big data systems contain a much broader picture of your business than does a traditional RDBMS. Big data systems combine data from multiple sources (inside and outside your organization), multiple channels (including social media, Web logs, and customer service interactions), and multiple viewpoints (context, content, sentiment, location, and time). As a consequence, it becomes possible to build predictive models of the behavior of objects (customers, machines etc.) within your business area.

The business output from our big data systems can therefore become much more than a report; the output could actually represent new knowledge (about the past, present, and future of your enterprise). This is discovered from the data via predictive analytics, which provides foresight about what is likely to occur regarding the objects within your business area.

Later, prescriptive analytics emerged, which provides insight into how objects behave, going beyond predictive models. Insight is needed in order to see beyond what has happened to understand objectively under what conditions a given object (customer, machine, etc.) will act or react in a certain way, perhaps even in a new way that was not seen in the historical data.

The next stage of development in analytics comes from the emerging field of cognitive computing. IBM's Watson machine is the prototype of this type of computing. The machine can access a vast store of historical data, then applies machine learning algorithms to discover the connections and correlations across all of those information nuggets. It uses that resulting "knowledgebase" as the engine for discovery, decision support, and deep learning. The result is cognitive analytics, which delivers what's right in a given situation – i.e., the right answer at the right time in the right context.

Think of the game show Jeopardy. If the information provided is "2001" and the context is "major events," then the correct response is, "What is the attack on the World Trade Center?" If the information is "1984" and the context is "American presidents," then the correct response is "Who is Ronald Reagan?"

Convergence of Cognitive Analytics and the Internet of Things

Cognitive analytics is the best paradigm for data-driven discovery and decision making. Machine learning algorithms applied to big data will mine the data for historical trends, real-time behaviors, predicted outcomes, and optimal responses. The cognitive algorithms can be deployed to operate in a self-automated way in appropriate settings leveraging the Internet of Things. Of course, not all applications should let a machine make the decisions, but it is not unreasonable to allow a machine to mine your massive data collections autonomously for new, surprising, unexpected, important, and influential discoveries.

It is also acceptable to allow autonomous operations in some environments (for example in oil industry), which has used huge amounts of real-time data to develop ever more hard-to-reach deposits. Now, the industry has extended its use of big data to the production side in the form of automated, remotely monitored oil fields. The benefit of this approach is that it cuts operations and maintenance costs. In the digital oil field, a single system captures data from well-head flow monitors, seismic sensors, and satellite telemetry systems (all part of the Internet of Things). The data is transmitted and relayed to a real-time operations center that monitors it, detects anomalies, and adjusts parameters to control anomalies, predict downtime, and act on that information to optimize production and minimize downtime.

Best-in-class manufacturers conduct conjoint analyses to determine how much customers are willing to pay for certain features and to understand which features are most important for success in the market. For example, one manufacturer used customer insights data gathered through sensors (again, part of the Internet of Things) to eliminate unnecessary costly features and adding those that had higher value to the customer and for which the customer was willing to pay a higher price.

The Road Ahead

Hiring data scientists is a popular solution for gathering insights about specific data sets. However, data scientists are fundamentally inefficient in such areas as real-time vision analysis, image recognition, speech analysis, video analysis, and other fundamental aspects of cognitive systems. Cognitive data and platforms such as IBM's Watson will help expand the capabilities of traditional data science to provide more sophisticated intelligence over traditional data sources.

Automatically taking actions based on data insights is becoming an increasingly important aspect of modern applications. Cognitive data in conjunction with the Internet of Things is a fundamental step towards enabling intelligent decision making based on the data insights generated by software applications.

Raghuveeran Sowmyanarayanan is a vice president at Accenture and is responsible for designing solution architecture for RFPs and opportunities. You can contact him at r.sowmyanarayanan@accenture.com .

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