Transformational Analytics: Internet of Things analytics
Welcome to the ‘We Economy’: Finding the business in your data
By Arnab Chakraborty, Michael Svilar and Prith Banerjee (l-r)
The Internet of Things (IoT) and the data it is producing are becoming main catalysts for change and transformation for businesses and consumers alike. Today, with every object, device, consumer, technology and activity being wired or connected wirelessly through the IoT into the digital realm, an increasing amount of data is being created that can offer countless benefits. For instance, businesses can analyze sensor data in real time to make key decisions impacting patient healthcare or resource management, and consumers can receive more personalized, timely and connected products and experiences such as smart smoke detectors or tailored deals from retailers.
And that’s just the start of it. While the IoT is driving new innovations, businesses can expand their purview on its capabilities even further – continuing the IoT momentum. Per Accenture’s 2015 Technology Vision, pioneering businesses are fundamentally changing the way they look at themselves and are mastering the shift from “me” to “we” – stretching their traditional boundaries to create a digital ecosystem called the “We Economy.” There is a transformational power with this growing web of data and connections – one in which everyone, from consumers to businesses, can win.
As the ocean of data continues to multiply from consumers, businesses and growing digital ecosystems, so does the opportunity it presents. The opportunity outlook for the IoT and the Industrial Internet of Things (IIoT) – the universe of intelligent industrial products, processes and services that communicate with each other and with people over a global network – in particular, is quite impressive. According to new research by Accenture, the IIoT could contribute $14.2 trillion to world output by 2030.
Figure 1: Selected results from 2015 Technology Vision survey.
One key way for businesses to get the most benefit from IoT and IIoT in the burgeoning We Economy is to capitalize on the value of their data, something Accenture calls the “Outcome Economy” in its Technology Vision 2015. Enterprises should move beyond selling just products to selling services, and focus on creating impactful outcomes that customers really care about. It is imperative for businesses to harness the data, analyze it for data-driven insights, and find the business in their data to achieve these better outcomes.
Analytics’ Role in Realizing the Potential from IoT
At every moment of every day, data and big data is being created and used by smartphones, office computers, sensors, social media, video cameras, wearable technology and more, impacting the lives of consumers and corporations. Organizations recognize the value big data can bring – 82 percent of respondents from global research agreed big data provides a significant source of value for their companies – but a challenge is determining how to manage the continuous and robust stream of data coming from the IoT, and delivering value on that data by providing exactly the right information to the right person, at the right time.
The business case behind most IoT deployments relies on collecting data and gaining actionable insight from it through the right types of analytic tools and techniques. Mere connectivity of devices already allows valuable enhancements such as remote service and predictive maintenance, but ultimately the value in IoT can be found in the ability to analyze the data and expose detailed and comprehensive insights from assets, processes and products in use that have traditionally been more or less opaque to accurate analysis. In other words, it’s the ability to translate and extract the signal and insights from the noisy data to support, for example, not just improved operations performance, but entirely new services based on how customers use products and the outcomes they are trying to achieve. By pursuing IoT analytics, connected organizations can make data-driven business decisions based on hard evidence and statistical probabilities instead of soft opinions and gut feelings.
Figure 2: The challenge of deriving insight from data.
Architecting Analytics Deployment in the IoT World
Developing an analytics strategy is critical to driving true business value for a company trying to take advantage of the IoT and the big data it is creating.
The first step is establishing a data supply chain to properly and quickly mobilize data for consumption. When building a data supply chain, firms should utilize a data service platform that makes all data swiftly and easily accessible to those who need it when they need it, while also integrating data from multiple sources. By enabling data to flow easily, quickly and usefully through the entire organization – and eventually throughout each company’s ecosystem of partners – companies are on their way to realizing the true value hidden in data.
Next, to reap the full benefits of the IoT, it’s important to develop a robust analytical platform that brings together the capabilities around sensor-driven computing, industrial analytics and intelligent machine applications. Myriad analytics tools can sift through and process data once they have been programmed with the parameters. Whatever the tools used, the ideal goal is to put a system in place that can automate the analytics process of collecting and processing raw data, which can then be used to deliver business insight.
Figure 3: Enabling data to flow easily, quickly and usefully throughout the enterprise.
The final step of analytics on IoT data is the “Intelligent Enterprise.” This involves a journey from rule-based automation based on IOT data (where some automated actions are taken based on some IoT data triggers such as turning on a thermostat when the temperature goes below 65 degrees), to machine learning on IoT data to automatically create the rules for automation (whereby the thermostat learns when to turn on the heating depending on the user’s preferences), and finally into the domain of cognitive computing, where automated systems start behaving with almost human intelligence to sense, understand and act.
When developing such an IoT platform, it is important to pay attention to security, data privacy and trust issues. Conventional security techniques work in the information technology (IT) domain and in the operational technology (OT) domain. However, the power of true IoT systems is the IT-OT convergence, and such systems require newer approaches to security, data privacy and data governance.
Following is a roadmap to developing an analytics platform that leads to value in an IoT world:
- The first layer of the platform applies analytics directly to raw IoT sensor data (it is often deployed at the edge). The layer collects, integrates, cleanses and filters data from a wide spectrum of IoT sensors and devices. It then applies additional processing, such as feature extraction and some low-level analytics (e.g., applying video analytics to extract the location of a moving object in a video stream, which can then be used to track that object).
- The second layer of analytics produces richer context and meaning, presenting higher value for the user. It applies data fusion and includes richer analytics, such as pattern recognition, event classification, behavior/routine learning and anomaly detection, activity recognition, object tracking clustering and more.
- The third layer of analytics includes complex operational capabilities, which creates significantly more business value. It includes model-based and data-driven predictive analytics, optimization and simulation techniques, and big data methodologies that can mine data coming from devices.
- The fourth and final layer of analytics is machine learning and cognitive computing where automated systems start behaving with almost human intelligence to sense, comprehend and act.
This technology architecture is not only meant for rapid product application development and operation, but more importantly to enable the collection, analysis and sharing of the potentially huge amounts of data generated inside and outside the devices/machines/products that has never been available before. Building and supporting the technology stack for smart, connected devices/products requires a range of new skills – such as software development, systems engineering, data analytics and online security expertise.
Following are a few examples of how IoT data can be put to use:
- Product and Service Development: The connected product’s quality and behavior is assessed, and then the areas for product feature improvement are flagged based on the insights obtained from product usage behavior.
- Predictive Maintenance: Predictive maintenance refers to a method in which equipment or infrastructure is maintained when an analysis of its operational (e.g., sound, speed, vibration) metrics indicate that a breakdown is likely to occur. The condition-based analytics method can be complemented by circumstantial data (e.g., ambient temperature, employee absences, product recalls) to make the analysis more accurate.
- Usage Behavior Tracking: This refers to cases in which usage or consumption of a product or service are tracked and analyzed by taking advantage of IoT connectivity and subsequent analysis of the collected data. Analysis of, say, electricity consumption within a smart grid could be used to reveal high-usage customers, and then mitigate their consumption through targeted efficiency programs. Also, car insurance providers and other companies applying usage-based pricing to their offerings are usually counted under this category.
- Operational Analysis: Here the organization employing IoT analytics applies the data assets to monitor and optimize its internal operations. For instance, a logistics group can analyze its delivery fleet to optimize routes and provide more accurate estimates on delivery times. Similarly, a retailer running connected vending machines can spot the bottlenecks and quiet zones within its network, and thereby optimize the machine sites.
- Outcome-Based Services: The most interesting cases are those that convert products into outcome-based services, such as IoT sensors in the agricultural industry that allows a fertilizer company to provide precision farming as a service.
In contemplating the various opportunities IoT data can offer businesses, the sheer growth potential can be overwhelming and cause various challenges. To help manage the data deluge and transform it into a competitive advantage, it’s important for firms to keep a focus on the following: 1. finding ways to collect and analyze the data to drive bottom-line business benefits; 2. managing the data privacy and data security risk associated with the data to help mitigate any business risks; and 3. investing in analytical tools and talent to help monetize the data to improve the business performance.
Several forces will continue to drive IoT’s continuous growth over the next decade. These include the miniaturization of sensors, the plummeting cost of instrumenting an asset with sensors, changing regulatory requirements, continuous advancement of data science and analytics, and the emergence of ecosystems and innovations for processing big data effectively. Not only will IoT-based analytics be a high growth opportunity by itself, it will also be a key differentiator for organizations looking to improve business performance and remain competitive and relevant through the delivery of a new generation of personalized, outcome-based experiences. That will require companies to build new partnerships beyond their own entities and industries, and apply these advanced analytics capabilities in the broader ecosystems that will constitute the We Economy.
Arnab Chakraborty (email@example.com) is managing director of Accenture Analytics – Communications Media & Technology Analytics. Michael Svilar is managing director of Accenture Analytics – Advanced Analytics. Prith Banerjee is managing director, Global Technology R&D at Accenture.
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