Over the last decade, data analytics has exploded from a niche field with narrow applications and a hefty price tag to a broad set of more cost-efficient processes applicable in dozens of industries.
Industries such as manufacturing and retail already are leveraging valuable data generated by billions of devices to provide new levels of operational and economical insights. And as tools like machine learning and automated algorithms come online, we seem to have arrived at a critical juncture in the evolution of the Internet – this is where the Internet of Things (IoT) enters the picture.
IoT is the network of smart devices, from industrial machines to consumer goods, “talking” to each other and working in the background, without the need for human intervention. Working hand in hand, big data and IoT represent a transformation of the way in which our world will be interacting in the very near future. Much like the World Wide Web connected computers to networks and the next evolution connected people to the Internet and other people, big data and the IoT appear poised to connect devices, people, environments, virtual objects, and machines in ways that only sci-fi writers could have envisioned just a few decades ago.
By 2020, analyst firm IDC predicts that between 20 billion and 50 billion devices will be connected to the Internet, with only one-third of those being computers, smartphones, and tablets. The remaining two-thirds will be devices such as sensors, terminals, household appliances, thermostats, TVs, automobiles, production machinery, and many other “things” that, traditionally, have not been Internet-enabled.
The advantages of gleaning and analyzing big data from all of these connected machines and appliances are being felt in nearly every industry. In the retail world, for example, IoT data can help companies create a closer relationship with consumers, giving brands the ability to instill deeper loyalty and the chance to influence future purchases. Additionally, a better user experience for the customer can be achieved. These technologies also help companies easily remind customers to replace parts, or alert them of service needs. In the mining industry, which operates in harsh environments very different from that of retail, data analytics and the IoT can provide the following enhancements:
- Safety. Location and proximity sensors can be used to track equipment and miners to minimize the chances of either one being in the wrong place at the wrong time.
- Independent operation of costly trucks. Remotely connected trucks with hundreds of sensors and guidance systems on them can operate 24 hours a day without the safety issue of human fatigue.
- Data collection/visualization. Sensors can collect real-time geological and equipment data that can be used on site by operators, as well as in operation centers that can be located in faraway locations, or even other countries, to streamline mining operations.
- Maintenance. Sensors can provide predictive models with data that can be used to schedule pre-emptive repairs on equipment, avoiding dangerous and expensive equipment failures.
Big data advancements have affected many other important industries, ranging from utilities to insurance to politics. In fact, more and more innovative use cases are emerging daily, and new insights are helping a range of industries around the globe to accelerate growth. These diverse scenarios are key to the continual growth of data analytics moving forward. Here are some examples of innovative use cases:
- Smart meters for household energy/water consumption. One important use case involves the use of smart electric meter data flowing at each second to understand the various devices used by a particular household. This process involves breaking down these devices’ energy usage using Fourier analysis (the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions). Once the information about the devices is understood, and given that the dynamic pricing of energy companies is understood, suggestions can be pushed to customers enabling them to use less energy and cut utility costs. The energy company can also help customers understand which of their devices are performing less efficiently than similar devices in their home – this can potentially identify faulty devices, prevent accidents, and lead to better energy usage overall.
- Automobile insurance services. This industry is now consuming telematics data coming from cars to understand the risks to a person’s life, car, and more by analyzing driving behaviors. For instance, if a driver consumes alcohol while driving, the insurance company can be watching via sensors imbedded in the car. In addition, the insurance industry can detect fraud through anomaly detection algorithms without much human intervention. These are self-learning algorithms that improve their performance over time.
- Political campaign analysis. Traditionally, political analysis was focused mainly on predicting election results, but data analytics and business intelligence tools are now being used to understand voter perception. Increasingly, political parties are designing local campaigns using text analytics or social media data and analyzing offline data from user surveys. In addition, pre-poll online and offline surveys around voter sentiments on various topics and candidates can help political parties strategize a campaign and understand topics that catch the voter nerve. Contents of political speeches also can be curated using the insights.
With use cases and applications that provide benefits to nearly every industry, big data analytics is here to stay. From our driving habits to how we function both at home and at work, the IoT is collecting data like never before – and it stands to have a huge impact on our daily lives in the years ahead.
Dr. Anil Kaul is the CEO and co-founder of Absolutdata. A prominent and well-known personality in the field of analytics and research, Anil has over 20 years of experience in marketing, strategic consulting, and quantitative modeling. Before starting Absolutdata in 2001, Anil worked at Personify and McKinsey & Company. He has a Ph.D. in quantitative marketing from Cornell University.
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