Over the past few years, there has been much hype surrounding the internet of things: both consumers and businesses strived to follow technological developments in the area of connected devices. But does IoT really merit attention or is it just overhyped?
According to McKinsey & Company, the potential economic impact of IoT would reach $11 trillion per year in 2025, which is equivalent to 11% of the world economy.
The numbers above highlight IoT’s huge potential to do a world of good and bring considerable financial value to its adopters. One of the factors enabling this is a combination of IoT and big data—IoT data analytics.
This article will provide answers to some of the most commonly asked questions about IoT data analytics:
Simply put, IoT data analytics is the analysis of huge data volumes generated by connected devices. Organizations can derive a number of benefits from it: optimize operations, control processes automatically, engage more customers, and empower employees. The combination of IoT and data analytics has already proven to be beneficial in retail, healthcare, telematics, manufacturing, and smart cities. However, its true value for organizations has yet to be fully realized.
At least a decade ago, it was exceedingly difficult and expensive to analyze massive volumes of information provided by a variety of connected devices. As time goes on, the cost to store data is coming down, while the analytics capabilities have made a huge leap forward. This creates favorable conditions for organizations to start investing in and implementing IoT data analytics.
Gartner ranked IoT data analytics as one of the main IoT-related technologies for organizations to invest in during 2017 and 2018, second to security. World-known companies such as Microsoft, GE, Amazon, SAP, and Salesforce have already started implementing IoT data analytics into their day-to-day processes.
A huge variety of devices connect to the internet and share data every day through sensors. This data is worthless without analysis. However, with an IoT analytics solution put in place, the data that organizations produce is effectively collected, analyzed, and stored. As a result, it allows organizations to optimize their operations at all levels, improve decision making, and achieve a number of benefits.
Some organizations install smart sensors throughout their facilities to collect data on employee engagement, performance ratings, and other work-related activities. This data is later used to improve day-to-day business operations and help utilize employee time and energy more efficiently.
As an example, Humanyze has developed a system that uses smart employee badges with sensors that capture more than 100 data points to measure the productivity of its workers. It analyzes how people interact, how much they gesture or listen, and what the tone of their voice is. This helped them find out that bringing call-center workers together for lunch at the same time as opposed to staggering them so that somebody can stay on the phone, significantly improved productivity:
Thus, this approach has a positive impact on employees’ productivity and overall business success if IoT data analytics is implemented properly. However, there is a privacy concern: almost 75% of employees worry that their employer is collecting data about them without their knowledge or consent.
The combination of IoT sensors and data analytics may help companies, especially in the manufacturing industry, to determine when equipment requires maintenance by measuring vibration, heat, and other important figures. Smart equipment can also send messages to operators about potential breakdowns, wear, and delivery schedules. This does not only facilitate regular equipment maintenance but also contributes to predictive maintenance. Sensor data is used to predict when assets need to be serviced which allows maintenance to be scheduled at the optimal time, thus reducing breakdowns and saving maintenance costs.
As an example, one chemicals company struggled with unplanned downtime due to multiple failures of equipment, occurring 90+ times per year, hurting production, driving up overtime labor costs, and frustrating workers. Once the company installed IoT sensors—along with predictive data analytical models—it reduced equipment downtime by 80%.
With IoT and analytics working in tandem, organizations can automatically control their processes that previously could only be tracked manually. For example, it gives manufacturers a comprehensive view of what’s going on at every point in production. This allows them to maintain a continuous flow of final products, identify bottlenecks in real time, and avoid defects. It also reduces the risk of human error.
General Motors, for instance, monitor humidity with IoT sensors to optimize painting. When conditions for painting are unfavorable, they send the detail to another place with more appropriate humidity levels, thereby reducing downtime and repainting needs.
Be it a retail shop or a healthcare center, each organization strives to create a better and more personalized customer experience. The implementation of IoT data analytics can help with this onerous task. IoT data reveals a wealth of customer behaviors and preferences that can be analyzed and used to build a more customized experience and predict customer needs.
For example, it can guide the shoppers to the jeans they have been looking at online when they enter the store and send them a personalized coupon to make the purchase in store that day. In addition, retailers, restaurant chains, and makers of consumer goods can use IoT data to do targeted marketing and promotions.
In healthcare, hospitals can use real-time IoT data analytics to manage increased patient traffic and improve general operational efficiency.
This is beneficial to both parties: consumers would gain more value through convenience and time saving, while organizations would increase their revenue and stay more attractive to customers.
The use cases described above should shed some light on the breadth of what organizations can achieve with IoT data analytics: reducing maintenance costs, avoiding equipment failures, improving customer experience and human productivity. Despite the proven benefits of applying IoT data, many organizations don’t derive value from their data assets simply because they don’t know what to do with the technology. For example, only 1% of data coming from 30,000 sensors on one oil grid turns into actionable insights, according to the McKinsey report.
At a time when organizations are looking to gain a competitive edge, the Internet of things offers an opportunity to gather even more information that can improve processes, drive innovation, and improve the customer experience. The challenge lies in understanding which data is most valuable and figuring out what do to with it.
Among 200 technology and business professionals surveyed, most respondents encountered the following IoT data challenges:
Other possible barriers to IoT data analytics adoption include security issues and high costs. For data analytics to deliver its maximum economic impact, these obstacles would need to be overcome.
For those considering IoT data analytics deployment, the first step is making sure to have a clear data strategy in place, which means a complete understanding of what data needs to be analyzed and why. Second, to generate maximum value from their data, organizations should have much bandwidth for data transmission and storage. Once data is aggregated, the biggest challenge of all comes into play: analyzing the data to derive actionable information. If your organization doesn’t have expertise in both data analysis and IoT development, a professional company is needed to take full advantage of IoT data technologies. Having all this in mind, organizations will be able to make their data analytics journey a profitable venture.