For organizations on the road to digital transformation, embracing the world of IoT might seem like an obvious business move. And statistics prove that. According to McKinsey, IoT devices are geared to generate up to $11.1 trillion a year in economic value by 2025.
By efficiently analyzing hordes of real-time data, IoT is helping enterprises make better decisions, improve efficiency, boost customer experience, and drive growth.
With IoT, there’s a lot that organizations can benefit from. However, more than having a robust IoT data strategy in place, what organizations need to work on is a comprehensive analytics strategy. After all, it is through analytics that you can harness the power of IoT data and lay it as a foundation of successful IoT solutions. And unless you have your analytics basics right, there’s no way you can achieve success with IoT.
So, let’s dive into the world of IoT analytics – right away!
What is IoT analytics?
The primary purpose of any IoT device is to capture information and analyze it to enable better decision-making. For example, in the manufacturing space, IoT devices collect and analyze data from manufacturing equipment, the assembly line, smart meters, and more to improve operational efficiency, provide alerts, avoid equipment failures, ensure safety as well as reduce manufacturing costs.
All this is made possible only by IoT analytics. So, what is IoT analytics anyway?
- IoT analytics is a set of data analysis tools and technologies that unearth insight and value from massive volumes of IoT data.
- By filtering, transforming, and enriching massive volume of unstructured data, IoT analytics applies machine learning queries to perform complex analytics and inference.
- It collects, processes, and stores data from IoT devices and uses analytics algorithms to understand correlations and make predictions.
- Through modern analysis algorithms, it offers accurate and comprehensive information for business reporting and analysis.
What goes into IoT analytics?
The main purpose of IoT analytics is to analyze data captured by IoT devices, gain actionable insights, and drive higher throughput.
For IoT analytics to provide accurate and timely analysis, here’s what is needed:
- A robust and modern analytics solution to predict results, detect deviations and improve performance.
- A data management solution that gleans and cleans IoT data before storing it in a database for analysis.
- A scalable and flexible data storage solution that stores and manages the ever-increasing influx of data from devices.
- Compelling analytics solution with data visualization capabilities that make it easy to spot trends and take action.
- A robust reporting engine that delivers actionable intelligence in the form of reports and dashboards.
How can you get it right?
Given how important IoT analytics is, to the success of the IoT solution (as well as to the organization), here are some tips on getting it right:
- Understand the why’s: Before you jump into creating a strategy for IoT analytics, you need to first ask yourself: What do you aim to achieve by using this IoT data? Is it to improve customer service? Is it to reduce failures? Is it to improve safety? Is it to enhance performance? Clearly understanding why you’re collecting the data is important to get the most value of your IoT data.
- Live on the Edge: To drive greater benefits from IoT analytics, it makes total sense to perform analytics at the Edge – closer to the devices that generate the data. This can help in reducing latency – by running analytics algorithms directly on the devices, as well as in accelerating the decision-making process.
- Ensure privacy: There’s a lot of sensitive information that IoT devices analyze including customer preferences, GPS data, data from cameras, and more. Ensuring privacy of this analyzed data is a business prerogative. Make use of distributed ledger technologies like Blockchain to provide decentralized trust across the IoT network, and sure data privacy 24×7.
- Use efficient data management techniques: For IoT analytics to really triumph, efficient data management is important. Using software libraries like Hadoop for distributed processing of large data sets can allow for high-speed and high-volume data analytics with greater flexibility and cost-efficiency.
- Leverage the world of AI: In contrast to manually analyzing the massive influx of IoT data, AI can more efficiently and quickly process a wide range of IoT information. To get the most out of IoT analytics, leverage the world of AI, robotics, and Natural Language Processing to spot trends, understand correlations, detect anomalies, identify false positives, and more.
- Implement an analytics governance framework: With IoT devices dealing with so much of analytics data, there is a pressing need to have controls in place that oversee how the data is being used, and how misuse is prevented. Implementing a governance framework that encompasses auditing devices, updating firmware, software and security controls, disconnecting and deleting data from a stolen or rogue device etc. will go a long way in ensuring widespread success (and adoption) of your IoT solution.
Drive meaningful experiences
No matter how advanced sensor, mobile, and wireless technologies get, the true business value of IoT lies in analytics.
While data is an important aspect of the IoT ecosystem, without proper analytics, the mountain of data that is being captured every second is unusable. IoT analytics plays a major role in the success of an IoT device is. So, make sure to invest the right amount of time, money, and effort in building your IoT analytics solution, and extract insights to improve device performance and drive meaningful consumer experiences.
Prashant loves technology. While this passion helped him top his Masters class at IIT, he wanted to ensure his contribution remains “applied” rather than “theoretical”. So after working on a few patented technologies, he got an MBA and took up a Practice Head role. This allowed him to leverage technology and expertise, to craft and deliver award-winning software products and solutions. His key areas of interest are analytics and software engineering. He is very excited by the convergence of technologies such as social, mobile, and IoT, and how they are allowing us to redefine the way business is done.