For organizations on the road to digital transformation, embracing the world of IoT might seem like an obvious business move. By efficiently analyzing real-time data, IoT is helping enterprises make better decisions, improve efficiency, customer experience, and drive growth.
Through analytics, you can harness IoT data’s power and lay it as a foundation of successful IoT development services. 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!
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, provide alerts, avoid equipment failures, ensure safety, and reduce manufacturing costs. IoT analytics is a set of data analysis tools and technologies that unearth insight and value from massive data volumes.
What Goes Into IoT Analytics?
IoT analytics’s primary purpose 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:
- Robust and modern analytics solution to predict results, detect deviations and improve performance.
- Data management solution that gleans and cleans IoT data before storing it in a database for analysis.
- Scalable and flexible data storage solution that stores and manages the ever-increasing influx of data.
- Compelling analytics solutions with data visualization capabilities to spot trends and take action.
- Robust reporting engine delivers actionable intelligence in the form of reports and dashboards.
How Can You Get It Right?
What do you aim to achieve with the IoT data? Improve customer service? Reduce failures? Enhance performance? Understanding why you’re collecting the data is important to get your IoT data’s most valuable.
IoT devices analyze sensitive data like customer preferences, GPS data, data from cameras, etc. Ensuring privacy is a business prerogative.
Use Efficient Data Management Techniques
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.
AI can more efficiently and quickly process a wide range of IoT information. Leverage the world of AI, robotics, and Natural Language Processing to spot trends, understand correlations, detect anomalies, identify false positives, etc.
Implement Analytics Governance Framework
Implementing a governance framework that encompasses auditing devices, updating firmware, software, and security controls, disconnecting, and deleting data from a stolen or rogue device will go a long way in ensuring widespread success.