Internet of Things IoT analytics enables organizations to leverage the massive amounts of data generated by IoT devices, using analytics stacks. IoT analytics is often considered a subset of big data, involved with combining heterogeneous streams and transforming them into consistent and accurate insights.
Insights generated by IoT analytics can help organizations improve many aspects of their operations. However, it is often complex to integrate the many types of IoT devices with existing ecosystems and analytics tools. This is why, insofar, organizations deployed Industrial IoT (IIoT), a technology built for collecting and analyzing data from sensors.
When sensors are placed on key manufacturing equipment, weather stations, delivery trucks, pipelines, and smart meters, organizations can properly integrate devices with analysis tooling. IoT data analytics can help other industries, like healthcare facilities and data centers, better leverage their data.
Why Use IoT Analytics?
IoT adoption has been on the rise and is expected to reach a peak of $1.29 trillion in spending by the end of 2020. However, while the number of connected devices increases, organizations struggle to handle the massive amounts of generated data. Because data collected from IoT analytics are less structured, the data sets are more complex.
IoT analytics tools help organizations successfully leverage complex IoT datasets. When organizations are better equipped to understand data, they can improve products and increase earnings. Since customers are connected to the Internet, organizations can optimize products according to customer needs.
For example, IoT analytics can provide insights into the popularity of features, average usage patterns, optimal battery capacity. Additionally, this level of connectivity and analysis helps organizations fix issues, releasing software updates over the air. When fixes are released quickly and timely, customer engagement and user retention increase significantly.
Instead of collecting and trying to use all data, sophisticated IoT analytics tools know how to collect the most significant data points, perform quick analysis, and provide insights relevant to the products and services. IoT analytics can save valuable time, reducing tasks related to the integration of data sources. The result is a data analytics pipeline that provides access to data. Ideally, any role in the organization can use the workflow to ask questions and gain insights.
IoT Data Analytics Challenges
There are many benefits to using IoT analytics. This technology can help reduce maintenance costs and equipment failures while improving customer experiences and staff productivity. However, there are challenges that might prevent organizations from successfully leveraging their data assets.
A key challenge in implementing IoT analytics is the technology and skillset required to access data. If only data scientists and analysts are able to gain access to data, and there are not enough roles in the organization, this might prevent the successful usage of data.
When data arrives in massive quotas, employees can get overwhelmed. If this happens, they might not be able to go through all the data, even though the information could significantly impact the critical operations.
Security is a critical concern for most digital operations. Data and analytics repositories are, particularly, sensitive and should be protected from external actors as well as insider threats (read more). IoT security is often complex because there are multiple connected devices, integrated with various networks, machines, and systems. If one device is breached, the entire network of connected devices could be compromised.
When it comes to data, compliance is always a critical aspect that must be addressed. Especially if the organization stores personally identifiable information (PII) or financial information. Non-compliance might result in monetary fines, reputational losses, and impact future business dealings.
IoT Analytics Strategy
When implementing IoT analytics, a strategy can prove helpful in ensuring the success of the project. There are many ways to adopt IoT analytics, depending on the industry and specific needs of the organization. Regardless of individual needs, there are certain best practices that should be followed universally. Here are key practices to follow:
Leverage automation—for data cleaning and profiling to quickly ensure data quality and accuracy and reduce the number of errors.
Match analysis components—determine the location of data analysis according to criteria such as type of data, the type of analysis, and existing analytics infrastructure.
Centralize data—for a deeper understanding of data, analyze IoT data along with other data sources.
Encourage employees—to leverage IoT data analysis, doing their own explorations into what and how can be gained from the data.
Enforce security—using policies and safeguards to ensure data protection and prevent the exploitation or loss of data.
Making data accessible to employees of all skillsets can be a detrimental challenge. However, there are tools that can help provide access to all decision-makers and stakeholders. These tools can aggregate, store, and manage diverse data types from multiple sources and sensors, smoothly integrating high volumes of data streams.
Ideally, IoT analytics tools are integrated with data intelligence capabilities for data ingestion, data transformation, stream processing, and data management and analytics. Here are key capabilities IoT analytics should provide:
Integration—seamlessly integrate with IoT platforms and enterprise stacks, load data, and then merge and manage multiple data types.
Deployment—provide deployment options for a wide range of environments, including on-premise installation, cloud hosting, and compatibility with complex hybrid ecosystems.
Customization—let employees easily access and explore data, as well as building personalized dashboards.
Collaboration—features for data governance and management, including capabilities for sharing analytics across workflows.
AI-driven IoT Analytics with MobiDev
Two years ago, industrial IoT processes produced 5 quintillion bytes of data daily. Having started 2020 with 9.5 billion connected IoT devices, now we are seeing continuous growth of the gathered data.
Applying data science and machine learning algorithms to IoT data, we are capable of getting valuable business insights and finding more opportunities to increase efficiency and reduce downtime for various businesses through methods such as predictive maintenance.
Machine learning algorithms can predict equipment failure by analyzing data streams and detecting patterns invisible to the human eye.