Best IoT Analytics Platforms

What are IoT Analytics Platforms?

Why Use IoT Analytics Platforms?

Who Uses IoT Analytics Platforms?

IoT Analytics Platform Features

The diverse platforms in this category offer a unique set of tools to assist with IoT data analytics. The following are primary features common in this category of software platform.

Data models and customization — IoT analytics solutions often come with data models for organizing and standardizing information generated by connected devices. Data modeling is useful for revealing relationships between large sets of unorganized data so users can draw conclusions. With some platforms, users can customize data models or configure entirely new models to fit their particular needs. Models may be useful for observing logical relationships within data sets, and determining how data is retrieved, stored, and formatted.

Ingestion and filtering — IoT sensors enable objects to generate limitless data; this increases based on the portfolio of devices in the network. IoT analytics platforms usually include filtering and ingestion tools, allowing users to collect the most relevant data points. When determining how data is ingested from IoT devices, users can decide whether specific types of data will be used immediately or filed away for later use. In some cases, users can create dashboards for real-time data streaming including location and what settings are most beneficial at the time.

Event scheduling and alerts — Along with determining which data should be collected, users of IoT analytics platforms can determine when to generate reports. Scheduling analytics readings could revolve around a specific time schedule, or particular events. Users might track IoT data in response to alerts such as environmental changes or equipment issues. In other cases, they may simply want to schedule data ingestion for a particular time to make basic observations about patterns and performance. Companies can elect to pull data in a variety of ways and adjust their analytics strategy over the course of their IoT campaign. The platforms in this category offer a number of configurations for reporting to suit these needs.

Potential Issues with IoT Analytics Platforms

Data gaps — In addition to taking systems offline at unexpected and inconvenient times, random lapses in connectivity cause inconsistencies in time-series data. For example, you may notice several hours between two data points where there would normally be a steady, uninterrupted time line of data. These random gaps can be a source of frustration when it comes to studying and drawing conclusions. To prevent this, IT experts should monitor edge networks and routers and proactively address any issues.

False or corrupted readings — The more end points a company adds to its IoT stack, the greater the potential for transmission issues from an individual sensor. This is an unfortunate risk of any new technology, when it comes to IoT, these possibilities are multiplied by the number of devices they enable. A false reading can happen for a number of reasons, inaccurate data point can corrupt the integrity of data sets. It’s important to perform audits on data and run as many tests as possible to quickly identify issues, before problematic devices contribute additional false readings. Regular software updates are critical to keep distributed smart devices updated, reducing the chances of incomplete or inaccurate data.

Software and Services Related to IoT Analytics Platforms

Realizing the benefits of smart technology is a significant undertaking. There is a great deal of planning, training, and investment that goes into an IoT initiative, including collecting and securing data from distributed platforms. Business leaders should do considerable research on the different types of IoT management software and services on the market, and work with a consulting firm to determine which solutions to prioritize. The following are professional solutions that complement IoT analytics tools. They all help companies collect insights about their modern systems, and optimize their operation using groundbreaking technology.

Stream analytics software — Stream analytics, also known as streaming analytics, is the study of data transferred between applications and exchanges, both historically and in real time. Stream analytics software enables the capture and analysis of this information. These platforms allow users to understand the flow and retrieval of data by disparate end points. A major use case for this is monitoring IoT telemetry across end points and the events pertaining to information exchange. If data is not received correctly, users of stream analytics tools can be notified and given insights into streaming issues. The platforms in this category are a natural complement to IoT analytics platforms, ensuring companies stay informed about the continuous flow of information between connected objects so devices can reliably execute tasks and generate their own insights.

Big data analytics software — Big data analytics involves the consumption of massive data clusters, culling the most notable findings using advanced data queries. Big data and big data software are increasingly common across a number of industries. With the rise of IoT, substantial data clusters are the focus of big data analytics and include observable information that can be exported from smart devices. If a business desires to analyze IoT data as part of a larger pool of data, they might consider a big data analytics tool such as those featured in this category. While IoT analytics solutions offer actionable insights into specific data generated by connected platforms, big data analytics can study these findings in relation to other critical data collected from various sources. This practice paints a more complete picture of relevant data patterns across the operation. Users can create detailed visualizations and record their queries into data sets to share discoveries with internal teams, business partners, and investors. Some platforms offer specific features for monitoring IoT data and collections of big data.

IoT device management software — Depending on the size and scope of IoT strategy, hundreds or thousands of devices are configured to work together and communicate information with one another and the network itself. Proper management, troubleshooting, and updates to these devices is essential for accurate data collection. This ensures each device performs the expected actions and generates the right insights. After designing and deploying an IoT network using IoT platforms, businesses may use IoT device management tools to monitor the state of their devices and maintain them as needed. At the very least, these platforms assist with pushing software and firmware updates required with certain devices and sensors. IoT device management tools provide assistance with device permissions and device vulnerability tracking, among other things. To receive the best possible data from smart devices, IT administrators need to make a consistent effort toward device tracking and organization. IoT device managers help prepare teams for success and ensure they maintain visibility and control over a company’s IoT infrastructure.

IoT security services providers — The power of IoT and device analytics comes paired with the responsibility of keeping these systems protected from unwanted access, theft, viruses, and other threats. Smart connectivity is a term used to describe the reliable connection of complex networks, including those used in edge computing and IoT networks. This connectivity requires reliable, uninterrupted transmission of data, and the ability to withstand environmental influences and malicious activity. Companies might require lots of outside assistance to supplement internal efforts and maintain a high degree of reliability. This is where IoT security services can help. IoT security providers are specialized firms that help companies configure IoT systems for optimum performance and consistency in the face of untold threats. Cybercrime is evolving, especially as new vulnerabilities are introduced. The specialists in this category can be enlisted to keep systems updated, adding or adjusting layers of protection based on the changing landscape of potential dangers.

When mapping out a security strategy, businesses might also consider data security services to bolster internal defense efforts. Data security experts can assist with protecting large volumes of critical business data, up to and including data sets generated by smart objects. A number of IoT security services providers offer solutions for protecting IoT data from external threats. A company might desire a more comprehensive program that touches on all aspects of data protection and the various groupings of data a business might collect. Data services providers can offer strategic advice, train employees on security practices, and assist with the implementation of security software so teams can maintain a consistent level of protection for data and company systems using internal resources. In some cases they can remotely monitor and respond to data-related threats in a business. In the current age, there is no limit to the security measures a company should consider, especially as operations are increasingly built on a foundation of technology. Compromised data, including IoT-related analytics, can be detrimental to a business and also place its employees and customers at serious risk. When adopting an IoT strategy, managers should take measures to keep assets and business data as safe as possible.