The concept of connected smart devices, known as the Internet of Things (IoT), has exploded in popularity. Organizations across all industry sectors look to take advantage of the proliferation of connected IoT devices.
As per Juniper Research, the number of IoT connections is expected to reach 83 billion by 2024. Rising from 35 billion connections in 2020, this represents a growth of about 130% over the next few years.
However, the success of all IoT initiatives depends on the ability of organizations to convert the continuous stream of data received into valuable business insights. Unless it serves a particular purpose, all this data becomes useless.
This is where data visualization can help. It can slice and dice large chunks of data into smaller visual forms that are easy to grasp. However, visualization of IoT data comes with its own set of challenges.
Let’s use this article to discuss more of these challenges.
Why is IoT data visualization important?
Before getting to the challenges, let’s discuss the benefits that IoT data visualization can offer. Data visualization can help unlock multiple insightful values. It can help deliver the following benefits –
- It helps create compelling visual experiences by translating the data into compelling visualizations and helping employees become more agile and collaborative.
- The data from various sensors can be used to deliver real-time data insights and contextualized information.
- Through device monitoring, users can engage in data in real-time and access system status, detect data anomalies, and provide effective guidance to employees.
- It helps make real-time decisions with the help of multi-layered visual data obtained by combining multiple data streams.
- It highlights new business opportunities by combining the new IoT data transmitted from sensors with existing data.
- It can improve IoT data flow by supporting the monitoring of IoT devices and infrastructure.
- It helps analyze multiple data correlations in real-time.
Challenges in IoT data visualization
Effective analysis of IoT data poses its own set of challenges. The data generated by sensors and sensor-enabled devices is different from the transactional data at the core of many organizations. It is usually less structured and requires a new set of tools to run an effective analysis.
Some other key challenges that businesses face in visualizing IoT data include –
- Data oversimplification
Although data visualization simplifies complex datasets through easy-to-understand visual representations, this data simplification can sometimes go too far. In order to confine the conclusions to a handful of pictorial representations, data visualization tools may miss out on certain key aspects or neglect certain modifiers that could completely change the interpretation of a big dataset.
- Human limitations of algorithms
The algorithms used to convert data into visual representations are ultimately based on human inputs. This is the biggest problem with data visualization as well as the most complicated. Since the human inputs can be fundamentally flawed, it could alter the visual outputs.
Here’s an example – A human developing an algorithm may highlight certain data pieces while discarding other pieces that they may consider unimportant. This creates visual elements that may be true in some contexts but untrue in others.
- Data quality
The quality of data is directly proportional to the quality of data visualization output. However, this data quality can be affected by how it’s stored, entered, or managed. Some key elements that determine the quality of data include accuracy, completeness, relevance, consistency across sources, reliability, appropriateness, and accessibility. Since these factors can be really difficult to ascertain in IoT devices, the quality of data is affected.
To ensure high-quality data, one requires a routine and regular review and evaluation of the data sources.
- Edge computing
IoT devices, sensors, and gateways are spread across different homes, manufacturing units, retail stores, and offices. This network of outspread smart devices is giving rise to the need for Edge computing.
As per IDC, about 40% of the IoT data will be processed at the edge by 2022. So, enterprises looking to visualize IoT data will need to come up with a plan to address this. It is going to be extremely tricky for large IoT deployments where billions of events may stream through each second.
- Knowledge gap
A surprising aspect that often prevents enterprises from using data visualization is a lack of knowledge about software and platforms that can help produce visuals.
Due to a lack of formal training, most data experts rely on statistical packages with limited visualization capabilities such as SPSS, STATA, spreadsheets, or word processing programs. Moreover, enterprises continue to rely on regression and correlation tables or simple bar and plot charts to display quantitative data.
Data visualization tools for IoT
The following are the top data visualization tools that are widely used across several industries globally. These tools can interpret the big data collected from various IoT devices and help organizations make better decisions by providing strong analytics data.
- Grafana tool – It is an open-source visualization tool that is built to consume time-series metric data. It offers a visual dashboard that covers multiple functionalities. It also supports various data sources seamlessly like MySQL, PostgreSQL, Elasticsearch, and Prometheus, etc.
- Kibana tool – It is an open-source visualization tool that can be used to analyze large volumes of log data. It also contains interactive dashboards that can be easily converted into reports for future reference.
- PowerBI tool – It is a popular business intelligence tool from Microsoft for real-time data visualization. Like many of its predecessors, PowerBI can provide detailed analysis reports for large enterprises.
- Tableau – It is one of the most popular interactive data visualization tools. It offers an extremely intuitive interface, delivers powerful analytics, is easy to learn, allows integration with multiple data sources, and can manage large data volumes. All these and more such features make it a popular choice for world-leading companies like Bank of America, Amazon, Burger King, and EY.
With the rapid growth of IoT devices, huge amounts of wireless sensor networks in various industries like healthcare, energy, transportation, etc. produce continuous data streams. Visual analytics tools can play an important role in generating valuable knowledge from these data streams in real-time to support critical decision-making.
However, visualizing IoT data comes with its own set of challenges. In case, you’d like some help in visualizing IoT data then Heptagon can be of help.