It’s great to be a data-driving business.
It’s commendable if you implement IoT technology in your enterprise.
It’s challenging to turn raw terabytes of information into meaningful insights.
Tracking and monitoring any aspect of business and every part of our daily lives have become easier than ever. Being on an island beach, top managers can observe the manufacturing process of Volkswagen Tiguan production in Germany in real-time! By utilizing modern technologies, humanity can estimate the real state of nature like water and air pollution around the globe and take actions to improve the situation. That all has become possible thanks to the data and the Internet global network.
However, companies bumped into a challenge:
The volume of data all the technologies produce is enormous and can’t be analyzed by people manually.
According to Amazon Web Services, data grows 10x every 5 years driven by network-connected smart devices. So the Internet of Things (IoT) makes enormous contributions to data growth, without taking into account the standard growth of information on the Internet. For example, international companies like Google, Facebook, Microsoft, and Amazon store at least 1,200 petabytes of information (which is equal to 1 200 000 terabytes).
So we have to face a question: “What should we do with all that information? Just store it and pay for the storage capacity?”
The answer is simple: “We should analyze it!”
And one of the greatest tools for that is data visualization. In the article, we find out what this term means, examine the reason why it became very popular in the 21st century, how to implement it in an IoT system through an IoT dashboard, and consider great examples of IoT data visualization implementation.
To visualize the data means to make charts out of numbers. The goal of data visualization is to provide a graphical representation of the data so analysts can identify patterns and trends. Data visualization shows great efficiency when it comes to large series of data. Its methods include graphs, bar charts, pie charts, status tables, maps, line graphs, scatter plots, and much more. These charts contain a lot of condensed information that allows analysts to see the data trends easily and make the right conclusions. Such conclusions can be priceless and bring new opportunities to businesses.
However, data visualization is a tool that is expected to be applied correctly. There are several questions you should ask yourself before building and implementing anything:
These common questions are not invented by our company, but we actively use them when working with clients. And we have to admit that the number of companies that look for IoT data visualization instruments for their businesses has grown. The reason is the increased flow of the data.
To examine the term “data-driven area” from all sides we divide this chapter into three parts:
Data visualization has been around for a long time. For example, you can analyze companies’ expenses for the previous month using a pie chart to understand the share of every expense category.
The world-famous stock exchange in the USA, New York Stock Exchange, was formed in 19 century. However, it was neither the first trading organization in the US nor the first company that issued stocks in the world. So such financial indicators like exchange rate, total sales, and stock price have a long history. Some of the tools to analyze these indicators are line charts, and histograms. This stage features human-to-human communication only.
In the 1980s, the spread of personal computers (PCs) for private use began, which gave rise to a massive demand for the Internet. The Internet, in turn, initiated the emergence of international giants like Google, Facebook, Amazon, and Microsoft that store enormous volumes of data about their users. Every click you make while surfing a website, every button you push, and every item to buy you look through is stored as bits of information in their databases to study customer behaviors and needs. Modern online marketing is based on personal information about users, and data visualization is something every market specialist works with. During this stage, people started to use the term “people-to-machine communication”.
Here the real data-driven area begins. The IoT network brought machine-to-machine communication that generates an incredible amount of data that cannot be processed or even stored using traditional methods. To process such massive data sets IoT uses Big Data methods, NoSQL solutions, cloud computing, and fog analytics (you can learn more about IoT architecture and fog computing in the whitepaper). IoT data visualization comes in handy here because there are no other options for people to analyze the data from IoT devices.
The IoT data is both valuable and worthless because of its volume. The trick here is to turn these massive data streams from a liability into a strength.
The Internet of Things dashboard (shortly, IoT dashboard) is a web page or web application that contains a visual display of IoT data on one screen. It provides at-a-glance views of key metrics that are essential to the whole system. In other words, an IoT dashboard displays meaningful visuals in a human-readable format that can be easily interpreted by a dashboard user.
Developers like to use IoT dashboards for two reasons:
We want to highlight the fact that the IoT dashboard and IoT platform are two different entities. A dashboard is a part of the sixth component of the IoT ecosystem that is called “an application component”, and it is a visual display of the metrics only. An IoT platform, in turn, covers all components of the ecosystem which means it collects the data from sensors, transfers them to the cloud or a gateway, stores, analyzes, preprocesses, encrypts, and, finally, displays.
Hence, an IoT dashboard is a part of an IoT platform in general and a part of an application component in particular.
You can see a simplified structure of an IoT ecosystem below.
Talking about a visual representation of the data, a logical question appears: what data to display? However, the answer depends on business goals and needs. It’s impossible to build a universal dashboard that fits every particular case – that is why good dashboards are mostly highly customizable.
So, let’s talk about what a good dashboard looks like.
A dashboard is a set of widgets that display some information that you consider to be meaningful for your business. So make sure that the widgets solve business challenges instead of just displaying condensed information from IoT sensors.
We have mentioned that visual data on a dashboard should be updated in real-time. An additional step here is to enable the adjusting of some parameters in real-time from a dashboard. Yes, the control option is not a function of a dashboard, but you probably will have some IoT management system behind the visual display. Interactive visualization is a very helpful tool for an analyst who can not only observe, but make some quick decisions and implement them.
Dealing with data involves data formatting like resizing, filtering, sorting, and data exporting. These functions are necessary.
All visuals on a dashboard should be meaningful, otherwise, you should reorder widgets, put some of them away or add them. However, sometimes some data is expected to attract attention immediately because of its importance. In such crucial situations, it’s good to have rules with strictly defined conditions when a dashboard should highlight data with colors, alert, notification, or in other ways.
Most dashboards contain numerous tools and widgets for various cases! Don’t be afraid to experiment with them and discover what sets bring better results. There are dozens of widgets at your disposal: scatterplots, interactive maps, bar charts, pie charts, line graphs, tables, timelines, and much more.
What decisions and conclusions are expected to be made after analyzing the dashboards results? A good dashboard should give clues to answer this or other similar questions. The visualization should be created in such a way it will give you a response to your questions or help to find an answer.
Good visualization is that which allows you to quickly grasp a high-level overview of data without the necessity to dive deep into analytics. Separate semantic blocks by white spacing. Do not use a large number of different colors, try to keep the dashboard in the same color scheme. Define the notification rules strictly so that the important alert will not be lost under a bunch of other less important messages.
A dashboard is a set of widgets, charts, and maps – analytics don’t have to memorize their arrangement and its content. So make sure that all captions provide additional information on what a graph displays in a digestible way.
The main reason why you should use IoT data visualization is that there are literally no other options for a human being to analyze such an enormous volume of data that comes from an IoT system. Besides that, IoT data visualization is an important part of the whole IoT system with its benefits like predictive maintenance, reducing operational costs, higher productivity, and much more. Instead of describing its benefits, we provide 5 examples of how this technology is used in different industries.
Let’s go back to the pollution topic. Nowadays, more and more people turn their heads toward this issue and start green initiatives aimed at raising awareness of this issue and improving the situation by implementing green technologies. One of such initiatives is a world’s air pollution dashboard that has been monitoring air quality all over the globe since 2014 with thousands of installed quality monitoring stations and IoT data visualization. You can look at an interactive map and see areas where the air is good, unhealthy, or even hazardous.
Wabash Heartland Innovation Network deployed hundreds of weather stations across the Indiana region in the USA to share information about the weather conditions and farmers’ assets. By using IoT data visualization, the company aims to make the densest agricultural weather network in the country. It has already covered a 10-country region, providing an expansive network of internet-connected sensors that aggregates and disseminates the data it obtains to all participants. By sharing information, farmers become more efficient, save time, and increase yields.
Arla Food is a multinational cooperative based in Denmark and the largest producer of dairy products in Scandinavia. Arla is committed to high standards of animal welfare, product quality, and safety. The company also looks for opportunities to use modern technologies to make its products and services even better. One of the areas is tracking and improving the journey that milk products take from сows to customers with the help of IoT data visualization. The company used the Power BI visualization tool along with other technologies like SQL and Azure to bring an idea of transparent delivery to life.
Grundfos is a Danish company with more than 19,000 employees globally and it is the largest pump manufacturer in the world. During the company’s growth, the Business Intelligence department became a bottleneck since all reports and dashboards for all data from the factory, Internet of Things devices, and ERP system go through this department. As a result, report development speed diminished.
The company referred to a cloud-based reporting and IoT data visualization tool to correct the situation. This decision sped up the reporting process as well as provided additional opportunities for visualization both for small analytic groups of users and on a bigger scale in the form of a dashboard that displays on a large screen on a production floor.
Siemens is a technology company focused on industry, infrastructure, transport, and healthcare. The company provides the next generation of data-powered customer service for the rolling stock industry in the U.K. and beyond. IoT Data visualization can bring great benefits to such a technology company as well. Siemens strives to empower its customers with resource-effective factories, resilient supply chains, smart electricity grids, clean transportation, and much more. By using the Grafana tool the company managed to observe the data from the trains in real-time and optimize their maintenance sequencing. Another case is building a temperature monitoring and control dashboard that helps to increase the lifespan of trains and decrease their downtimes.
SumatoSoft helps companies to digitalize their businesses and develops meaningful visualization for clients’ businesses. For example, we build a platform for monitoring the work of industrial (commercial) refrigerators online with instant alerts on urgent issues and management of historical data. You can look at the whole case study in the link.
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Get in touch with us for a free consultation. Let’s build a new product together.
As for keeping up with the advent of technology, the world looks for new opportunities to analyze the data that is generated in enormous volumes by computers and new technologies. We entered the data-driven area, and with the arrival of the Internet of Things systems and machine-to-machine communication traditional methods became unfit even for data storing and processing. The IoT industry faced a new challenge: processing and analyzing massive flows of data.
New technical solutions like NoSQL databases solved the issue of data processing, while IoT data visualization is expected to address the issue of data analysis. Analysts started to use the Internet of Things dashboards to aggregate the most valuable information in one place. Dashboards became especially popular in the IoT industry thanks to their responsive design and real-time data updates.
The IoT visualization was implemented to build various solutions for agriculture, supply chain, manufacturing, technology sector, and to develop a solution for tracking world air quality. That’s all sounds impressive, and we expect the implementation of IoT technology along with data visualization will rise.
Thanks for reading!
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Everything around us has become smart, like smart infrastructures, smart cities, autonomous vehicles, to name a few. The innovation of smart devices makes it possible to achieve these heights in science and technology. But, data is vulnerable, there is a risk of attack by cybercriminals. To get started, let’s know about IoT devices.
The Internet Of Things(IoT) is a system that interrelates computer devices like sensors, software, and actuators, digital machines, etc. They are linked together with particular objects that work through the internet and transfer data over devices without humans interference.
Famous examples are Amazon Alexa, Apple SIRI, Interconnected baby monitors, video doorbells, and smart thermostats.
When technologies grow and evolve, risks are also on the high stakes. Ransomware attacks are on the continuous increase; securing data has become the top priority.
When you think your smart home won’t fudge a thing against cybercriminals, you should also know that they are vulnerable. When cybercriminals access our smart voice speakers like Amazon Alexa or Apple Siri, it becomes easy for them to steal your data.
Cybersecurity report 2020 says popular hacking forums expose 770 million email addresses and 21 million unique passwords, 620 million accounts have been compromised from 16 hacked websites.
The attacks are likely to increase every year. To help you secure your data of IoT devices, here are some best tips you can implement.
Your router has the default name of make and model. When we stick with the manufacturer name, attackers can quickly identify our make and model. So give the router name different from your addresses, without giving away personal information.
If your devices are connected to the internet, these connections are vulnerable to cyber attacks when your devices don’t have the proper security. Almost every web interface is equipped with multiple devices, so it’s hard to track the device. But, it’s crucial to stay aware of them.
When we use the default usernames and passwords, it is attackable. Because the cybercriminals possibly know the default passwords come with IoT devices. So use strong passwords to access our IoT devices.
Use strong or unique passwords that are easily assumed, such as ‘123456’ or ‘password1234’ to protect your accounts. Give strong and complex passwords formed by combinations of alphabets, numeric, and not easily bypassed symbols.
Also, change passwords for multiple accounts and change them regularly to avoid attacks. We can also set several attempts to wrong passwords to set locking the account to safeguard from the hackers.
Are you try to keep an eye on your IoT devices through your mobile devices in different locations. I recommend you not to use the public WI-FI network to access them. Because they are easily accessible through for everyone, you are still in a hurry to access, use VPN that gives them protection against cyber-attacks, giving them privacy and security features, for example, using Express VPN.
There are software and firewalls like intrusion detection system/intrusion prevention system in the market. This will be useful to screen and analyze the wire traffic of a network. You can identify the security weakness by the firewall scanners within the network structure. Use these firewalls to get rid of unwanted security issues and vulnerabilities.
Every smart device comes with the insecure default settings, and sometimes we are not able to change these default settings configurations. These conditions need to be assessed and need to reconfigure the default settings.
Nowadays, every smart app offers authentication to secure the accounts. There are many types of authentication methods like single-factor authentication, two-step authentication, and multi-factor authentication. Use any one of these to send a one time password (OTP) to verify the user who logs in the smart device to keep our accounts from falling into the wrong hands.
Every smart device manufacturer releases updates to fix bugs in their software. These security patches help us to improve our protection of the device. Also, update the software on the smartphone, which we are used to monitoring the IoT devices to avoid vulnerabilities.
When we connect the smart home to the smartphone and control them via smartphone, you need to keep them safe. If you miss the phone almost, every personal information is at risk to the cybercriminals. But sometimes it happens by accident, makes sure that you can clear all the data remotely.
However, securing smart devices is essential in the world of data. There are still cybercriminals bypassing the securities. So make sure to do the safety measures to avoid our accounts falling out into the wrong hands. I hope these steps will help you all to secure your IoT devices.
If you have any, feel free to share them in the comments! I’d love to know them.
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Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.
Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.
Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.
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Disclaimer: The ideas presented in this article are from the book:** Story Telling With Data by Cole Nussbaumer Knaflic**. To preserve the original message of the author, the visualizations presented in the book are also directly from the book.
When I was reading and enjoying the book, I thought it would be really cool to share my most key takeaways with the rest of the data science community. The book highlights the best practices for communicating effectively with data. It’s has been almost two months having all these tips drafted in my laptop, but thankfully now I realized it could help some data scientists looking to deepens their visualizations and communication skills.
In your opinion, how good are the following visuals? Hold on for few seconds and see what they are all lacking.
What would say about these visuals?
The main best practices for making effective visualizations and communicating the data are:
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