IoT analytics companies make businesses more productive

Utilising IoT analytics allows your business to make confident, data-driven decisions

IoT analytics solutions are the future of optimized workflow

Building data infrastructure and IoT analytics from scratch allows us to extract, process and store massive volumes of IoT data efficiently to support your business in its day to day operations.

Boost your data utilisation with IoT analytics

We help you uncover the potential of flawless IoT data processing

Improved crucial decision making. IoT analytics let your business receive real-time, actionable insights as soon as they’re available.
High-level, better automation. Use IoT analytics for better automation, like increased uptime and operational efficiency.
Easily accessible data. Let us do the hard work by constructing the foundations of data analytics for you to build on.
The perfect data infrastructure. Companies produce huge amounts of relevant data, but we can identify which data deserves priority.

How can intelligent video analytics improve retail?

Every day, retail stores waste petabytes of CCTV video data that could be analyzed to improve stores.

IoT analytics companies can extract, transform and analyze data from store cameras that would normally go to waste.

Using machine learning, video feeds can be set up to do things like alerts for more cashiers when there are large queues or notify manage.

Read Case Study

We’ve had a long-term relationship with Netguru. Netguru is a great and super-professional service provider, which brought new technologies, new methodology, and a fresh perspective to our project.
Assaf Davidi
VP Product at temi

Choosing the best IoT analytics solution for your business is crucial

IoT analytics provide advantages such as robust architecture and better data management

Data Architecture & Pipelines Design. Designing data architecture and pipelines from scratch to provide flawless IoT data streaming
Data Architecture Consulting. Assisting customers who already have IoT architecture to build solutions and process data
Migration Services. Handling data migration from the previous data infrastructure to the new purpose-built one
ETL Integration. Integration of the existing ETL tools and workflows with new dedicated solutions

Using a formalized process to uncover data potential

Discovering IoT data management processes that could be improved and sensor data analytics are two key results of IoT analytics companies delivery. Understanding the benefits of the result vs the cost of implementation is a crucial step.

Discover. Identifying high business value opportunities and setting a goal roadmap to achieve them
Assess available data. Use 5Vs industry standard for detecting Big Data problems and assessing the value of available data
Assess business impact. Charting impact versus feasibility helps visualize the trade-off between costs and benefits of the IoT analytics solution
Design. Design a solution for the business goal and recognise how it should be integrated into existing processes
Prototype. Implement a PoC for business goals and understand how it can be brought into existing processes
Deploy and improve. Deployment of the data infrastructure and refinement of analytics confidence for IoT analytics problems that require highest possible fidelity

Holistic process of data processing

Reach the full potential of your business data: from data strategy to active machine learning models.

The data processing flow is mapped out by IoT analytics companies using data science to make it as simple as possible.

IoT data analytics services allow us to do more than consult on data infrastructure development. These foundations can be improved by further implementation of machine learning to model IoT data or a digital twin network to create digital representations of physical IoT devices.

What can IoT analytics companies do to support your business growth?

What exactly is IoT data, what does it look like and how can IoT analytics companies help you implement it in your business?

What is IoT data?

Contents

The Internet of Things is the network of billions of devices around the world all now connected to the internet, capable of sharing information between each other.

Any device can become an IoT device capable of communicating IoT data with others. The only addition needed is a sensor to collect IoT sensor data and the ability to connect to the internet.
In recent years, huge strides have been made in this area and by 2025, IDC predicts that over 46 billion devices will make up the Internet of Things. These devices all produce IoT data, and this data can vary depending on the specific IoT device.

When a device produces IoT data, this data flows from sensors into a data lake, where engineering data takes place. An example of a device producing this kind of data may be a driverless truck that produces all kinds of IoT sensor data, from fuel levels to traffic reports.

Using IoT big data, companies can run analytics simultaneously to the data collection, supporting real-time decision making and providing advantages in business optimization.

IoT data management and infrastructure

Due to the massive amount of incoming data from IoT applications, IoT data management and infrastructure is top priority. By managing IoT data collection correctly, organisations can utilise these sensor data analytics to improve and automate aspects of business in various ways.

One of the most important aspects of IoT data management is scalability and integrations. Due to the aforementioned large amounts of IoT applications, increases in data inflow must be able to be dealt with seamlessly, prompting the need for excellent scalability.

In addition to this, the types of IoT devices vary wildly. This means that all these different types of data must be integrated by the IoT data management system in place.

Once this data has been collected and integrated, it can then be used for IoT data visualization, allowing companies to look at IoT dashboards with real time analytics to inform future decisions regarding the current state of the company.

What are IoT challenges?

Although IoT analytics platforms are incredibly useful tools, there are some IoT challenges that come with the implementation and upkeep of big data analytics services.

For example, data volume within the IoT ecosystem can be a big problem if not dealt with correctly. IoT devices produce hundreds of gigabytes of data every day, and being able to deal with and sift this data is an essential aspect of every IoT system.

IoT challenges also include the upkeep of elements within the IoT ecosystem, such as the metadata of the various machines and devices connected to the IoT network.

Businesses must work with an IoT software development company to choose whether they want to process the incoming data in real time, or whether they would rather do it in batches.

Each solution comes with its own challenges, but the important thing is to recognise and account for these problems. IoT consulting companies can help your business overcome these problems to create an efficient and effective IoT solution.

What is a digital twin technology and how does it combine with IoT?

Digital twin technology refers to the digitized representations of physical devices that input into an IoT system. It’s essentially an entire virtual model reflecting a physical object, such as a machine or device in a factory.

Digital twin technology can be used to monitor and visualize the state of the asset using the incoming physical data stream. Digital twin software is an incredibly useful tool that provides machine operators with crucial information, such as when to switch the devices off for maintenance, when a particular part or aspect of the machine is malfunctioning and when the machine may be performing at a sub-optimal efficiency.

This allows engineers to perform predictive maintenance using a digital twin model, increasing the overall efficiency of the operation.

Combining digital twin technology with IoT allows for the most efficient machine and device management possible, as incoming data streams from all devices within a network can be simultaneously monitored using digital twin software.

Data management success stories

With our IoT expertise, clients have improved and implemented data systems for a variety of processes designed to provide multiple solutions. These include our client, Temi, and our concepts like carLens or using LoRa platform for real estate IoT.

The Ultimate Personal Assistant Temi

With our expertise in AI and native app development, we supported system development for the personal home assistant Temi.

Israeli company Roboteam teamed up with us to create a light-weight, personalised home assistant with all the modern tech a robot could ever need. This included facial recognition as well as voice ID and state of the art emotion detection system.

Building a robot from scratch takes a lot of hard work and expertise. However, with us on their side handling voice recognition, video loads and fast data processing, Roboteam were able to create a final product that surpassed every expectation.

Read Case Study

Improving smart real estate with LoRa

Using IoT data management and analytics solutions, we were able to revolutionise the real estate industry.

The real estate industry may seem to be one of the industries who could use IoT technology the least. However, Lora is a perfect example of why this is wrong.

With Lora, real time real estate data can be transmitted from properties across the country straight into the cloud.

This allows commercial real estate businesses to simplify property management and upkeep as well as maximise savings and increase property values, all using our integrated IoT technology to track sensors and devices.

Read Case Study

A smart app for car lovers everywhere

Combining machine learning technology with optimal image analysis.

carLens is every car lovers’ dream app. Using cutting edge machine learning technology combined with image analysis, carLens can recognise car makes and manufacturers from images.

We trained machine learning to recognise car models and classify them based on images by feeding the model thousands and thousands of images of cars. If combined with the latest in AR technology, these cars can be seamlessly visualized in the environment around us.

Read Case Study

carlens application view on a smartphone

See how our support helped those companies

My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.
Gerardo Bonilla
Product Manager at Moonfare
Whenever we faced challenges this year, we could rely on Netguru for our urgent staffing needs and time-critical deliverables. The Netguru team has gone above and beyond any expectations of what a strong and reliable partner can be.
Hima Mandali
CTO at Solarisbank
Working with Netguru has been a fantastic experience. We received a lot of support in terms of thinking about how we track metrics, how we design this properly, and how we build the architecture. We are extremely grateful for making our platform what it is today.
Manon Roux
Founder at Countr

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Everything you need to know about our approach to IoT analytics

Not everyone is familiar with IoT, so here are some common FAQs

What is the role of data analytics in IoT?

Data analytics in IoT allows businesses to gain valuable insights into system data to inform crucial decision making. Patterns and trends can be identified and accounted for, optimizing not just operations but maximising profits.

Without data analytics, IoT simply isn’t realising its full potential. Using data analytics, businesses can make decisions with confidence by implementing data-driven decision making.

How do you analyze IoT data?

Gathering potential data to analyze is easy, but the analysis itself can prove harder. IoT data in particular is highly unstructured, making it tough to process.

There is also huge amounts of data, meaning that automation is necessary if we want to analyze all the data we collect every day. This is done by software that can collect incoming data streams for further analysis of trends and problems that may be arising.

How many types of analytics are needed for IoT?

There are 3 main types of IoT platform analytics. These include descriptive analytics, predictive analytics and prescriptive analytics, with each one carrying out its own unique function.

Descriptive analytics is the most basic form of analysis, allowing users to describe incoming IoT data for quick measurements.

Predictive analytics attempts to model future trends by analyzing historical data and prescriptive analytics helps businesses decide the future direction to take the company based on a question.

What are the most important aspects when planning an IoT analytics project?

When first considering the implementation of Iot analytics solutions, there are some important aspects to consider.

These include factors such as potential cost-benefit scenarios, asking yourself whether the benefits the technology would bring to certain aspects of the company will outweigh the costs of implementation.

Another important one is the decision what kind of data you want to be analyzed and visualised, as well as the end user experience.

Do I need clean data from the start?

No, you won’t need to clean your data from the start. Part of the implementation process of IoT solutions involves cleaning data.

IoT analytics companies such as Netguru support you in the extraction, processing, storing and analyzing of your data, meaning that you don’t have to worry about cleaning your data from scratch.