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IoT Data is an Untapped Goldmine


The Internet of Things (IoT) has exploded over the last several years as the technology for connecting devices, appliances, and homes has grown unlike ever before. All of these devices are producing enormous amounts of data at levels companies appear ill-equipped to handle.

Managing the volume of data that comes from connected, smart device is a challenge for companies, but they’re finding it is worth the effort because the value of the data might even exceed that of the very products that generate it. Providing support for companies trying to effectively sort the IoT data and extract the valuable analytics that result was a focus at a recent tech conference.

Mobile World Congress

The annual Mobile World Congress held in Barcelona is historically a gathering of the world’s largest telecom carriers and their suppliers. Recent industry trends and the impact of IoT specifically has led many at this conference to focus heavily on connected devices, wireless networks, and all of the data being generated by the IoT. Consumer, industrial, and automotive data and how to support it was a major theme at this year’s conference.

mobile world congress 2013

Participants at the Mobile World Congress also addressed the revolutionary potential of this data and the current state of companies to presently handle it. Many organizations, even those that are tech leaders, are ill-equipped to deal with the sheer volume and resulting analytics of this data.

Considering there are now billions of connected devices communicating and sending data all of the time, companies need up-to-date cloud network architecture, ultrafast connections, and the processors to succeed. 5G networks that could support this traffic were also a focus. The networks and their suppliers have recognized the value and impact of this transformative data not only to their own businesses but to the end users of the data as well.

IIoT Challenges

Industrial IoT (IIoT) technology providers attending the Mobile World Congress were open and honest about the challenges they are facing. What’s become apparent is that, despite the technology providers commitment to grow and support the IoT through connected devices, even the largest and most technologically advanced of the group were not prepared for the influx of data. Humera Malik, founder, and CEO of Da-Uh (an IIoT analytics solution provider) was quoted as saying, “One of the biggest surprises, when you work with Fortune 500 companies, is how far behind they are in their data readiness. There’s a huge opportunity in putting data together in a way to make the data useful.”

IIOT Data Value

Helena Schwenk, manager at IDC European Big Data and Analytics, proposes that

“Successful IoT solutions are those that will be able to convert data flows from sensors, devices, and endpoints into valuable business information, enabling organizations to automate key processes, create new products and services, and become more intelligent and connected entities.”

The data could serve to drive corporate directives, initiatives and even help with product innovation. Few at the Mobile World Congress challenged the inherent value of all of this data to any organization. It was just a question of how to wrap their hands around it, sort through the noise, and extract valuable data in a meaningful way.

The potential to share data across industry and organizations adds additional value. The data may belong to a customer, but the aggregate analytics are what really matters. Keywell posits,

“You don’t need to own the data to create insights across industries.”

A tire manufacturer, for example, could provide invaluable insights with its data for transportation businesses or construction companies. The possibilities for how the data is used become boundless.

Takeaways for Corporate Leaders and IIOT professionals


  • Companies are hard at work gathering experts to manage the IoT data and take responsibility of the information across all areas of business.
  • Many companies – even large, technologically savvy corporations – lag behind in terms of readiness meaning there is still significant opportunity to put all of this data together and make it meaningful and useful.
  • The value of the data generated by IoT devices and sensors could end up surpassing the value of the underlying products themselves.
  • Infrastructure to support the transfer of IoT data will be a key contributor to any organizations success in managing and extracting value from it.
  • There is as much opportunity to provide data to additional parties or impacted industries as there is to utilize the data in-house.
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Machine Learning

computer servers

Machine learning is a branch of artificial intelligence that takes advantage of advanced statistical techniques to give computers the ability to learn from data without being explicitly programmed to do so. It lets a system spot patterns and quickly make decisions with little to no human intervention.

How Machine Learning Has Evolved Over Time

Machine learning isn’t a science that is completely new. However, it has gained a lot of momentum lately due to the availability of more powerful computing technologies. Machine learning started out from simple pattern recognition and a theory that computers can be made to learn on their own by being shown data, even if they weren’t programmed to carry out specific tasks with it. Thanks to machine learning, systems can continuously adapt when presented with new data.

two people checking some data on their computers

The algorithms behind machine learning have been around for decades. What has changed lately is that we now have the ability to apply even more complex calculations to big data faster than ever thought possible. Examples of machine learning applications many people are familiar with include: automatic driverless cars, fraud detection systems used by credit card companies, social media sites showing trending topics and online shopping sites recommending items to their users.

Machine Learning Growing in Popularity

During the last few years, there has been a vastly growing amount of interest in machine learning from organizations in various industries. This is mainly due to the availability of big data, more powerful and cheaper computers to process the data and the affordability of information storage solutions.

Businesses can now work with larger and more complex sets of data, all while expecting fast and accurate results. By building the right model, an organization can take advantage of the power of machine learning to spot profitable opportunities while reducing exposure to risk.

Who Uses Big Data?

Industries that routinely deal with large quantities of data have quickly realized just how valuable machine learning technology can be. It can provide them with valuable real time insights that lets them work more efficiently and be more competitive. Here is an overview of who uses machine learning and how they’re applying it to their industry:

some graphs on a laptop

Sales and Marketing

If you’ve ever seen a website recommend an item based on purchases you’ve made before or similar items you’ve looked at during a past visit, it means they were using machine learning to promote items you’re more likely to have an interest in.

Marketers are able to use machine learning to acquire data, analyze it and use it in a way that results in more customized promotional campaigns. The result is advertising and marketing materials that are more relevant to shoppers, making them more likely to make a purchase.

Health Care

The health care industry is adopting machine learning at a rapid pace, mainly as a result of the development of wearable medical devices and sensors providing real time information on a patient’s health. Data collected by these sensors can be very useful for medical professionals, as they can use it to identify trends or find ways to improve treatments.

Financial Services

Machine learning lets businesses in the financial industry make sense of their data by rapidly identifying the most important insights. This can lead to finding previously unknown investment opportunities or help investors by showing them the most profitable trades at crucial moments. Machine learning can also analyze patterns in data to identify high risk clients or transactions, which may be used to prevent fraud.

Government Agencies

Government agencies, like public utilities, are able to benefit from machine learning as they often have large amounts of data coming from a variety of sources. This data can be analyzed in various ways. For example, information coming from sensors can help utilities find ways to prevent waste, boost efficiency and save money.

Popular Machine Learning Methods

There are various machine learning methods out there, but the two most popular are supervised learning and unsupervised learning. Here’s how they work:

machine learning types

Supervised Learning

Supervised learning uses labeled examples to train a system. It’s used in cases where a desired output is already known in advance. As it learns, the algorithm receives many inputs together with the right outputs. It perfects its model by comparing its own output with the correct one to spot errors.

Supervised learning is often used in applications where past events are likely to predict future ones. For example, it can help an insurance company determine which customers are likely to file a claim in the near future.

Unsupervised Learning

In unsupervised learning, the system doesn’t know which answer is the right one and must figure it out on its own. It will have to explore the data and find some kind of structure within it. A popular example of unsupervised learning is systems being used for marketing campaigns identifying customers with similar attributes and segmenting them so they can receive the right kind of marketing materials.

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Big Data Use Cases Explained

big data use cases

Big data isn’t just a catchphrase. The reality of using high-end computers to crunch unimaginable volumes of data in pursuit of insights that can mean greater profits has developed to a point where it is hard to fathom an industry that wouldn’t benefit from the process. And if “greater profits” is too general a term, let’s get more specific. Big data applications can reduce processing flaws, increase efficiency, improve production quality, as well as save time and money. To get a sense of the possibilities, let’s take a look at a few specific big data use cases.

Before We Start

While the ways in which big data can be useful to an industry are essentially unlimited, unless you approach the process with a specific business challenge to be addressed, there’s a good chance you will end up wasting time and money. While the power of data acquisition and analysis is a mighty tool, unless you have a tightly focused query, useful insight will be hard to find.

Use cases, like those we are about to reveal, provide real world scenarios that illustrate the value to be found in spending the time beforehand to come up with a targeted question. You need to learn to think tightly and specifically when formulating a question for big data to chew on.

For example, asking where the next big market for your product will likely not yield as useful of a response as asking who in the US is more likely to buy more of that product? Ultimately, big data focuses on finding patterns and examples, thus coming up with appropriate questions that play to this strength is critical.

Product Design Customization

Without naming names, one example of using big data comes from a $2 billion company that engages in product manufacturing. With big data analysis in place, this company decided to focus on the behavior or repeat customers. At the heart of the attention lies the long-held 80/20 rule of business, which decrees that approximately 80% of a company’s sales come from 20% of its customers. By focusing on discovering the habits of this critical minority, it stands to reason that more profits could be generated overall.

One of the gold nuggets uncovered was that the company lost productive manufacturing time while waiting for contracts to be signed. By insuring that the necessary paperwork was always complete ahead of time, the result should be more uptime, productivity, and profits. Another result of the big data process in this example was a shift to lean manufacturing, which advises how to cease production of what the customer doesn’t want and concentrate primarily on what they do want.

Improved Manufacturing Process

The following is an example of successful big data use from the pharmaceutical industry. This company manufactures vaccines and various other blood components. In order to insure purity in the end result, they tracked 200 different variables. On the surface, that sounds like a pretty impressive effort to maintain a high quality in the final product. The problem, as big data analysis pointed out, was that this intensive quality assurance process still allowed for a yield variation from 50 to 100 percent. This level of inconsistency is enough to draw attention from federal regulators, which is bad news.

The analysis was able to separate and identify nine parameters that directly impacted the quality of the final vaccine yield. The rest of the 200 variables were essentially eliminated. The end result was that the company was able to save time in the testing process, increase production by 50 percent, end up with a higher quality yield, and save $5 to $10 million per year.

Fortifying the Supply Chain

Here’s a nifty way one manufacturer found to use big data to make sure they got their raw materials no matter what, even in the face of tornadoes, hurricanes, earthquakes, etc. Through various predictive applications, the company was able to calculate the probabilities of delays at various points in the supply chain, then make arrangements beforehand to identify backup suppliers. This is how you use big data to guard against downtime from unexpected natural disasters.

Better Testing = Higher Quality

A normal Intel computer chip used to go through 19,000 tests before it was cleared for sale. As you might imagine, this was a huge but necessary chunk of time and money dedicated to maintaining high quality standards. But you can bet that any company CEO would give his or firstborn child (an exaggeration!) to be able to cut down on testing time without quality taking a nosedive. Big data to the rescue.

By an exhaustive analysis that began at the wafer level, Intel was able to throw out a large number of their standard tests and focus on those that specifically yielded the most value. The savings on a single line of processors was $3 million in the first year, which is expected to grow to $30 million once fully implemented.

The Bottom Line

The preceding examples are just a few of the dozens of use cases that could be cited. Once you narrowly define the problem and turn loose powerful computers on a big pile of data, you might be surprised at the creative solutions uncovered to problems you didn’t even know you had.

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Exploring the Data Acquisition Industry

For such an innocuous term, data acquisition (DAQ) is well on its way to being a billion dollar industry. DAQ is the process by which a company measures sound, temperature, pressure, voltage, current, or other physical and/or electrical phenomena. Though it wasn’t so long ago that these measurements were taken with simple mechanical devices and a chart recorder, the Computer Age has changed all that. You would expect a modern day DAQ system to consist of sensors, measurement hardware, and programmable software on a PC. Who are the top 10 vendors in the DAQ market? Grab a seat and we’ll give you our opinion.

Campbell Scientific

As a major vendor in the global DAQ market, Campbell Scientific produces systems intended for survival in rugged conditions associated with long-term, unattended monitoring. Typical applications would include collecting data from machines, soil, weather, water, and energy. As an industry leader, expect Campbell Scientific to stay on the forefront of developments in the PC-based world of data acquisition.

Rockwell Automation

If you’ve ever eaten food, there’s a good chance an integrated system from Rockwell Automation might have had a hand in the process. Though the company specializes in process manufacturing (recipes and formulas) related to large-scale food production, you can also find them at work in other industries like oil and gas, mining, metals, and life sciences.


Dewetron’s niche in the DAQ field is all areas of research and development, as well as specialized data acquisition instruments and custom built solutions related to the automotive, energy and power, transportation, and aerospace industries. With a focus on always moving the DAQ field forward with state-of-the-art technology advances, Dewetron is part of the larger TKH group and is headquartered in Austria.

Yokogawa Electric

Based in Japan, Yokogawa Electric has earned a solid reputation in the design, manufacture, and sales of information technologies, control systems, and measurement solutions. With a century of experience behind it, this company has managed to successfully reinvent itself to stay in step with changing industrial demands. For today’s world, Yokogawa Electric continues to satisfy clients with top-notch products and service, while emphasizing environmental sustainability.

Honeywell International

Make no mistake, Honeywell International is a multinational conglomerate with fingers in a whole bunch of different industrial pies. One of those pies happens to be DAQ. As well as the usual measurement and control products, this company enjoys a high demand for its circular chart and paperless recorders. Honeywell International presently focuses in particular on energy, safety, security, productivity, and global urbanization.


As an ISO 9001-2015 certified company, Pentek prides itself on the manufacture and sales of cutting edge DAQ solutions, with a specialty in digital signal processing and software radio applications. You can expect to find Pentek embedded chips in rugged environments associated with military and defense applications. The ISO 9001 certification assures clients that Pentek products will meet the most rigorous standards for performance.


With 15,000 employees and locations in 30 countries, one could say Ametek has created a rather large footprint in the DAQ industry. Equipment in high demand from this manufacturer includes programmable power equipment, industrial battery charges, analytical instruments, electromagnetic compatibility test equipment, and gas turbine generator sensors. Ametek is a leading provider of systems to the aerospace and defense industries.


This UK company might have a name that reminds us of a secretive James Bond organization, but Spectris is anything but fiction. Specialty equipment includes a focus on improving productivity, streamlining processes, and delivering higher quality for laboratory and industrial applications. A corporate focus that encourages employer entrepreneurism keeps cutting edge products always in the pipeline.

Keysight Technologies

As another company that provides measuring equipment to the aerospace and defense industries, Keysight Technologies prides itself on not selling one-size-fits-all solutions off the shelf but rather provides a process that includes consulting, customization, and optimization that fits directly into the client’s product lifecycle. The result of almost eight decades of refinement and innovation, Keysight Technologies enjoys high name recognition for the future.

National Instrument Corp.

With more than forty years under its belt, National Instrument Corp. has created a solid spot in the niche dedicated to manufacturing virtual instruments and automated test equipment. A sampling of industries served includes academic and research, wireless, aerospace and defense, automotive, energy, and heavy equipment. National Instrument Corp. prides itself on creating more than singular products but rather entire ecosystems for clients.

These are just a few of the big players in the DAQ field. As the Industrial Internet of Things (IIoT) grows, expect this already important industry to become even more so.

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Data Acquisition and Control System Modules For Industrial Applications

DAQ Industrial

Recent developments in control systems modules and data acquisition devices have led to them becoming cheaper, easier to use, and compatible with a wider range of software than ever before. For this reason, and increasing number of hobbyists are starting to use data acquisition systems, and the number of different systems available is growing rapidly.

If you are an industrial engineer, it might seem like data acquisition systems designed for hobbyists are not going to provide the kind of capabilities you need. But think again. Some of the systems that are available on the consumer market today match the specs of advanced industrial systems, incorporating multi-channel data collection and in built data analysis software.

If you are thinking about upgrading your data acquisition system, it’s really worth giving these devices a look, as they have several advantages over “traditional” industrial control and acquisition implementations.


First and foremost, the huge surge in the market for data acquisition devices has led to huge competition in the market, and this means that data acquisition devices are now very cheap. The cheapest data acquisition devices for you will depend on your own systems, what data you want to collect, and what you want to do with it.

But shop around, and you should find that upgrading to a new data acquisition system will now cost a fraction of the cost of your old system. And if you get a modern, fully-featured system, it will be better.


One of the biggest amateur uses of data acquisition systems is for race cars. In this type of implementation, where the system under study is inherently mobile, wireless connectivity is a key concern. The most advanced data acquisition systems, like the DAQIFI Nyquist, achieve this by incorporating a WiFi connection directly into the device.

And while the most obvious use of these devices is for mobile machinery like race cars, using WiFi for your data collection can have advantages in industrial set-ups also. Years of testing in the consumer market has made WiFi one of the most reliable data connections out there. In addition, devices to collect data, and to extend the range of your network, can be bought cheaply on the consumer market.

Modular Systems

Nowadays, almost all of the most advanced data acquisition systems are modular in design. This means that you can easily add extra control devices or sensors, without having to re-design your whole system. This is particularly useful if your set-up is likely to expand in the coming years – if you buy new machines, for instance, you can quickly extend your data acquisition system to include them

In short, this is a great time to update your industrial data acquisition and control systems. With so many new devices on the market, the price of a new system has dropped dramatically, and you can easily achieve a fully-featured system using easily available hardware.

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Input Device for DAQ – What is the Best Way to Gather Data

Today we will take a look at input devices for Data Acquisition (DAQ) systems. If you are new to DAQ, or are coming to it afresh after some time away, it’s worth reminding yourself of the basic parts of DAQ systems.

Essentially, most DAQ systems incorporate three components – the sensors that take a real-world phenomenon and turn it into an electrical signal, a card or other device that aggregates and sometimes amplifies these inputs, and then the computer terminal which is used to analyse the data produced.

Input devices are therefore at the heart of DAQ systems, taking the input from many sensors, aggregating them, and then passing them to software for analysis. Nowadays, many input devices are able to perform quite sophisticated manipulation of signals before passing them to software, and these devices range widely in terms of performance and extra features.

It is impossible, of course, to recommend the perfect DAQ input device for your purposes, because the sheer range of systems that now have DAQ systems incorporated in them means that each system is unique.

Nevertheless, there are three broad types of data input device, and it worth knowing the differences between them:

Direct Output

This is the way it used to be done, in the bad old days before modern systems. Typically, in a factory 20 years ago, each sensor would be hard-linked to a dedicated computer terminal. There were many problems with this approach, not least the expense of having individual terminals for each sensor, and replacing these every time the factory’s environment killed them.

Today, this is not a serious consideration for most DAQ users, unless you have very specific requirements that necessitate a direct hardware link.

DAQ Cards

When DAQ cards were invented a few decades ago, they were hailed as a revolution in DAQ systems. The advantage over older systems was certainly pronounced – one card inside a computer could take and aggregate inputs from multiple sensors, and this significantly cut down the cost of DAQ systems.

As DAQ cards developed, the number of inputs they could receive increased year on year, and multi-channel DAQ systems became commonly used. The low initial investment also meant that many companies who had never used DAQ systems before started to implement them.

In addition, as DAQ cards developed, more and more sensors and software systems were made compatible with them, which helped them to become the industry standard DAQ input device for many years.

Portable DAQ Units

Today, however, DAQ cards are themselves being replaced with portable, discrete DAQ units. These devices incorporate all of the advantages of DAQ cards, being massively multi-channel and able to accept a huge range of input types, but also have a few features that give them the edge.

Many of these new devices are able to output data via wi-fi, for instance, using already existing infrastructure as a medium to collect DAQ data, and further cutting costs. This also makes them portable, obviating the need to disassemble machinery to access DAQ data.

All in all, it is expected that these portable DAQ input devices will eventually replace DAQ cards in the vast majority of situations.

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How Do Temperature Data Acquisition Systems Work

temperature data acquistion

temperature data acquistion

Data acquisition systems are, at the most basic level, systems designed to collect data on a particular aspect of a physical process. Though long used in both industrial and scientific contexts, a growing number of hobbyists and semi-professional users are now recognizing the advantages of data acquisition.

Temperature data acquisition systems may be regarded as a sub set of generalized data acquisition systems. Temperature is often one of the most critical variables in any system, whether one used for industrial production or in specialized applications such as racing vehicles.

In addition, collecting data on the temperature of a system is often a critical safety requirement. If your equipment runs at its optimum only within a very narrow temperature range, or can be damaged by excessive heat or cold, you need to ensure that the data you are collecting on this variable is accurate and reliable.

There are several things to consider when setting up a data acquisition system for temperature, as there are with any kind of data acquisition system.

For more information on temperature data acquisition please take a look at our Nyquist and other DAQ Products.

Primarily, three factors need to be thought out:


Perhaps the most critical part of your temperature data acquisition system are the sensors – the devices you will use to measure temperature. Unfortunately, it is very difficult to recommend sensors here, simply because the range of temperatures your equipment works within may be significantly different depending on the type of system you run.

To state the obvious, however, make sure that your sensors are able to deal with the expected (or even unexpected) temperatures produced by your system – a sensor freezing or overheating, and then giving inaccurate readings, is a sure way to damage your equipment.

Data Aggregators

The signal produced by your sensors must then be fed to a device that is able to aggregate and store the temperature data produced. In days past, the most common way in which this was done was by having dedicated Integrated Data Loggers in each piece of machinery. Then came DAQ cards, which represented an advance, but still required dedicated workstations to be set up for each part of the system being monitored.

Nowadays, a good approach is to go for a portable data acquisition system. These are normally stand-alone units, able to run under their own power and to upload data via a wireless network. This is especially useful when the system being studied is inherently mobile, or operates in a hazardous environment.

Data Analysis

Once temperature data has been collected, it must be fed to a central location and analysed. Whilst in the past this was typically done through bespoke software, which required technicians to know a number of programming languages, today there are better approaches.

Many of the portable data acquisition systems mentioned above come equipped with dedicated analysis software which requires no specialist knowledge to implement and use. This is especially useful for amateur or semi-professional users, giving them access to powerful temperature analysis tools that were previously the sole domain of highly trained engineers.

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How Field Data Acquisition Works and Where to Buy Them

image of daq basics background

image of daq basics

Field Data Acquisition presents significant challenges for engineers, but in recent years a new generation of DAQ devices have come onto the market that aim to take the hassle out of the process.

These devices are typically able to run under their own power for significant periods and in surprisingly difficult environments. They are lightweight and compact, meaning that they can be incorporated into many systems with little hassle. They are also able to upload data to a central repository via wireless networks, which makes the collection of previously inaccessible data possible.

Our products offer the latest in field data acquisition. To find out more refer to our product page.


The Challenges of Field Data Acquisition

There are several challenges to be overcome when trying to collect data in the field. First and foremost, one problem is that whilst the system under study might be a large, rugged, mechanical piece of machinery, data acquisition systems are notoriously fragile.

This can be a problem if the system you are collecting data on produces, or works within, a hazardous environment. In days past, it was often the case that every piece of machinery had its own dedicated data acquisition system, and was hard-wired to a dedicated computer terminal. At best, this meant the expense of running delicate cables far enough away from the machinery to keep the engineer safe. At worst, it meant that data acquisition was only performed during shut downs.

Another challenge of field data acquisition is that in many cases the system under study is inherently mobile, such as a vehicle. Since, as already pointed out, most legacy DAQ systems required hard links, these systems were essentially unable to access data from mobile machinery.


The Solutions

Much time and thought has been expended in trying to overcome these challenges, and the new DAQ systems coming onto the market go a long way to overcoming them. Modern DAQ systems are increasingly based on mobile DAQ devices, which are inherently portable in themselves.

Nyquist DAQ in action

These devices achieve this portability in several ways. Primarily, instead of requiring a hard link they are able to collect data from a variety of sensors, aggregate this, and then upload it to a central repository via commonly used wireless networks. Some of these devices even have the ability to produce their own Wi-Fi signal, making connecting to them in the field very straightforward.

They are also admirably light and compact. This means that even in machinery where internal space is at a premium, or in performance contexts where extra weight would be a hindrance, they can be incorporated into the system under study with a minimum of effort or performance loss.

Being able to incorporate a data logger into the design of machinery, and fetching data from it wirelessly, also means that time is saved that would be otherwise spent disassembling systems in order to download the data collected.

Lastly, these portable devices come equipped with an on-board suite of software that means they are able to do quite complex data manipulation by themselves, dispensing with the need for a dedicated workstation whilst in the field.

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How Multi Channel DAQ Works

image of daq basics in action

image of daq basics in action

At their most basic level, data acquisition systems are systems that collect and aggregate data on an external physical system. Whilst much thought is often put into the choice and design of sensors for DAQ systems, less goes into the choice of the data loggers these sensors are connected to.

One of the major advances of the past few decades has been the advent of multi-channel data acquisition systems. These systems are able to accept and aggregate a variety of incoming signals, whether analog or digital, and collect them in a format which is easily accessible for external data analysis software.

The History of Multi-channel Data Acquisition

In order to understand why multi-channel data acquisition was so revolutionary when first introduced, it is worth looking at the way that DAQ systems worked before the advent of multi-channel devices. Typically, a bespoke data acquisition system was required for each piece of machinery being monitored, often with obscure and mutually incompatible hardware for each.

As such, each DAQ, and in some cases each individual sensor, was hard linked to a dedicated PC workstation. This not only meant the extra expense of running delicate and unreliable cables to numerous discrete workstations, but also required arcane, custom software to be running on each terminal also.

types of DAQ systems

And of course, for systems that are inherently mobile, or operate within hazardous environments not conducive to PCs, data acquisition was almost impossible.

Multi-channel data acquisition changed this by allowing one device to collect data from a vast variety of sensors simultaneously. This had great advantages at all stages of the data acquisition system. By feeding the signals from a variety of sensors into one data collection device, DAQ systems were made significantly smaller, and could therefore be fitted to systems where space constraints previously limited this.

Multi-channel data acquisition devices also meant that one work station could easily aggregate all the data being logged from a particular system, greatly simplifying workloads and data analysis.

Choosing a Multi-channel Data Acquisition System

Today, almost all high-end DAQs for general use are multi-channel. Though each system offers its own advantages and disadvantages, one of the major distinguishing features of each is the number of channels available for data.

Some DAQ devices offer minimal channels, down to just two in some cases, and others offer huge number of inputs, with 256 channel systems becoming increasingly common.

When choosing a multi-channel DAQ, it is important to consider how many channels you are ever going to need on one device. Whilst it can be tempting to go for a large number of input channels, devices with large numbers of channels are generally expensive, and may offer far more than you will ever need.

A second factor to consider is the type of data you will be feeding to your DAQ device. Whilst most of these devices now offer both analog and digital inputs as standard, in some cases each channel is dedicated to a particular type of input, limiting your usage of each.

Only after considering these questions, and having decided the number of channels you require, should you give consideration to other features of DAQ devices, such as the ability to wirelessly upload data, or the software included with them.

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What is the Cheapest Data Acquisition Available?

image showing the cheapest DAQ options

image of daq basics background

Choosing a data acquisition system (DAQ) can be a real task. In recent years, the explosion of devices on the market, each with their own advantages and disadvantages, means that any DAQ you are considering probably comes with a bewildering array of extra features.

Getting a DAQ need not be expensive, however, if you carefully consider what you need your system to do.

I would never recommend going for the cheapest DAQ available, for a number of reasons. Firstly, if you are using a DAQ to monitor a piece of machinery that you’ve spent many hours working on, you need a DAQ that is reliable. Your system overheating because your sensors have failed is frustrating, to say the least. Secondly, many of the cheapest DAQ systems on the market today are designed as discrete, proprietary units, meaning that they will not mesh well with the rest of your equipment, and will be difficult and expensive to expand.

Luckily all of Daqifi’s products are both affordable and extremely reliable. To see more please refer to our product pages.

That said, it is possible to achieve good value when purchasing a DAQ system, if you keep a few points in mind:

How Many Channels Do You Actually Need?

Whilst it may be tempting to go for a monster, 256-channel DAQ system, for most people and organisations this is more capability than they will ever utilize. Think carefully about the how much data you need to collect, and in what format, and buy a DAQ system accordingly.

Of course, you can always start with a small DAQ system, and then expand. Which brings me to my second point:

Go For a Modular System

The best DAQ systems available today are modular. This means that if you buy a device today, and then in a few years decide to add a second, they will work well together. This is perfect if you are using DAQ as hobbyist or semi-professional user, because it means that your DAQ system can expand as your passion does.

It also means that these systems work well with most third-party components, such as sensors and software. This can be a real advantage if you don’t have the time to be debugging each of your sensors.

How Are You Going To Collect Data?

The absolute cheapest DAQ systems on the market today output data via hardlink. The price of these systems might be tempting, but to my mind it is worth going for a system that is capable of sending data via Wi-Fi. You likely already have a Wi-Fi network in your workshop or home, and it makes sense to use it rather than building a whole separate system to handle your DAQ.

Get A System With A User Interface

Again, the cheapest DAQ systems around output data as a raw string of numbers. This is great if you are a software engineer, but for the rest of us means we have to learn a complex programming language.

The better DAQ systems come with on-board interfaces that allow you to collect and display data in an intuitive manner, and to my mind systems like this are well worth the extra investment.

So, whilst it may be tempting to go for the absolute cheapest DAQ system available, in my humble opinion you should always invest a little more in a few key features that will ultimately save you time and money.