Trends in Technology
It was predicted by Gartner back in 2017 that by 2020 there will be over 20 billion things connected online. That prediction wasn’t hard to make in 2017, but since then the adoption and implementation of IoT technology have surpassed all expectations.
Companies and businesses all over the world continue to invest in IoT and that number is only going to grow. That is mainly down to the rapid technological advancements being made, allowing more effective and cheaper adoption.
The technology no longer poses a challenge, as that has shifted towards how much value businesses can extract from data collected. IT teams deploying new IoT solutions must deliver high-value to companies who want something for their investment. It has always been difficult to make insights from data and extract value from it for the business.
Adding more streaming data, data types, and data sources make it impossible to obtain the desired value, especially with current data analytics, processing, and storage methods. Businesses’ must align several things within their strategy if they want to maximize their investment in the Internet of Things.
IoT is a Growing Market with Unparalleled Potential
IoT has become mainstream quickly and is no longer an emerging technology thanks to sensors for all kinds of purposes. The economic value of the IoT market is predicted to reach around $11.1 Trillion by 2025! Big enterprises with large budgets aren’t the only ones with access to IoT as even small companies with limited budgets are looking for ways to tap into the IoT market to improve their business model.
Apart from that, there are mature automation tools designed to accelerate the extraction of value from data and have an immediate impact. Companies managing and driving value from their data are now looking towards implementing machine learning, artificial intelligence, and deep learning.
There are no limits to what businesses will manage with their data, as it is no longer an issue of if they can afford the technology but has turned into what they manage to create with the creative application of insights available to them.
Data is the most valuable asset for businesses, big and small, as it is the one thing that affords them the chance to gain an edge over their competition. Organizations can now afford sensors for IoT applications to obtain every possible data set.
However, investing in IoT isn’t worth anything if the organization can’t derive value and actual insight for their business operations. That’s the reason why automation tools are critical for leveraging the full value of IoT investments.
Looking at Automation
The only realistic solution to handle the immense amount of IoT data is automation, because of the volume of connected devices and the data produced. Automation is a gamechanger for organizations as it allows them to ingest, transform, and deliver insights and data in real-time.
IT teams can then absorb the massive volume of data and manage to deliver insights in a manner that allows businesses to extract value and use it to their benefit.
The best thing about automation is that it eliminates the hassle of hand-coding the time-consuming and repetitive aspects of data infrastructure projects that provide major advantages to data warehousing teams. Data insights can then be delivered quickly and cheaply with increased reliability and improved quality in the results. It also frees data teams to focus on the strategic analysis of data output.
However, simply automating data processing isn’t going to be enough, because the only way to process data realistically is to stream it from devices in the field when it’s created and not in the future. Automation plays a major role in data processing as it allows data teams to process data produced in the field in real-time, which allows them to obtain more accurate insights.
The Impact of IoT on Storage
When you’re looking at infrastructure for supporting IoT environments reflex reaction to the massive influx of data from IoT devices is to purchase more storage. But that is a short-term and costly strategy because this growth is exponential. A better solution for businesses would be to think about how they can process data when it is being stored. That would mean analyzing data in real-time, which will allow organizations to save on data summaries and make future analysis easier.
That allows businesses to take advantage of massive savings on storage costs and it also speeds up reporting processes in the future, while improving the reliability and quality of insights. However, there is still value in storing raw data for a short period for testing exploratory workloads.
That’s where cloud storage comes into the picture as it offers a short-term cost-effective option that would be part of the data infrastructure. For that to work, you must implement automation tools to manage the schemas, organize the information, allow data to be queried, searched, and analyzed in its original format.
Making Sense of Data Sources for IoT
There are various types of data formats and sources created and used by IoT devices, which include the likes of machines and video surveillance cameras in the factory, sensors in modern airplanes, and sensors in bus brakes. Most of it is structured data in its traditional form, but there’s a lot of unstructured and semi-structured information that must be processed in real-time.
This data must be processed and collated into a manageable form before it can be converted into insights and performing this task manually is impossible due to the volume and complexity of the data.
The only way to do this is through automation, and in some cases, consuming entire data streams can produce value, as you can save those data sets and analyze them later for identifying trends. However, it’s better to filter all that data during the ingestion, because to understand what should be done with various IoT data streams, businesses must build an information flow to create the big picture of the time-sensitive and critical information that offers the most value to organizations.
Organizations also must identify historical information they plan on storing, which is going to be useful in revealing trends over time. The best bet in such a case is using automation tools to transform the data into a distinct heap from which valuable insights can be obtained.
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