Big data and the Internet of Things (IoT) are two hot topics top of mind for business leaders. Together they have been making a significant impact on companies’ ability to capture and analyze data to drive business decisions. In today’s environment there are many situations where the Internet of Things and big data work hand in hand with each other. However, they evolved as separate technologies and have some differences as well.
This raises the question—how exactly are the two connected?
Big data has been an evolving concept since the start of the digital age. Used to describe a huge data set that is defined by three characteristics, known as the three Vs— volume, velocity, and variety—big data differs from other data sets by the size (volume), rate of growth/change (velocity) and the variety of structured, unstructured, and semi structured data within the set.
The benefit to having an expansive data set is its potential to hold hidden patterns or trends that are only visible in a set that large. Additionally, it enables a full-picture view.
However, due to the magnitude and complexity of big data, the value comes from being able to analyze this data -- not the data itself -- which can prove to be a challenge. Big data is so large and complex that identifying business value from so much information can’t be done through traditional methods for processing and analyzing information.
Historically, organizations would have had to dedicate enormous amounts of time, money, and resources to analyzing the data if they wanted to gain any valuable insights from it. Fortunately, due to advancements in computing, big data analytics now makes it possible to combine big data sets with high- powered analytics. The result? Previously unwieldy data sets can now reveal actionable insights. Big data analytics packages huge data sets into a comprehensible format that allows organizations to use them. Additionally, by incorporating technologies like artificial intelligence (AI) and machine learning, more applicable insights can come to light. There are many sources of big data, one of those being data from the Internet of Things (IoT).
The Internet of Things (IoT) refers to physical objects connected through shared networks. A variety of sensors gather information and share it across systems that can store, manage, filter, and analyze the data. An IoT device can refer to everything from wearables to medical devices to industrial equipment.
The IoT enables companies unprecedented visibility into what is happening across their connected devices in real time. A vast amount of real-time data points are collected from connected IoT devices and transferred across the internet for storage and analysis.
IoT and big data have many overlapping components, and IoT is considered a major source of big data.
However, they were developed independently of one another. As the volume of IoT-generated data increased to the point that conventional storage and analysis methods became inefficient, big data and IoT become more and more interrelated.
In the current environment, the complex data and information gathered by IoT devices can be considered a big data set being gathered in real time. Big data storage and analytics currently help to make sense of the plethora of those real-time data points and provide helpful insights.
To sum up the relationship at a high level: A network of devices equipped with electronics and sensors (connected devices) send real-time information to the internet (IoT), where it is compiled and stored into vast data sets (big data) and analyzed to find useful patterns (big data analytics).
Big data analytics help to make sense of the data and information that is gathered by IoT devices. These solutions take the vast, unstructured data that’s been collected, and identify ways to organize it into smaller data sets that can give companies insights into how their processes are working, as well as improve decision-making.
Big data analytics can provide different types of insights when used with the IoT; namely, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics gives insights into how a connected device is performing in real time. It can be used for anything from locating a connected device, to understanding how that device is used by costumers, to identifying anomalies.
Diagnostic analytics gives insights into the “why” behind descriptive analytics. For a particular connected device, it can help organization understand why it is running in a certain way or why it is producing certain outputs.
A very applicable use of big data in IoT is in predictive analytics. This type of analytics utilizes machine learning by analyzing past data and producing probabilities for how the device will function in the future. This is especially beneficial when it comes to the servicing of IoT devices. Using this technology, organizations can anticipate failures or servicing needs before the device stops working.
Lastly, big data is used in IoT for prescriptive analytics. This type of analysis gives insights into how to impact things that have been observed or predicted.
Data visualization is an important aspect of IoT analysis, aiding in the ability to identify key trends. Data visualization is needed to properly identify and convey the best data insights that can be used to drive business decisions. The data generated by IoT devices is heterogenous, meaning it comes in a variety of formats: structured, unstructured, and semi structured. While in theory visualizations of data should make it easier to understand trends, when the data comes in so many different formats, a method of visualization becomes more difficult.
Big data continues to grow at an exponentially high rate. As they are today, big data storage systems have a limited amount of space, so it is becoming a significant challenge to manage and store such a large amount of data.
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