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Why Big Data and IoT Differs Yet is Compliant Across a Common Ground

Data may very well be the new oil for this century. The world generates data on an average of 2.5 quintillion bytes each day. That’s more than we can manage. However, leveraging these massive data troves could reveal fascinating insights that have the potential to transform whatever field it touches. This is where big data and IoT comes into the spotlight and so understanding the relation between them is crucial. Globally, IoT has moved up the ladder with IDC estimating $1.2 trillion spending by 2022. Big data is not behind with revenues forecasted to reach about $260 billion in 2022. Data remains fundamental to both. Big data involves collecting large volumes of data from sources like social media, devices, and sensors. IoT is procuring data from every device that surrounds us like home appliances. However, the underlying characteristics differ in both these technologies as they are two entirely different concepts. It would be better if we come to an understanding of these differences since our focus is on finding this out so as to apply them collectively in different fields to gain competitive advantage.Big Data and IoT are Two Entirely Different Concepts- IoT vs Big DataBig DataAs said, big data is all about huge volumes of data either structured or unstructured. The data is so large that it remains incapable of processing via conventional data processing software or techniques. It is characterized by three key determinants known as the 3Vs of big data - Volume, Velocity, and Variety.The volume deals with the large amounts of data procured, which are measured in larger units of data like terabytes, petabytes, and exabytes. Velocity determines the speed at which the gathered data must be processed to derive insights quickly and in real-time. Variety is about the nature of the data, whether structured or unstructured that include different file types.Together, these three characteristics - volume, velocity, and variety determine whether a stream of data could be labeled as big data. The need for higher computing power to process big data brings it in correlation with machine learning, natural language processing, business intelligence etc., which makes up the key components of its ecosystem.Currently, big data has acquired renewed importance particularly in businesses and industries that rely on data. Companies like IBM, SAP, Microsoft etc. have increased their spending on big data analytics and management. Moreover, with the proliferation of data globally, big data is swiftly penetrating into diverse applications such as healthcare, industry, enterprises, research & development, education, retail, e-commerce, and many more.Big Data Analytics: The Revolution Has Just Begun A talk from Dr. Will Hakes of Link Analytics, giving an extensive, high-level talk on the way big data analytics is changing- and will continue to change- the business intelligence landscape.                                                                        [Source : SAS]IoTImagine a world where your home appliance plugs into the internet. Your home could automatically respond to your presence such as a Hi-Fi system that plays your favorite music while you are at the living room or lighting systems that automatically switch on or off and optimize energy usage to save on your monthly utility bills. This is exactly what constitutes the Internet of Things.IoT extends connectivity from digital devices to non-internet physical devices that we use in our everyday life such as the appliances in our homes, automobiles, and other objects. All of them remain plugged to the internet enabling seamless interconnectivity to collect and exchange data for real-time performance metrics as well as remote monitoring and control.Sensors are the primary means by which devices and objects gather information from their surroundings. When connected to an Internet of Things platform, the data from all these devices are integrated and subjected to analytics. Once done, this can reveal readily useful information like patterns, potential issues and even put forth recommendations aimed at improving their performance.A more concrete example of IoT at work is in an IoT enabled car. The data collected from various sensors positioned at crucial components inside the car can help integrate the control and communications of the entire unit. For instance, the IoT platform can continuously assess road traffic conditions and suggest the driver change routes or adopt driving practices that heighten safety depending on the weather. Being a breakthrough technology with the potential to disrupt everything, IoT finds use particularly in agriculture, energy conservation, transportation, healthcare, manufacturing etc.Differences in Data Stream Management and ProcessingBig data involves collecting huge volumes of data. However, it does not subject these data streams to any kind of processing at once for extracting meaningful insights or patterns for making real-time decisions. Analysis occurs at a later stage and there is a delay or gap between when the data is procured and when it gets processed.IoT, on the other hand, is all about collecting and processing data streams at the same time. The data gathered gets processed instantly in real-time for optimizing the performance as well as correcting any malfunctions or identifying problems early on. In IoT, the data streams are constantly managed real time for accurate data-driven decisions and analysis.Since the collected data is analyzed only after a certain interval, big data solutions find use primarily in areas such as capacity planning, predictive maintenance, etc. Whereas IoT is all about time, as the simultaneous collection and processing of data find application in real-time scenarios like vehicle dynamics or traffic management to optimize their operational capabilities as well as detect issues.Big Data is Human Generated, Whereas IoT is Machine GeneratedBig data is about data generated solely due to human activity across different interfaces. For instance, our social media usage of scrolling through news feeds, subscribing to various channels and the messages we send and receive will leave out data trails that are collected by these platforms over time to build up a picture of our behavior and detect patterns in usage so as to personalize their services.Besides social media, human-generated data used in big data projects comes from other sources like emails, browsing activity etc. Amassed over longer periods, these data remain helpful in detecting patterns in human behavior with certainty, which remains helpful for planning and fixing long-term solutions.IoT relies extensively on machine-generated data from sensors attached to everyday objects like home appliances and personal digital devices. An IoT platform collects and analyzes these data real-time to optimize operations and detect potential problems simultaneously. A typical example is smart traffic lights that can switch traffic across the lanes real-time depending on the number of vehicles on the road or any congestions in vehicular traffic.Summing UpAs we have seen above, both big data and the Internet of Things shares several differences that make them two dissimilar concepts. Big data is more into collecting and accumulating huge data for analysis afterward, whereas IoT is about simultaneously collecting and processing data to make real-time decisions. In spite of their differences, it is not correct to say that the two never complement one another. In fact, both these technologies seem better off when combined together.Being perfectly compatible, big data can leverage the power of IoT to make sense of the data collected in real-time. The reverse is also true as IoT can benefit from the massive data troves gathered in big data projects to optimize, detect and even predict issues, so as to prevent them from occurring. Merging these two together is one step to being proactive with data, to shift processes and operations entirely into a data-driven approach.
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Why Big Data and IoT Differs Yet is Compliant Across a Common Ground

Tony Joseph
Blog
12th Nov, 2018
Why Big Data and IoT Differs Yet is Compliant Across a Common Ground

Data may very well be the new oil for this century. The world generates data on an average of 2.5 quintillion bytes each day. That’s more than we can manage. However, leveraging these massive data troves could reveal fascinating insights that have the potential to transform whatever field it touches. This is where big data and IoT comes into the spotlight and so understanding the relation between them is crucial. Globally, IoT has moved up the ladder with IDC estimating $1.2 trillion spending by 2022. Big data is not behind with revenues forecasted to reach about $260 billion in 2022.

 big data

Data remains fundamental to both. Big data involves collecting large volumes of data from sources like social media, devices, and sensors. IoT is procuring data from every device that surrounds us like home appliances. However, the underlying characteristics differ in both these technologies as they are two entirely different concepts. It would be better if we come to an understanding of these differences since our focus is on finding this out so as to apply them collectively in different fields to gain competitive advantage.

iot impact
Big Data and IoT are Two Entirely Different Concepts- IoT vs Big Data

Big Data

As said, big data is all about huge volumes of data either structured or unstructured. The data is so large that it remains incapable of processing via conventional data processing software or techniques. It is characterized by three key determinants known as the 3Vs of big data - Volume, Velocity, and Variety.

The volume deals with the large amounts of data procured, which are measured in larger units of data like terabytes, petabytes, and exabytes. Velocity determines the speed at which the gathered data must be processed to derive insights quickly and in real-time. Variety is about the nature of the data, whether structured or unstructured that include different file types.

Together, these three characteristics - volume, velocity, and variety determine whether a stream of data could be labeled as big data. The need for higher computing power to process big data brings it in correlation with machine learning, natural language processing, business intelligence etc., which makes up the key components of its ecosystem.

Currently, big data has acquired renewed importance particularly in businesses and industries that rely on data. Companies like IBM, SAP, Microsoft etc. have increased their spending on big data analytics and management. Moreover, with the proliferation of data globally, big data is swiftly penetrating into diverse applications such as healthcare, industry, enterprises, research & development, education, retail, e-commerce, and many more.

Big Data Analytics: The Revolution Has Just Begun 

A talk from Dr. Will Hakes of Link Analytics, giving an extensive, high-level talk on the way big data analytics is changing- and will continue to change- the business intelligence landscape.

                                                                        [Source : SAS]

IoT

Imagine a world where your home appliance plugs into the internet. Your home could automatically respond to your presence such as a Hi-Fi system that plays your favorite music while you are at the living room or lighting systems that automatically switch on or off and optimize energy usage to save on your monthly utility bills. This is exactly what constitutes the Internet of Things.

multiple solutions

IoT extends connectivity from digital devices to non-internet physical devices that we use in our everyday life such as the appliances in our homes, automobiles, and other objects. All of them remain plugged to the internet enabling seamless interconnectivity to collect and exchange data for real-time performance metrics as well as remote monitoring and control.

Sensors are the primary means by which devices and objects gather information from their surroundings. When connected to an Internet of Things platform, the data from all these devices are integrated and subjected to analytics. Once done, this can reveal readily useful information like patterns, potential issues and even put forth recommendations aimed at improving their performance.

A more concrete example of IoT at work is in an IoT enabled car. The data collected from various sensors positioned at crucial components inside the car can help integrate the control and communications of the entire unit. For instance, the IoT platform can continuously assess road traffic conditions and suggest the driver change routes or adopt driving practices that heighten safety depending on the weather. 

Being a breakthrough technology with the potential to disrupt everything, IoT finds use particularly in agriculture, energy conservation, transportation, healthcare, manufacturing etc.

Differences in Data Stream Management and Processing

Big data involves collecting huge volumes of data. However, it does not subject these data streams to any kind of processing at once for extracting meaningful insights or patterns for making real-time decisions. Analysis occurs at a later stage and there is a delay or gap between when the data is procured and when it gets processed.

IoT, on the other hand, is all about collecting and processing data streams at the same time. The data gathered gets processed instantly in real-time for optimizing the performance as well as correcting any malfunctions or identifying problems early on. In IoT, the data streams are constantly managed real time for accurate data-driven decisions and analysis.

Since the collected data is analyzed only after a certain interval, big data solutions find use primarily in areas such as capacity planning, predictive maintenance, etc. Whereas IoT is all about time, as the simultaneous collection and processing of data find application in real-time scenarios like vehicle dynamics or traffic management to optimize their operational capabilities as well as detect issues.

Big Data is Human Generated, Whereas IoT is Machine Generated

Big data is about data generated solely due to human activity across different interfaces. For instance, our social media usage of scrolling through news feeds, subscribing to various channels and the messages we send and receive will leave out data trails that are collected by these platforms over time to build up a picture of our behavior and detect patterns in usage so as to personalize their services.

usage of machine dats and iot

Besides social media, human-generated data used in big data projects comes from other sources like emails, browsing activity etc. Amassed over longer periods, these data remain helpful in detecting patterns in human behavior with certainty, which remains helpful for planning and fixing long-term solutions.

IoT relies extensively on machine-generated data from sensors attached to everyday objects like home appliances and personal digital devices. An IoT platform collects and analyzes these data real-time to optimize operations and detect potential problems simultaneously. A typical example is smart traffic lights that can switch traffic across the lanes real-time depending on the number of vehicles on the road or any congestions in vehicular traffic.

Summing Up

As we have seen above, both big data and the Internet of Things shares several differences that make them two dissimilar concepts. Big data is more into collecting and accumulating huge data for analysis afterward, whereas IoT is about simultaneously collecting and processing data to make real-time decisions. In spite of their differences, it is not correct to say that the two never complement one another. In fact, both these technologies seem better off when combined together.

Being perfectly compatible, big data can leverage the power of IoT to make sense of the data collected in real-time. The reverse is also true as IoT can benefit from the massive data troves gathered in big data projects to optimize, detect and even predict issues, so as to prevent them from occurring. Merging these two together is one step to being proactive with data, to shift processes and operations entirely into a data-driven approach.

Tony

Tony Joseph

Blog Author

Tony believes in building technology around processes, rather than building processes around technology. At Fingent, he specializes in custom software development, especially in analyzing processes, refining it and then building technology around it. He works with clients on a daily basis to understand and analyze their operational structure and come up with technology solutions to deliver an efficient process.

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Top comments

Jessica

15 November 2018 at 12:44pm
Great updates. technology is updating day by day on every field. These blogs are really informative

venkey

15 November 2018 at 12:44pm
nice article.

navya

15 November 2018 at 12:44pm
Nice article,the writing style is awesome.

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