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As we knew, we are living in the world of data drive age and digital transformation.
In this blog, we’ll explore how data is shaping our world and driving to innovation.
Data comes from many sources. From the moment we interact with internet , be it through a each click , each transactions, each search, Email, Text, or even online we spent time on social media generate data. Our smart phones are all time uploading data, everything we do online is stored and tracked as Data. Not just smart phones and computers or other devices which connected to internet generate data, the smart homes devices connected to it such as voice guides, security cameras, smart lights, smart vacuums, fitness and so on., could be connected to the internet and generates massive amount of data.
Yes, This is called as Internet of Things, IoT
The term IoT refers to collective network of connected devices and the technology that facilitates communication between devices and cloud, as well as between the devices themselves.
This is the concept of connecting any “THING” to the internet to exchange data.
Let’s analyze with One of the scenarios happened , In the industry and Manufacturing units , the concept of industry 4.0 , the revolution firstly introduced in Germany .
It is all about smart factories, connecting machines and devices to the internet in order to exchange data.
Now we can find IoTs in the big cities , where trying to implement this smart technology , in order to
Reduce waste
Save money
Improve quality
Research says in 2022 we have 14 billions of IOT devices from small household things to cooking devices, machines connecting to internet , transportations and more that are directly connected to internet generating exchange of data .
Big Data :
The amount of data transforming from IOTs , social media, web services, industrial machines ., is outstandingly electrifying.
They are currently over 44 Zetta bytes of data through out the entire digital universe. So, we can understand , we are no longer dealing with normal transitional data, Thus we are dealing with BIG DATA
This Big Data is helping to make billions of real time data points collected by IOT , Internet of Things devices , organizing information , into digestible datasets that inform companies on how to optimize their processes.
There are 3 indicators that define the dimensions and properties of Big Data . Popularly said to be 3 ‘V’s
1.Volume represents the immense amounts of Data generated every second from various sources.
Estimation is around 2.5 quintillion bytes of data is created each day.
As a result, it is now not uncommon for large companies to have Terabytes and even Petabytes of data in storage devices and on servers.
This data helps the shape and future of the company and its actions, all while tracking its progress.
2.Velocity - measures how fast the data coming in and how quickly data is processing and analyzed
But the traditional batch processing methods are often slow to handle speed and performance.
With Big data , the sources are generated streams of data which are very high speed.
In real time , to achieve the speed required data is often stored and processed in memory rather than on disk.
This significantly reduces latency and enables faster data processing.
3. Variety -
Data collected from one place and Delivered in diverse formats . Organized data that fits into traditional databases like SQL, Excel spreadsheet are called as Structured Data.
Data that doesn’t fit neatly into structured Databases but has some organizational
Properties , like JSON, XML files are said to be
Semi structured data.
Data that doesn’t have predefined format such as text documents, emails, videos, images, social media posts are said to be Unstructured data.
Therefore, the Big data is collection of huge volume, high velocity and different varieties which can efficiently store, process and analyze data to reveal significant values for the business.
But still there is a problem with this kind of generated data what is Raw Data .
Raw Data is the unprocessed data sometimes called source data or primary data, which has not yet been subjected to cleaned. It is often messy, unorganized, containing error, duplicates or incomplete information which can include numbers, texts, images, audio or any other formats.
Approximately 70% of this unstructured data are unused . When raw data is left without processing or unrefined accumulates digital waste , consuming storage space and financial resources in data centers which are not beneficial.
The cost associated with storing raw data , along with missed opportunities resulting from failure to process and analyze it , can significantly impacts the businesses growth.
So, there comes ,
The British mathematician Clive Humby famous phrase “Data is a new oil” .
It means we have to extract the raw data like we extract the oil , we have to refine and then process, transform it into something useful and has value to the business.
This has been widely debated and analyzed since he first declared in 2006.
Subsequently, many businesses and companies have come to regard this raw data as their most valuable asset. They recognize that extractions, analyzation and refining is paramount to derive the valuable insights that drive better decision making and strategic planning .
What can be done with this Raw Data ?
1. Data Architecture : Process of creating a blue print how data to be managed. From collection through to transformation, distribution, consumption and storage the data into different layers for different purposes. It is foundational to data processing operations and artificial intelligence applications. So, Data Architecture makes it easier to manage , protect and access our data.
2. Data Engineering : It is very complex process for designing and building data pipelines and data storages. ETL processors plays the important role. To extract raw data from multiple sources , then transform it and loaded into targeted storage.
3. Data modeling : The process if connecting the dots , making relationships between entities and objects. It involves in organizing data into logical structures, defining attributes to these structures and making relationships.
4. Data Mining : Process of analyzing massive amount of raw data to discover business intelligence like patterns, trends, to solve problems and to mitigate risks . By applying advanced statistical and machine learning techniques we can uncover the hidden insights.
5. Machine learning : It’s a method of data analysis that automates analytical model building. It is a branch of AI based on the idea that computer can learn from the data, identify patterns, and make decisions with minimal human intervention.
6. Data science : The Scientific study of data . With the power of programming languages, together mathematics and statistics and knowledge of specific domain can help to uncover the insights from raw data.
7. Data visualization : Process of transforming raw data into visual representation.
Conclusion :
This data when combined with powerful analysis tools like Big Data technology and Internet of things (IoT) , is transforming the industries and our daily lives.
IoT connects devices to the internet letting them share and collect data from various sources continuously.
Big Data process and analyzes this vast amount of data to dig into important patterns and trends.
Using this technologies together, industries and companies can improve their efficiency, reduce data waste, save money and enhance their productivity.
Hence, by effectively using Data Analytics, IoT and Big Data businesses can unlock valuable insights, innovate and stay ahead in data driven digital world successfully.
References :
IoT
4.0 First Revolution in Germany
Big data