BIG DATA is changing the world. Technologies and techniques have come up that can transform any kind and volume of Data Trends into treasure troves of information. Humongous amounts of data generated every day are now being used by businesses to improve performance and profits, all thanks to big data analytics.
The past decade saw significant strides in data analytics and big data. Big data analytics powers business intelligence platforms, machine learning systems, intelligent automation, the world’s top e-commerce websites, and even online learning & paper help services.
So, what is this so-called Big Data? How did it bring about such revolutions in the world of technology? And what Big Data trends can we expect in 2022?
This article explores in depth.
Insights Into 2022’s 9 Biggest Big Data Predictions
Get online, and you will find a vast array of articles, research journals, and books on Big Data. So much has been written on Big Data and data science, but the underlying complexities of the concepts make things quite challenging to grasp at one go.
Big Data is a catch-all term for signifying giant sets of usually unstructured data. You will find big data sets everywhere, from e-commerce sites, banking and financial institutions, online media, marketing platforms, cybersecurity, space exploration, energy production, etc.
TinyML:
This is a machine learning paradigm specifically designed for devices with smaller and low-power devices. TinyML takes advantage of edge devices such as smartphones that have low latency. These devices consume micro or milliwatts of energy, much lower than your average graphics processing units. TinyMCE is thus exceptionally eco-friendly and can run for years in many cases.
The downsides are a lack of data storage and reduced processing capabilities. On the other hand, zero data storage ensures security as there’s no chance of any data theft.
AutoML:
Here is another machine learning paradigm that focuses on reducing human intervention and interaction. AutoML systems can solve real-life problems with minimal human intervention. From mining raw data to developing accurate ML models, AutoML can do it all.
One of the primary motives of this AI paradigm is to offer extensive learning techniques to newcomers and beginners in the domain.
Predictive Analytics :
Predictive analytics is a viral & powerful application of big data and machine learning today. It finds extensive usage in marketing, online education, the financial market, business intelligence, etc.; 2022 will witness applications in numerous other domains.
Machine learning and predictive analytics using big data are the most prominent digital transformation technologies across several domains. Extensive data mining is a central aspect of predictive analytics, wherein massive amounts of data are mined for knowledge discovery. In addition, more and more businesses can now implement ML & big data analytics in their operations & strategies with ease, thanks to cloud integration with analytics systems.
Data Fabric:
This is yet another major trend that will continue to dominate the tech domain over the years. Data fabric is an architecture comprising an amalgamation of data services on the cloud.
Data fabric is expanding at a phenomenal rate across enterprises. Consisting of critical data management technologies such as data pipelining, data integration, data governance, businesses use this technology to unify all data and develop an even data landscape. Reduced time consumption, faster mining and analysis, and easier extraction of insights are the most prominent advantages of a data fabric.
The proliferation of Business Intelligence Applications:
Thanks to the phenomenal rise of data science & analytics, data is now an extraordinarily precious & potent resource. Discovering knowledge through data mining is integral to predictive analytics, automated learning, and business intelligence.
Across industries like retail, manufacturing, education, services and energy, there’s an increasing demand for business intelligence tools & platforms. Business intelligence enhances the strategic and decision-making processes of a business and changes how organizations use their data. BI employs big data analytics to reduce the amount of computation required and allows for more straightforward interpretation & faster decision-making.
Powerful BI platforms such as Microsoft Power BI, Oracle Analytics Cloud, Google Analytics, etc., find applications across businesses of all sizes.
Growth Of Cloud-Based Analytics:
Cloud-based technologies are empowering businesses of all shapes & sizes. Cloud simplifies access to powerful analytics technologies & enables companies to unlock the true power within their data.
Cloud-based technologies have become mainstream, with companies like Amazon, Google & Nvidia offering cloud resources as a service. Cloud-native big data analytics solutions are becoming the top preference of numerous companies as they increase competitive advantage through advanced data dissection and intelligent support.
In one of their articles, Geeks For Geeks, a leading coding computer science online community, reports businesses to increase their big data/BI budgets by up to 50%. Additionally, Allied Market Research states that in the global retail sector alone, big data analytics generated $4.85 Billion in 2020. And this number will rise to $25.56 Billion by 2028.
The Rise of The Data Universe:
The rise of big data would have inevitably led to advanced data management infrastructure development. Data universe systems bring together all the different data analytics components in the simplified package and will become the heart of enterprise operations in the coming years.
The data universe describes the vast amount of data today’s organizations deal with, alongside different data management architectures such as data mesh, data lake-house, data fabric, etc. They culminate all the processes and systems for quick and easy conversion of big data into actionable insights and actions.
NLP:
Natural Language Processing is an AI that helps assess natural written text or speech. A leading sub-domain in AI research, NLP involved heavy data mining of massive data sets. Big data analytics allows NLP models to understand grammar, syntax, semantics, context, emotions, and eventually discourse & purpose.
NLP deals with the uncertainty and vagueness in natural human language using predictive analytics. Different methodologies & techniques are used to understand & generate natural language that’s strikingly similar to human interaction & language. The best examples can be Apple’s Siri, Amazon’s Alexa, Google Assistant, or any advanced AI chatbot.
Cyber Security:
Digital technology is one of the most powerful tools in the hands of man. It was only a matter of time before malicious individuals began using it for nefarious reasons. Today, cybercrime is one of the biggest dangers to an increasingly digital world. As we start depending more and more on computers, data & networking, our vulnerability to cyber-attacks increases manifold.
Big data analytics and machine intelligence are reinforcing the cybersecurity industry substantially. Techniques such as XDR (Extended Detection and Response) and SOAR employ advanced security analytics to boost a network’s security for better performance. As a result, expect big data to be a significant trend in cybersecurity through 2022 & well beyond.
And those were the top 9 big data predictions of 2022. As we move further into the digital information age, data will become a central cog in industries all over. Big data analytics has the power to offer insights that can transform everything, from individuals to enterprises.
That wraps up this write-up. I hope it was an exciting read for everyone alike. Take care!
