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Data Engineering

Big Data Business Applications and Main Trends for 2022

Four significant trends in Big Data drive changes in how organizations process, store, and analyze data.

Nicolás Mosconi
by
Nicolás Mosconi
|
November 2022

Big Data proves its value continuously to companies and organizations of all types and sizes in various industries. Enterprises that use big data are realizing tangible business benefits, from improved efficiency in operations and increased visibility into rapidly changing environments to optimizing customer products and services.

Big Data technologies, practices, and approaches continue evolving as organizations find uses for these large data stores. New methods and architectures for collecting, processing, managing, and interpreting the gamut of data across an organization continue to appear.

Working with big data is more than just dealing with large volumes of stored information, as Volume is just one of the many challenges (V's) of big data that companies and organizations need to handle.

There usually is also a considerable variety of data, from structured information sitting in databases distributed throughout the organization to extensive amounts of unstructured and semistructured data residing in multiple formats and devices, such as files, images, videos, sensors, system logs, text, and documents, including paper ones that remain to be digitized.

In addition, this information often is produced and altered at a rapid rate (velocity). It has variable levels of data quality integrityy), creating additional challenges in data management, processing, and Analysis.

Four significant trends in Big Data are enabling organizations to meet those challenges.

More data and the growth in data diversity propel breakthroughs in data processing and the rise of edge computing.

The rate of data generation keeps gaining speed. A considerable amount of this data is not caused by database transactions but comes from different sources, including cloud systems, smart devices, and video streaming.

This so-called "dark data" is mainly unstructured and, in the past, was left primarily unprocessed and unused by organizations.

This acceleration brings us to the most significant tendency in big data: Non-database sources will continue to be the predominant generators of data, in turn pushing organizations to reevaluate their needs for data processing.

In particular, voice assistants and IoT devices are causing a quick ramp-up in Big Data management requirements across industries as varied as retail, healthcare, finance, insurance, manufacturing, and energy and in a broad spectrum of public-sector markets.

This eruption in data diversity is compelling organizations to think outside the classic data warehouse as a means for processing all this information.

In addition, the need to handle the generated data is moving to the devices themselves, as breakthroughs in processing capability have led to the development of advanced devices capable of gathering and storing data independently without straining network, storage, and computing infrastructure. 

Edge computing translates to using devices for distributed processing, which shifts the processing load to devices before data is sent to the servers, optimizing performance and storage by reducing the need for data to flow through networks.

Edge computing allows for quicker data analysis and faster responses to the user while lowering computing and processing costs, cloud storage, bandwidth, and processing expenses. 

For example, wearable devices like Fitbit, Apple Watch, and Google Android devices drive growth in telemedicine by allowing healthcare providers to gather crucial patient data in real-time.

At the same time, results are used for many big data processing and analytics applications designed to improve patient outcomes.

Big Data storage requirements spur innovations in cloud and hybrid cloud platforms and the growth of data lakes

Organizations spend more of their resources storing this data in a range of cloud-based and hybrid cloud systems to deal with the inexorable increase in data generation.

In previous decades, organizations had to manage, secure, and operate massive data centers, a dynamic that changed by migrating the responsibility to cloud infrastructure providers such as AWS, Google, Microsoft, and IBM.

Without maintaining their own large and complex data centers, organizations can handle almost limitless amounts of new data by paying for storage and compute capabilities on demand.

However, restrictions preventing public cloud infrastructure use or technical limitations challenge heavily regulated industries in their use of cloud infrastructures, such as healthcare, financial services, and government.

Cloud providers have developed more regulatory-friendly infrastructure and hybrid strategies that incorporate aspects of third-party cloud systems with on-premises computing and storage to satisfy pressing infrastructure demands in the past decade.

As organizations continue to push for the economic and technical advantages of cloud computing, the evolution of both public and hybrid cloud infrastructures will no doubt progress.

In addition to cloud storage and processing innovations, enterprises are shifting toward new data architecture approaches that allow them to handle big data's variety, veracity, and volume challenges.

Enterprises are evolving the data lake concept rather than trying to centralize data storage in a data warehouse that requires time-intensive data extraction, transformation, and loading, as data lakes store structured and unstructured data sets in their native format.

Data Lakes can also provide services for data analysis, sharing, and processing, as this approach redirects the responsibility of conversion and processing to endpoints with different data requirements.

The usage of advanced analytics, machine learning, and other Artificial Intelligence technologies has dramatically increased

With the extensive data being generated, classic analytics methods are questioned because they're not easily automated for data analysis at scale.

Distributed processing technologies allow organizations to process petabytes of information rapidly, while Machine learning and AI systems will enable them to spot patterns, detect abnormalities, and make forecasts more easily.

Businesses use big data analytics technologies to optimize their business intelligence and analytics initiatives, leaving behind slower reporting tools dependent on data warehouse technology and shifting to more innovative and responsive applications.

This change enables greater visibility into consumer behavior, business processes, and general operations.

Machine learning and Artificial Intelligence systems have been more revolutionary than any other technology for big data analytics, as organizations of all kinds and sizes use AI to optimize and improve their business processes.

Machine learning allows them to recognize patterns and irregularities in large data sets more efficiently and provide predictive analytics and other state-of-the-art data analysis capabilities. 

This usage of machine learning includes:

  • High degrees of personalization and recommendations.
  • Information classification by automated processes.
  • Natural language processing capabilities for voice and text analysis.
  • Image, video, and text data processing by Recognition systems.
  • Autonomous business process automation.

With AI and machine learning, businesses are utilizing their big data environments to deliver more resounding customer support via intelligent chatbots and more personalized interactions without needing substantial increases in customer support staff.

These AI-enabled systems allow enterprises to collect and analyze extensive amounts of information about clients and users, particularly when paired with a data lake design that can aggregate a broad spectrum of data across many sources.

Data Visualization is another area where enterprises are reaping the rewards of AI, and Machine Learning applied to Big Data. When information is represented in a visualized form, such as charts, graphs, and plots, people better understand the meaning of data.

Emerging forms of data visualization are helping organizations spot key insights that can improve decision-making by delivering AI-enabled analytics into the hands of casual business users.

Advanced visualization and analytics tools even let users ask questions in natural language, with the system automatically determining the correct query and showing the results in a context-relevant manner, allowing companies to discover the value of data-driven decision-making and the power of data across the organization.

DataOps and data stewardship push to the forefront.

Innovation is driven by technology needs and changes in how we think about and relate to data, with many aspects of Big Data processing, storage, and management in continuous evolution. 

The appearance of DataOps, a methodology, and practice focused on agile methods for dealing with the data's complete lifecycle as it courses through the organization, is one of the main areas of innovation.

Rather than dealing separately with data generation, storage, transportation, processing, and management, DataOps processes and frameworks address organizational requirements along the data lifecycle from generation to archiving.

Data governance is another area where organizations increasingly deal with data privacy and security issues. Enterprises often were somewhat relaxed about data privacy and governance concerns.

Due to widespread security breaches, diminishing customer trust in enterprise data-sharing practices, and challenges in data management, new regulations make corporations much more liable for what happens to their users' and consumers' personal information in their systems.

Organizations are working harder to properly secure and manage data, especially as it crosses international boundaries. New tools are emerging to ensure that data stays where it needs to, is secure at storage and in motion, and is appropriately tracked over its entire lifecycle.

Primary applications of Big Data for Businesses in 2022

Technology is becoming integrated into our daily workspace at an increased rate; according to the most current estimation, data analytics and data science will continue to conquer the worldwide industry.

Improved technology and broader access to an ocean of data allow organizations to reach more insights, improve performance, generate income, and innovate more quickly; Big data has a bright future.

Collecting and analyzing large quantities of data allows organizations to make informed decisions, boost productivity, and improve customer service, making big data and analytics increasingly crucial to businesses of all sizes.

Big data and analytics are constantly changing, with new technologies and approaches always emerging.

Studies reveal that big data has spurred a change in the global business perspective in a couple of years; as more than 2.5 quintillion bytes of data are being generated daily, big data is undoubtedly gearing up to change how businesses think!

Rise of Predictive Analytics

Big data has been empowering business organizations and data analytics stakeholders with its fundamental approach for quite a time., helping them gain a competitive edge and accomplish better services, more sales, more customers, and happier customers.

Predictive analytics is the practical consequence of business intelligence and Big Data. Many companies use predictive analytics to apply machine learning or artificial intelligence algorithms, conducting data mining and predictive marketing to optimize their business operations. The global predictive analytics market will reach $28.1 billion by 2026.

These digital transformation technologies have transformed the legacy approach into a more modern and integrated one by increasing internet proliferation, cloud technologies, and connected systems while compelling businesses to invest in predictive analytics.

Advancements in big data, artificial intelligence, and machine learning position predictive analytics to acquire more power and provide more crucial insights, while Predictive Analysis offers a realistic and data-driven future prediction for different specialties.

Growing Cloud-Based Solutions

As leading and small and medium enterprises go remote, more cloud-based technologies facilitate the shift to hybrid or multi-cloud deployments, helping companies save costs associated with legacy tools and bottlenecks.

Several organizations will choose cloud-native analytics solutions to achieve a competitive advantage with streamlined analytics and business intelligence, making Cloud-based technologies mainstream, and this trend will continue.

With worldwide cloud revenue reaching $474 billion in 2022 due to the ongoing pandemic and the growth in digital services, the cloud has become the focal point of new digital experiences. By 2022, public cloud services will carry out 90% of data analytics innovation and processes.

The cloud's future benefits are limitless; as data analytics moves to the cloud, cloud-native analytics will become necessary for industry-leading enterprises, with the revenue from the public cloud market surpassing $331 billion in 2022, and more than 85% of companies adopting a cloud-first strategy by 2025. 

Humans to Drive Artificial Intelligence Evolution

Artificial intelligence is already making substantial progress in the corporate sector, improving its ability to learn algorithms and reducing time to market, granting enterprises that use artificial intelligence for data analytics a higher probability of thriving than their counterparts that don't.

However, one of the biggest obstacles to embracing artificial intelligence was the dread of job loss. People regarded technology with the paranoia of losing their jobs and being substituted by bots.

While their fears weren't necessarily baseless because machines can perform faster and eliminate errors, the true potential of AI can be achieved through a coexistence between machines and humans.

Reinforcement learning and distributed learning techniques result in more adaptive and flexible systems. By 2022, industries will be able to manage more complex business cases employing artificial intelligence. 

Artificial intelligence models will be at the vanguard of business strategy; companies will see a more vertical growth slope because artificial intelligence and machine learning models have become the standard, extending to every department and touching every business function.

Artificial intelligence is expected to become more widely embraced in diverse areas, particularly finance, healthcare, retail, and manufacturing.

Increased use of Blockchain for Data Security

With rapid digital technology advancements introducing new challenges around data security, organizations must secure their data by enforcing robust authentication and cryptography key vaulting mechanisms, pushing Blockchain as one of the most secure data protection technologies.

Blockchain is growing in popularity for guaranteeing secured transactions with little effort. The network approves peers' transactions without relying upon a third party, enabling users to store encrypted data on a secure, decentralized network while making data sharing and auditing much easier and preventing unauthorized access.

 

Online eCommerce transactions have started using Blockchain. However, it has also found applications in other domains, including healthcare and security, with technology evolving new possibilities to disrupt business services and solutions for clients.

This technology will emerge with growing global services in various sectors as the front-runner.

The magnitude of data is growing exponentially with the rise of the Internet of Things, creating a potential threat to its security. Blockchain will be a satisfactory answer for this situation as it is considered tamper-proof and hacker-proof.

In addition, Blockchain can provide anonymized digital identities to IoT devices and enable data sharing between them.

Higher Adoption of Business Intelligence Tools

Industries, including manufacturing, banking & finance, and consumer & retail service, will increase the adoption of business intelligence tools and technologies in 2022 and beyond because these tools transform how organizations approach data analytics.

The worth of the global business intelligence and analytics software market is predicted to be $17.6 billion by 2024. Business intelligence is already bringing transformations in numerous sectors, including marketing, consumer services, customer experiences, and the entire eCommerce segment.

These tools make big data more unrestricted, lowering the processing power and specific knowledge required to analyze and interpret information. 

Even if users are not from the Information Technology or data mining background, they can still run analytical functions such as exploring data sets or performing data-mining tasks. The only prerequisite required is knowledge of how best to use these tools.

Final Thoughts

With promising trends and many more upcoming ones, big data is undoubtedly all-set to revolutionize multiple business sectors, processes, and public infrastructure.

The data analytics market is constantly expanding, and businesses must embrace these Big Data trends for 2022 to remain relevant.

Companies wishing to retain a competitive edge over their competitors must start investing in these latest technologies, as welcoming change is the only way for businesses to succeed in the future, remain competitive, and thrive in this rapidly transforming technological landscape.