HomeTechnologyBig Data - Benefits, Examples, Trends, and Beyond

Big Data – Benefits, Examples, Trends, and Beyond

Big data refers to data collections that are often bigger, more complicated, and often come from novel sources.

These sets are too large for the capabilities of typical data processing programs.

The good news is that you can utilize this mountain of data to solve previously intractable business issues.

There is a global market worth $274 billion dedicated to “Big Data” and “Analytics”. The amount of information created daily is around 2.5 quintillion bytes.

Dynamics of Data Centers

Let’s get to know more about big data applications in the following sections-

What is Big Data Technology?

‘Big Data’ refers to the massive amounts of unstructured, semi-structured, and structured information gathered by businesses and then analyzed using tools like machine learning and predictive analytics.

Generally, when people talk about big data solutions, they mean datasets that are too huge or complicated to be processed by standard information analysis programs.

Larger datasets (more rows) provide better statistical power. Still, those with more characteristics or columns may have a higher false discovery rate.

Despite its absence of a technical definition, the most common use of the term big data applications refers to a massive collection of information that exceeds our capacity to process when it is presented in smaller doses.

A Brief History of Big Data Technology

7 stoppages representing the brief history of big data
Figure 1 – History of Big Data Technology in a Snapshot

Big data applications have been around since the 1960s and 70s, thanks to the first information centers and relational databases.

Around 2005, the sheer volume of information produced by Facebook, YouTube, and other online services became apparent.

That same year, the open-source framework Hadoop was built to store and analyze extensive information collections. At the same time, NoSQL was rising in popularity.

Since open-source frameworks like Hadoop (and, more recently, Spark) make extensive information more manageable and less expensive to store, their development was crucial to expanding the industry.

Since then, the amount of this technology has increased exponentially. Although it is not only people, users continue to produce massive volumes of information.

More and more things are becoming internet-enabled, allowing companies to track consumer behavior and optimize their offerings. Even more, information is now available because of the development of machine learning.

While this technology has gone a long way, its applications are only getting started. The advent of cloud computing has widened the scope of big data applications even more.

Cloud computing provides true elastic scalability since programmers can quickly and easily create new clusters to run tests on small information samples.

Graph databases are also rising in prominence because of the speed and depth of analysis they provide to large datasets.

Characteristics of Big Data Solution

When describing big data applications, its six defining features are-

6 boxes showing Characteristics of Big Data
Figure 2 – Six Significant Characteristics of Big Data


The massive volumes of information that businesses are responsible for managing and analyzing


Big data solutions value the most crucial “V” from a business’s perspective comes from discovering insights and pattern identification that lead to more efficient operations and better customer connections.


The variety and scope of available information formats may vary from raw to semi-structured to entirely unprocessed.


The rate at which businesses acquire, retain, and handle information, such as the exact amount of social media posts or search queries received within a specific time frame (whether it is a day, hour, or something else entirely).


Whether or if top-level executives may have faith in the “truth” or integrity of data and information assets


For example, in sentiment or text analytics, the meaning of keywords or phrases might change over time, presenting a challenge for businesses as they attempt to acquire, store, and analyze this information.

Benefits of Using Big Data Solutions

While big data management has the potential to provide a wealth of advantages to businesses, it also presents several risks that might undermine any future gains.

Why do businesses need to start using big data solutions? Let’s get to know about the benefits to understand more about this technology-

5 circles showing Benefits of Using Big Data Solutions
Figure 3 – Five Major Benefits of Using Big Data Solutions

Facilitates Better Decisions

Big data solutions help businesses get valuable insights into their operations.

For instance, this technology may be helpful for human resources in employment and recruitment.

Due to the time and effort required to identify suitable employees, a firm might need better recruiting processes.

With big data solutions, HR departments can streamline operations and provide better results.

Enhances Efficiency

Using big data solutions, IT professionals may boost their output.

This technology automates the process of sifting through information from many sources, freeing human resources for use elsewhere.

Faster and more thorough information analysis is critical in boosting productivity in all company areas.

Less Money Spent

Any competent individual would agree that it is crucial for a business to minimize expenditures wherever feasible.

To save expenses, businesses are exploiting big data applications in the following ways:

Effectively Reaches Consumers

It digitizes supply networks to raise productivity and lessen the likelihood of expensive interruptions. It helps detect fraud and avoid financial loss.

Those mentioned above are a few potential cost savings that can be realized using big data solutions.

Using big data applications, Netflix can cut its yearly subscriber retention costs by around $1 billion.

Beneficial to Customer Service

The quality of a company’s customer service department may significantly impact its brand’s popularity, customer retention rates, and market standing.

With the help of this technology, customer service teams may get a wealth of data-driven insights that can be used to track and improve staff performance.

It is expected that many businesses would exploit the quantity of data at their disposal, to provide superior service to their clientele.

How does Big Data Work?

Insights gained from Big Data applications may lead to novel strategies and revenue streams. There are three essential steps before taking off-

3 circles showing how does big data work
Figure 4 – Three Steps of Big Data’s Work Process


Big data applications integrate information from a wide variety of systems and locations.

Extract, transformation, and loading (ETL) and similar conventional information integration approaches often need improvement.

Analysis of terabyte or petabyte-scale information collections requires novel approaches and tools.

It is essential to bring in the data, process it, and have it ready in a manner that your business analysts can use throughout the integration process.


To store big data solutions, you need space. You may store data in the cloud, on-premises, or in a hybrid configuration.

You may keep your data in whichever format you like and apply any processing rules and process engines whenever necessary.

The location of an individual’s existing data is a significant factor for many when deciding on a storage option.

The cloud is growing in favor of a convenient computing solution since it can meet your immediate needs and scale your business.


When you examine and use your data, big data applications become valuable.

Gain insight by comparing your data visually. Try to learn more by digging deep learning into the facts.

Do not keep your research to yourself. Utilize ML and AI to construct your data models. Use the information you have.

Use Cases of Big Data Applications

A rising number of specialists are working to use the insights gleaned from big data applications. Use cases of this technology in industries like-

5 boxes presenting use cases of big data
Figure 5 – Five Significant Use Cases of Big Data Applications

Soundtracks, Films, and TV shows

Most people now utilize at least one of the most widely available music streaming services, completely transforming the music business.

The music streaming services Spotify and Pandora use this technology to analyze user preferences and make instantaneous adjustments to the user’s listening experience.

As the “most complete study of music ever conducted,” Pandora’s music genome project is one way the service tailors its offerings to each customer.

Users who sign up for Spotify get a weekly curated playlist featuring their pictures.

Treatment and Medical Care

Many of your medical records are now likely stored digitally. The prevalence of electronic data may alter two significant facets of your daily existence.

First, it helps medical facilities better record their patients’ medical histories to serve them better.

However, your information is being examined alongside the health records of millions of other people to monitor the spread of illnesses better, gauge the success of new therapies, and much more.

Stories with Big Data

To further inform their stories, journalists are increasingly turning to social media.

Because of how widespread social media platforms like Twitter have become, even regular people may be the first to break important news within minutes.

Reporters may utilize the search functions on these sites to zero in on postings made within specific time and location parameters, which is helpful when compiling information for articles.

How you consume news is another area where this technology directly affects your day-to-day existence.

Significant data tendencies feed your numerous media timelines with items ranked as more important or exciting, depending on your preferences.

News aggregators and social media sites generally prioritize the most-discussed articles, which automated data systems determine.

Knowledge and Work

Educational institutions are using statistics-based programs to find and recruit the kinds of students who would help them achieve their aims.

Improved graduation and enrollment rates are two ways schools may reap the benefits of increased access.

In making admissions choices, it is common practice to consult data that attempts to foretell the applicant’s future performance and the school.

Likewise, businesses are trying to optimize their employment procedures by analyzing data.

They often use systems that compile massive amounts of data to find the best match for open positions.

Intelligent or Artificial Machines

With the help of big data management examples, chatbots can streamline the process of finding particular pieces of information.

These chatbots may “learn” and enhance their capacity to personalize your experience over time since they use big data and AI.

Overall, the interaction between this technology and AI is mutually beneficial.

This technology fuels the development of AI is helping us get better insights from this technology in various ways.

In fact, artificial Intelligence is developing new approaches to data analysis.

Even better, it makes data analysis easier to do. In this era of massive data is critical to remember that AI needs human leadership.

Artificial intelligence (AI) may be used to fix typical data issues, making data analytics more predictive and prescriptive.

Examples of Big Data in Big Companies

Nearly all sectors are now using big data management technology to better prepare for developing new goods, services, and more.

About 97% of companies plan to invest in big data’s rising potential by 2022.

However, when used properly, this technology may strengthen and anchor people’s innate intuition.

The following are examples of businesses that utilize technology to influence sectors as diverse as marketing, cybersecurity, and many more.

4 boxes showing examples of big data
Figure 6 – Four Giant Examples of Big Data in Big Companies


Amazon, like Facebook and Google, entered the ad tech industry because of the wealth of customer data it had collected.

Since its inception in 1994, the corporation has amassed vast amounts of data on millions of customers’ transactions, delivery addresses, and payment methods.

Amazon’s self-service ad platform has expanded in recent years, allowing more businesses (including marketing firms) to buy ads and target them to very targeted audiences (including previous Amazon customers).


To anticipate demand surges and fluctuations in driver availability, Uber constantly analyzes its data.

With this knowledge, the corporation may charge fair trip prices and incentivize drivers to maintain a sufficient fleet size.

To further ensure happy customers, Uber uses data analysis to anticipate when their rides will arrive.


Forge provides private securities market infrastructure services, including technology, data, and marketplaces.

Trading in privately traded shares, fractional loans, and derivatives occurs between private parties rather than on a centralized market.

Using big data, the Forge Intelligence app provides users with real-time price and trade data from the private market.


House of Cards, the first of Netflix’s original TV shows and directed by David Fincher, is a political thriller with its genesis in big data technology.

Due to the common interests of its audience in David Fincher films and Kevin Spacey, Netflix allocated $100 million for the production of the first two seasons of House of Cards.

The executives had the foresight to see that combining the three would result in a successful series.

Now more than ever, Netflix’s investment in shows and the promotion of those shows are both influenced by this technology.

Users’ viewing habits, such as when they stop a program, may affect anything from the thumbnails on their homepages to the shows in the “Popular on Netflix” section.

Big Data Implementation Cost

Figure 7 – Cost of Big Data Implementation

Hadoop’s low-cost scalability enables Big Data. 125-250-node petabyte Hadoop clusters cost $1 million.

A managed Hadoop distribution costs less than $4,000. A corporate data warehouse costs $10–100 million. Hadoop’s Big Data management system seems cheap.

Creative organizations use Hadoop, but how and how quickly they incorporate it into their IT infrastructure is the issue. Big Data management and integration are costly.

Yahoo requires nodes for 200 petabytes over 50,000 as the technology environments grow. Web 2.0 companies using Hadoop depend on data redundancy.

In contrast, corporate banking and communications service providers must follow security, disaster recovery, and availability procedures. Hadoop’s complexity demands expert staff.

The “Data Scientist” is a statistician who can code and use MapReduce frameworks. New MapReduce training will quickly outweigh hardware and software costs for most enterprises.

Integrate this technology with Hadoop using an information warehouse and business intelligence architecture. Unfortunately, Hadoop does not support SQL.

Businesses need help to benefit from Big Data management’s complexity and hidden costs. Hadoop is young, but its usability and consistency are increasing quickly.

Apache open-source contributors, large and small, are innovating. These two aspects will most affect adoption costs and difficulties:

Hadoop data may be compressed granularly, searched using SQL, and analyzed with BI tools. With them, businesses can survive without any issues.

Problems of Using Big Data

Listed below are a few of the drawbacks of big data that businesses need to be aware of-

3 problems of using big data
Figure 8 – Three Major Problems of Big Data

Threats to Cybersecurity

It is only natural for businesses to be wary of completely embracing cutting-edge technology.

Hackers increasingly focus on big data solutions, so businesses that use these cutting-edge analytics technologies are putting themselves in harm’s way.

Keeping large amounts of data, especially private information, has several dangers. However, businesses may safeguard their information by using a variety of cyber precautions.

Imbalance in Talent

As the use of this technology continues to grow in the corporate world, so does the need for skilled data scientists and analysts.

These IT workers may majorly influence a business, which is why they command high salaries.

However, there needs to be more qualified IT professionals to take on these technology-related projects.

Certainly, this technology may be valuable for businesses, but only if handled by experts.

Compliance Factors

Compliance difficulties are another potential drawback of big data technology for businesses.

Organizations, especially those dealing with private or sensitive customer information, are responsible for maintaining compliance with all applicable industry and government regulations.

The management, storage, and use of extensive information would be complicated without the presence of a compliance officer.

For instance, the General Data Protection Policy (GDPR) is a massive information privacy policy designed to safeguard customers in the European Union, and businesses operating there must be aware of it.

This information technology has quickly become an indispensable tool for today’s businesses.

This technology is predicted to continue its meteoric rise and eventually become an indispensable tool for businesses of all sizes and sectors.

Latest Trends of Big Data Technology

The four most significant big data trends are helping organizations overcome numerous challenges and get many benefits.

These trends of this technology suggest deployments for organizations. Let’s get to know about them-

4 boxes showing latest trends of big data technology
Figure 9 – Four Latest Trends of Big Data Technology

More Data and Diversity Drive Processing and Edge Computing

Information production is increasing day by day. Even, database transactions generate little of this information.

Cloud, internet applications, video streaming, and innovative gadgets like smartphones and voice assistants drive this change.

Due to its lack of structure and complexity, organizations often overlook this data, converting it into “black data.”

Voice assistants and IoT devices drive big data management needs in retail, healthcare, finance, insurance, manufacturing, energy, and many public sectors.

Businesses are looking for new storage and retrieval solutions due to the facts explosion.

Using cloud-based information storage and processing reduces expenses. Edge computing accelerates information processing and user responses.

Wearables like Fitbits, Apple Watches, and Android smartphones are helping telemedicine flourish and capture real-time patient information.

Patient-focused big data technology processing and analytics programs leverage the results.

Large Information Storage Demands with Hybrid Cloud Architectures

To stay up with exponential information expansion, organizations are investing more resources in storing this information in cloud-based and hybrid cloud systems that support all the V’s of big data technology.

Businesses used to build their storage facilities, which required massive information centers.

Cloud companies have spent the past decade refining infrastructure to comply with laws and hybrid ways of combining third-party cloud services with on-premises computing and storage.

Public and hybrid cloud infrastructures will grow as more firms recognize the financial and technical advantages of cloud computing.

Enterprises use cloud computing and storage to address big data applications’ variety, veracity, and volume.

Data lakes are replacing information warehouses, which need complex extract, convert, and load procedures.

Mainly, data lakes include structured, semi-structured, and unstructured facts. Information transformers and preparers are users with different information needs.

The information lake may be a shared resource for information processing and analysis.

Advanced Analytics and Machine Learning are Being Used More

Consequently, companies are replacing information warehouse-based reporting tools with agile, real-time apps that give deeper insights into consumer activities, processes, and demographics using big data technology.

Big data applications were transformed by machine learning and AI. All sizes of companies are using AI to improve operations.

According to Enterprise Strategy Group, 63% of 193 respondents who were familiar with artificial intelligence and machine learning programs in their organization expected to invest more in such technology in 2023.

By leveraging its big data management sets to develop innovative chatbots and personalized interactions, companies can provide complete customer support using AI and machine learning.

These AI-enabled systems can collect and analyze vast consumer and user information using a data lake approach.

Information visualization, especially for corporations, has also improved. Charts, graphs, and plots assist readers in understanding the information’s meaning.

Significant insights are easier to identify, improving decision-making. Some advanced visualization and analytics systems allow users to ask natural language queries and present the responses in a form that makes sense.

Data Operations and Management Become A Priority

Over the following years, big data solutions processing, storage, and administration will evolve.

Technological needs and advances in information conceptualization and interaction have driven this wave of innovation.

DataOps, a concept, and methodology that stresses agile, iterative data lifecycle management, is a significant innovation in this industry.

Big data management environments have increased corporate worries about data governance, privacy, and security.

Businesses no longer neglect information governance and consumer information protection.

Due to widespread security breaches, dwindling customer trust in enterprise data-sharing practices, and difficulties managing facts over its lifecycle.

Organizations focus more on figure stewardship and adequately securing and managing information, especially when it crosses international borders.

New solutions are emerging to maintain this information where it belongs, safeguard it in transit, and monitor its aging.

These big data solutions developments make 2023 and beyond a fascinating time to work.

Final Thoughts

A wall of pricing constraints no longer imprisons the future of big data management, and many major firms are now moving closer to, if not wholly adopting, all of these trends.

Data scientists and engineers are developing novel approaches to sift through massive data sets in search of actionable insights, all without the resources of a Fortune 500 company.

More and more small and medium-sized businesses will use big data management to improve their operations.

For those who try to learn and adapt, the future is bright. So go for it!

What is Big Data in simple terms?

Big data includes large, complex datasets from new sources. Data processing techniques cannot handle these massive data sets. This pile of data can address previously insurmountable business problems.

Who invented Big data?

The term “big data” has been around since the 1990s, although some credit John Mashey with popularizing it. Traditional software systems struggle to obtain, curate, organize, and analyze large data volumes quickly.

Why do we need Big Data?

Big data lets you monitor production, customer feedback, refunds, and other factors to reduce downtime and anticipate consumer demands. Big data improves market-driven decision-making.



Please enter your comment!
Please enter your name here

- Advertisment -spot_img