These days it is very common to hear business managers talk about “big data” and “big data analytics.” Virtually every industry and most professions now employ applications that make use of big datasets. But how do these differ from ordinary datasets? What sets them apart and why have big data applications become so ubiquitous — or at least so widely talked about? Does big data analytics have advantages that go above and beyond those of traditional data analytics? Here we’ll shed light on these and myriad related questions.
What Is Big Data?
The term “big data” generally refers to very large volumes of data that are difficult to manage, although it can also refer to ways of analyzing and making use of datasets whose size and complexity go beyond what traditional data processing tools can capture and process.
The business role played by big data analytics dovetails with that of business intelligence — both types of applications are used to improve business decision-making. But while business intelligence tools can only answer questions posed by the business, big data analytics can point the way toward questions the business didn’t even know to ask.
Key Takeaways
- Big data analytics has rapidly come into widespread use and is now essential for many different types of businesses.
- Big data takes advantage of — and makes profitable use of — the vast quantities of customer and operational data generated by today’s digital businesses.
- There are multiple ways to process very large sets of data, but they all aim to uncover patterns and behaviors that would otherwise remain hidden.
Big Data Explained
Today’s businesses are drowning in data. This data comes from many different sources, takes many different forms and the volume is constantly growing, especially as more aspects of business becomes digitally automated. That makes it very challenging for a company to sort through all its data and extract meaningful information.
Yet this data, when properly understood, can reveal many insights that will help the company grow and remain competitive. Sound data analysis can reveal insights about virtually everything that’s important to a business — customers, employees, processes, the larger industry and economy. For fast-growing businesses, data can inform a leap to the next level.
Big data analytics provides the tools and techniques to turn enormous quantities of data into those insights — useful information that a company can act on. But only in relatively recent years has computer processing power and the analytical capability of software advanced to the point that data analytics systems can make sense of the vast oceans of data that fully digitized businesses generate.
How Does Big Data Work?
There are a variety of techniques for managing very large datasets. What all of these have in common is that they provide ways to analyze vast quantities of different types of data in order to reveal hidden patterns. Once these patterns are identified and understood, they can provide the basis for truly sound business decisions. The technical details of how big data analytics does this is of little or no use to business managers.
More germane for business managers is learning how to put big data to work within their organizations. This requires serious commitment of time and resources. The steps required to develop a successful big data program within a business generally flow as follows:
- Establish big data goals. This means articulating the key questions that matter most to the success of the business, how the answers to those questions will add value to the business, and how the analysis of big data will provide the answers.
- Staff the program. To work well — and not lead business thinking down bad paths — big data programs require a variety of expertise in technology and math. Hire big data program leaders who know statistics and how to interpret them. And, most importantly, how to recognize when noise in the data or the incorrect application of a modeling technique can cause untrustworthy results.
- Find the data. Identify the type of data that’s most relevant to the key questions from step 1 and how it will be captured or acquired. In addition to data generated by a business’s operations, there are many valuable external datasets available for purchase or provided free by various governments and nonprofits. Any number of these may be relevant to the business’s key questions.
- Store the data. This includes defining how the data will be gathered, stored and retrieved such that its quality, consistency and reliability are maintained, as well as how it will be secured in a way that is consistent with government regulations and privacy standards. This is sometimes called data governance.
- Analyze the data. Let the statistics experts apply big data analytical models until they find insights that answer the key questions established in step 1.
- Share the answers. Automatically generate (and distribute to all relevant stakeholders in the organization) the most useful results.
- Rinse and repeat. Reprise steps 3 through 6 to refine your analysis and dig for more insights. Sometimes a later step alters the thinking for a prior one and you must iterate — again!
Why Is Big Data Important?
The patterns identified through big data analysis can help companies answer questions crucial to their business. Businesses can wield these insights to improve operational efficiencies and better manage their resources. For example, big data analysis can help businesses streamline product development, better cater to the needs of their customers and identify new markets and growth opportunities.
Using Big Data
Without always realizing it, we’ve probably all experienced big data at work. When a credit card issuer calls a card holder to warn about potential fraud, that’s the result of big data analytics tuned to perform fraud detection based on real-time review of an immense number of credit transactions. IBM’s Watson big data analytics system famously won on Jeopardy! against the show’s greatest champions before making advances in the realm of medical diagnoses. Smartphone mapping services that provide drivers with routes through hundreds of miles of congested roadways are another example of big data in action.
Which Industries Use Big Data?
Big data is in use throughout both the public and private sectors, and virtually every industry has developed its own relevant applications. Some prominent examples include:
- The retail sector, which uses big data to identify and target different market segments, improve brand perception and increase customer satisfaction.
- Financial services, where big data is widely used to manage risk, identify fraud and project trading outcomes.
- Manufacturing, which uses it for demand forecasting and supply chain management, as well as product planning and design.
- Health care, where big data applications are used for genome mapping, patient diagnosis, and tracking and managing population health.
- The federal government, which employs big data to develop weapon systems and track security threats, analyze economic data and develop regulatory policies.
Big Data Use Cases
The breadth of uses for big data is far-reaching. Some of the many business use cases for big data include:
- Product development, where big data analytics is used to anticipate consumer demand by correlating the characteristics of previous products with their acceptance by customers.
- Predictive maintenance, where the collection and analysis of many different types of data — such as part performance, prior failures, stress factors, manufacturing history and sensor data — can be monitored and used to optimize maintenance schedules and replace parts before they fail.
- Identifying fraud, through the use of pattern recognition based on extremely large datasets.
- Maintaining regulatory compliance, by aggregating data for reporting purposes.
- Personalizing customer experience, by tapping data from websites, social media, customer call center logs and numerous other sources to improve interactions and create a detailed and more intimate portrait of the customer.
- Improving operational efficiencies, which is where big data analytics is having the greatest overall impact. Big data is analyzed to prevent production failures and downtime, avoid supply chain interruptions, gather and assess customer feedback, improve products and project future demand.
History of Big Data
Large collections of data have been gathered since as far back as the 1960s, but it was around 2005 when businesses began generating vast amounts of data through web commerce, digitalization of more aspects of business operations — including digital marketing — and the use of Facebook, YouTube and other online services. That’s when the volume of data really exploded. Around the same time, an open-source framework known as Hadoop was created to store and analyze extremely big datasets, and NoSQL databases, which can manage huge quantities of rapidly changing data, also became popular.
These technologies made it much easier and more cost-effective to derive value from large datasets, and the number of big data applications began to skyrocket, along with the volume of data itself. Most recently, with the advent of the internet of things (IoT), which encompasses a myriad of individual real-world sensors, these countless networked devices and the applications that they’re used with have begun adding to the big data trove.
Advantages and Disadvantages of Big Data
To understand the scope and range of big data’s pros and cons, consider two axioms: “Nothing in this world is worth having or worth doing unless it means effort, pain, difficulty” (the words of Theodore Roosevelt, Jr., 26th president of the U.S.) and “With great power comes great responsibility” — first stated by Voltaire, not Peter Parker’s Uncle Ben (as most moviegoers believe).
Big data analysis is hard. It requires concentrated strategic thought about how to create business value from the information, expertise in statistics and one or more social sciences (depending on the business context), and extensive advanced information technology (IT) infrastructure. The IT part is becoming easier, thanks to cloud computing. But figuring out how to create business value and finding and hiring the experts who can both suss out meaning from large datasets and know when their results are skewed by noise in the data remain very difficult challenges.
Companies that succeed in big data, though, find great economic power. The commensurate responsibility consideration comes from growing worldwide concern — and regulatory initiatives — about individual data privacy. State-of-the-art big data analytics can violate individual privacy, even unintentionally, unless it is carefully designed to avoid doing so.
Here are additional thoughts about the advantages and disadvantages of big data:
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Advantages:
The ability to process and analyze vast quantities of data has multiple benefits, such as the ability to better understand and target customers, identify fraudulent behaviors and optimize business processes. For example, it can tell a business if it is attracting the type of customer it expects (male, female, young, old) and offer insights into how to optimize those customers. With big data-based statistical modeling, a company can identify different potential scenarios for what it might do if a large country places import tariffs on its industry.In addition, big data has a symbiotic relationship with artificial intelligence. Large datasets feed machine-learning software, helping to train these applications and make them more robust. They, in turn, can then find more valuable patterns when analyzing big data.
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Disadvantages:
An important disadvantage is that it’s hard to keep up with big data. With data volumes doubling roughly every two years, organizations must devote ever larger amounts of resources to try to stay on top of the influx. Without sufficient expertise, it’s difficult to tell how trustworthy the results of a big data analysis are, which could lead a business down the wrong path. Also, cybersecurity becomes an immense challenge, as the damage caused by a data breach can be devastating. Add data privacy laws and regulations into the mix, and companies must make considerable investments in data protection measures in order to safeguard their customers and remain compliant.
Types of Big Data
Not surprisingly, the types of data collected and analyzed by big data analytics are vast. But they fall into three broad categories:
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Structured:
Structured data is the type of data that can be stored in spreadsheets and relational databases that are organized into columns and rows. The data is either numerical or standardized text, and each entry has an address — the column and row in which it appears. The addresses make it relatively simple to track, map and analyze this data, even on a very large scale.
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Unstructured:
On the other hand, unstructured data is not standardized, usually non-numeric and not suited to the column and row structure of a relational DBMS. It includes mobile texts, emails and Word documents, as well as phone calls and videos. Its lack of a uniform or inherent structure makes large volumes of unstructured data more of a challenge to track and analyze. Doing so requires specialized tools, such as data lakes.
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Semi-structured:
And then there’s semi-structured data, which shares some of the characteristics of both structured and unstructured data. Web pages, Word docs, or emails that are highly formatted, have subject lines or are organized by topic are considered semi-structured, as are highly organized reference texts such as a dictionary or an encyclopedia. Like unstructured data, semi-structured data is not well suited for a relational DBMS. But since it has some degree of structure, it can be stored, mapped and analyzed more readily than unstructured data.
Characteristics of Big Data, AKA the 6 Vs of Big Data
Although people argue about who coined the term “big data,” we know it came into use during the 1990s. That’s when it became clear that the sheer volume of data being generated by internet applications such as ecommerce was far larger than the ordinary datasets that information technology professionals were used to until then. Since then, the IT industry has chosen to define the characteristics of big data using words that begin with the letter “V.”

Now, some experts define big data with four Vs, some with as many as 10. Most commonly, however, big data is characterized by these six features or attributes that set it apart:
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Volume:
First and foremost, big data is distinguished by the quantity of data collected. In today’s digital enterprises, this data can come from many different sources, including customer contacts, sales transactions, invoices, bills of lading, equipment sensors and performance readouts, web traffic, market research, documents, phone calls, emails, smart devices, GPS tracking systems and many, many others. As one measure of just how much data is generated, internet users create 2.5 quintillion bytes of data every day.
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Variety:
All this data arrives in many different formats — spreadsheets, database entries, text documents, voice and video recordings, images and many other types. Big data embraces all of them.
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Velocity:
This refers to the speed at which data is gathered. Devices that make up the IoT, such as traffic sensors, smart meters and security cameras, for instance, generate continuous data streams at enormous rates — and this is only one set of inputs that big data management systems must handle. It is currently estimated, for example, that 1.7 megabytes of data is generated per second by each and every person in the developed world.
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Variability:
The ways in which big data accumulates are unpredictable and can change at a moment’s notice, reflecting events like a sudden spike in web traffic or a new sales trend.
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Veracity:
The quality and reliability of the data in question is known as its veracity. To be useful, big data must not only be varied and voluminous, it must also be accurate. Maintaining this accuracy is a challenge, since the quality of the captured data can vary greatly depending on the source and how it is stored and secured.
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Value:
Big data’s value is its worth to the organization — how much profit can be realized from the insights it provides.
Other Possible Characteristics
In addition to the six Vs shared by all big datasets, there are many other distinguishing characteristics that some big data may have, depending on its purpose, architecture and implementation. Five of the most common are:
- A big dataset is considered exhaustive if it includes all relevant data from all possible sources.
- Big data is classified as relational if it consists of common fields that lend themselves to a single, overarching meta-analysis. This is opposed to non-relational big data, which comprises related but separate datasets, each with its own distinct fields and characteristics.
- Big data can also be extensional when new fields can be readily added to the dataset or the existing fields can be easily modified.
- If the storage system used to house the big data can be rapidly expanded on demand, the data is considered scalable.
- If the dataset is highly detailed, thoroughly indexed and easily searched, it said to be fine-grained.
Examples of Big Data
Many big datasets are routinely collected on a daily basis. Here are a few examples:
- The New York Stock Exchange generates over 1 terabyte of order and reconciliation data each trading day.
- Facebook gathers more than 500 terabytes of data daily from the comments, messages, photos and videos that are posted to the site.
- Sensors can record more than 10 terabytes of data from a single jet engine during a half-hour of flight time. When multiplied by the many thousands of flights that take place each day, the airline industry ends up generating many petabytes of engine performance and maintenance data every 24 hours.
How Is Big Data Stored and Processed?
Data warehouses that make use of relational databases are often ill suited for managing big datasets, since they can store only structured data. Instead, data lakes are commonly used, since they support a variety of both unstructured and structured data formats. The data housed in the data lake may also be combined with other data stored in an RDMS (remote desktop management system) or data warehouse.
Since processing all this data to perform various types of analytics requires prodigious amounts of computing power, clustered computing systems are often used. These consist of many thousands of inexpensive servers running in parallel and linked together using technologies like Hadoop and the Spark processing engine. This is known as massively parallel processing.
To give themselves greater flexibility and the ability to scale their big data infrastructure as needed, businesses have increasingly begun to store and manage their data in the cloud.
Big Data and Regulations
There really aren’t laws or regulations that specifically address big data in most parts of the world, including the U.S. Instead, companies are constrained by applicable data privacy laws and regulations that pertain to their industry, such as HIPAA for the U.S. health care industry; the Gramm-Leach-Bliley Act, which regulates financial data; and the Federal Information Security Modernization Act, which regulates federal government data, to name only a few. In Europe, the major relevant regulation is GDPR — the General Data Protection Regulation.
In general, these regulations aim to protect individual privacy. They apply to big data in situations where the sophisticated analytical techniques in use with big datasets are capable of associating behaviors with specific individuals who assumed their behavior to be private.
Five Big Data Best Practices
Big data initiatives require many moving parts — and they’re all complex, with their own moving subparts. A business launching its first big data project is unlikely to succeed without a carefully conceived plan. Here are five best practices to follow when launching a big data project:
- Set a strategy: To succeed with a big data application, place it in the proper context. This means starting with the business problems it will be used to address and how the initiative feeds into the company’s long-term objectives. This stage includes identifying questions key to business success on which the big data initiative will focus. The right strategy should set the stage for future success and guide decisions on what data the company should acquire, how it should be used and with whom to share it both within the organization and among its ecosystem of business partners, suppliers and customers.
- Identify your big data sources: What type of data will the business need to achieve its goals for the project? Will it naturally accrue this data as a result of its operations, or will it have to come from other sources? There are many potential sources of big data, including:
- Streaming data from the IoT and other connected devices. This can include data generated by wearables, smart cars and appliances, medical devices and industrial equipment.
- Social media data that originates from interactions on Facebook, YouTube, Instagram and other platforms. Much of this is unstructured or semi-structured data in the form of images, videos, voice, text and sound, and can be useful for marketing, sales and customer support.
- Publicly available data from sources like the U.S. government’s data.gov, the CIA World Factbook or the European Union Open Data Portal.
- Other sources, which can include industry databases and the data lakes and warehouses operated by the company’s partners, suppliers and customers.
- Combine structured with unstructured data: Important insights can be gleaned by integrating structured data — such as the transactional and performance data generated by the company in the course of its business operations — and unstructured data collected from customer support center phone logs, websites, social media and other sources. The combination provides more relevant data points for a big data application to work with and can lead to better, more accurate conclusions.
- Make liberal use of data visualization tools: Human beings excel at identifying patterns visually. Even people who don’t have the coding skills or the technical background to understand a clustering algorithm can readily discern a pattern created by the data points that the algorithm generates if those are plotted on a graph. When visualized, data outliers and values that don’t fit into the overall pattern are easily spotted by most people. Using the appropriate data visualization tools allows everyone at a company to become a big data analyst and assist the organization in making sense of vast quantities of information.
- Plan your application for the cloud: Big data applications use a tremendous amount of computing resources, many of which need to be dialed up or down depending on how much data is flooding the system at any given time. To succeed, a big data project requires uninterrupted data flow and processing, which in turn requires extensive resource management. Well-managed private and public clouds are designed to support large-scale and ever-changing data processing requirements and are the ideal way of provisioning most big data projects.
Trends in Big Data in 2025
Big data emerged from the rising power and falling cost of computer processing power and data storage, plus the parallel growth of digital business activities that generate increasingly massive volumes of data. Neither of these trends is likely to subside soon. Within these high-level “mega-trends,” though, are several key facets that will shape big data and how it’s used in 2025 and beyond. Here are some noteworthy highlights.
- The use of cloud computing is what makes big data initiatives accessible for midsize and larger businesses. Cloud-supported big data projects will continue to spread, making big data analytics more reliable and affordable over time.
- As advancing technology leads to the integration of more IoT devices, edge computing will become a vital component of big data strategies. Instead of relying solely on cloud infrastructure as before, edge computing will reduce latency and bandwidth issues by bringing data closer to its source.
- The access of data for non-technical users, known as data democratization, will make data analysis accessible for teams without requiring advanced data science knowledge through low- and no-code platforms.
- Big data analytics will continue to be augmented by artificial intelligence and machine-learning technologies. Predictive analytics, automated insights, and data-driven decision-making will increasingly rely on AI for processing vast amounts of data efficiently. AI will also assist in cleaning, sorting, and interpreting data faster than human capabilities allow.
- The rapid growth of the IoT will provide vast new data streams, enabling new and more far-reaching big data applications.
- The relatively new and rapidly growing data-as-a-service (DaaS) market will provide new and more cost-effective ways for businesses to enrich and expand their big data applications, which will create opportunities for data monetization and collaboration between companies.
The Future of Big Data
As the uses of big data continue to grow and mature, and the amount of data that can feed them continues to multiply, more companies will become more heavily invested in big data collection and applications. This will make a company’s data an ever more valuable asset, and many businesses will attempt to monetize this asset by selling it. Thus, data marketplaces will emerge that will lay the basis for even more big data applications, increasing the value of different types of data even further.
At the same time, though, the rise of privacy regulations will require companies to take greater care in the collection of data to avoid the consequences of noncompliance while sustainable data centers become a focus. That means certain types of data — especially consumer behavior data — will become harder to come by. Simply by applying the laws of supply and demand, that means the price of such data will go up.
Companies are becoming more dependent on their big data endeavors both to manage their operations and as a source of revenue. This will ultimately redefine the nature of many businesses — slowly, at first, and then very fast. Sooner or later, every company will be a data-centric company and every business will be using big data.
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Big Data FAQs
What is big data technology?
The simplest definition of big data technology is information systems designed to make sense of datasets that are too large and complex for conventional data management systems to handle. Compared with conventional databases, big data generally has far greater sheer size (volume), many more different types of information (variety) and relevant new data is added much faster (velocity). These “3 Vs” were the characteristics originally understood to set big data apart from normal database systems when the term “big data” came into use in the 1990s.
What are big data basics?
There are three basic types of data used by big data analytics: the structured data found in spreadsheets and relational databases; the unstructured data that comes from images, texts and videos; and semi-structured data, such as website content, which has some of the characteristics of both. Data warehouses and traditional relational databases are used to store and manage structured data, while data lakes are generally used for unstructured and semi-structured data.
What are three examples of big data?
Common sources of big data include the transactional databases maintained by most companies to record and process sales, sensors and systems that record and analyze machine and heavy equipment performance, and social media feeds. All of these produce continuous streams of big data.
What is big data used for?
Big data applications have been developed for most every industry and business sector. Airlines, for example, use big data collected from ticket sales to maximize flight occupancy and revenue. Hospital chains use big data applications to identify disease patterns and manage population health. And consumer products companies use big data to spot sales trends and design products to fulfill customer expectations.