Big Data Definition

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Definition - What does Big Data mean? Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be. Aug 15, 2013 Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis. Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making,.

What is big data? That’s a good question. There seems to be as many definitions for big data as there are businesses, nonprofit organizations, government agencies, and individuals who want to benefit from it.

One popular interpretation of big data refers to extremely large data sets. A National Institute of Standards and Technology report defined big data as consisting of “extensive datasets—primarily in the characteristics of volume, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis.” Some have defined big data as an amount of data that exceeds a petabyte—one million gigabytes.

Another definition for big data is the exponential increase and availability of data in our world.

This data comes from myriad sources: smartphones and social media posts; sensors, such as traffic signals and utility meters; point-of-sale terminals; consumer wearables such as fit meters; electronic health records; and on and on.

Buried deep within this data are immense opportunities for organizations that have the talent and technology to transform their vast stores of data into actionable insight, improved decision making, and competitive advantage.

Big data defined. Here’s our definition: Big data is the data that is characterized by such informational features as the log-of-events nature and statistical correctness, and that imposes such technical requirements as distributed storage, parallel data processing and easy scalability of the solution. Oct 07, 2020 'Big data' refers to data sets that are too large or too complex for traditional data processing applications. The term is often used to refer to predictive analytics or other methods of.

By harnessing the power of big data, healthcare systems can identify at-risk patients and intervene sooner. Police departments can predict crime and stop it before it starts. Retailers can better forecast inventory to optimize supply-chain efficiency. The possibilities are endless.

But to fulfill this promise, organizations need qualified professionals with the skills to extract meaning from the mountains of data—and these elusive data scientists are in short supply.

The “Three Vs” of Big Data

In 2001, industry analyst Doug Laney defined the “Three Vs” of big data:

  1. Volume

    The unprecedented explosion of data means that the digital universe will reach 180 zettabytes (180 followed by 21 zeroes) by 2025. Today, the challenge with data volume is not so much storage as it is how to identify relevant data within gigantic data sets and make good use of it.

  2. Velocity

    Data is generated at an ever-accelerating pace. Every minute, Google receives 3.8 million search queries. Email users send 156 million messages. Facebook users upload 243,000 photos. The challenge for data scientists is to find ways to collect, process, and make use of huge amounts of data as it comes in.

  3. Variety

    Data comes in different forms. Structured data is that which can be organized neatly within the columns of a database. This type of data is relatively easy to enter, store, query, and analyze. Unstructured data is more difficult to sort and extract value from. Examples of unstructured data include emails, social media posts, word-processing documents; audio, video and photo files; web pages, and more.

Data

What Is Big Data

Beyond the Big Three Vs

Big Data Definition Business

More recently, big-data practitioners and thought leaders have proposed additional Vs:

  • Veracity

    This refers to the quality of the collected data. If source data is not correct, analyses will be worthless. As the world moves toward automated decision-making, where computers make choices instead of humans, it becomes imperative that organizations be able to trust the quality of the data.

  • Variability

    Data’s meaning is constantly changing. For example, language processing by computers is exceedingly difficult because words often have several meanings. Data scientists must account for this variability by creating sophisticated programs that understand context and meaning.

  • Visualization

    Data must be understandable to nontechnical stakeholders and decision makers. Visualization is the creation of complex graphs that tell the data scientist’s story, transforming the data into information, information into insight, insight into knowledge, and knowledge into advantage.

  • Value

    How can organizations make use of big data to improve decision-making? A McKinsey article about the potential impact of big data on health care in the U.S. suggested that big-data initiatives “could account for $300 billion to $450 billion in reduced health-care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health-care costs.” The secrets hidden within big data can be a goldmine of opportunity and savings.

Bringing It All Together

No matter how many Vs you prefer in your big data, one thing is sure: Big data is here, and it’s only getting bigger. Every organization needs to understand what big data means to them and what it can help them do. The possibilities really are endless.

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