What Is Big Data? Big Data Explained

Big data has become more than a buzzword as information has grown more complex and vast in quantity and organizations struggling to gather, curate, understand, and use data effectively. It also describes challenges in IT, business, as well as emerging analytics technologies. But where did the term come from, how can you use big data at your organization, and how can you advance your big data analytics strategies? We’ll address these questions and provide tips to get started using your big data.

History of big data

In the 1960s, the United States created a large data center to store millions of tax records. This data center was the first real use case of digital data management. Through the 1990s and 2000s, leaders in the data space worried that existing technology would not be able to store large amounts of information produced by businesses, by government, and by people around the world. An even larger worry: would anyone be able to make sense of that much data. Today, thanks to technology innovations and more sophisticated analytics capabilities, it’s become cheaper and easier to store data and then analyze it. Now, concerns have shifted to effectively using big data in many formats such as structured, unstructured, and semi-structured, to inform business decisions.

The four Vs of big data

Data professionals describe big data by the four “Vs.” These characteristics are what make big data a big deal. The four Vs distinguish and define big data and describe its challenges.

1. Volume

The most well-known characteristic of big data is the volume generated. Businesses have grappled with the ever-increasing amounts of data for years. However, now it’s possible to store data for pennies on the dollar using data lakes or data warehouses like Snowflake. Businesses prioritize data organization with platforms like Hadoop, but it’s important to develop policies that standardize how long users keep data, and then a procedure for deleting or archiving it. Ultimately, it doesn’t matter how big the data is if you can’t use it. With increasing volume, users’ hands will be tied behind their backs unless information is stored and governed by an agile, accessible framework. Without strategies to use and access huge volumes of data, that data will sit stagnant, lose value, and fail to surface insights.

2. Velocity

Not only are businesses producing a lot of data, but they are also doing it at an ever-increasing rate. Customers and employees use many applications to complete data-driven tasks. Technologies have to be ready for the speed and volume of that data to keep up with the pace of business. Because the volume is high, velocity becomes more and more difficult to manage as it becomes more important. Speed to insight is a serious consideration in both data software as well as data structure.

3. Value

Velocity, volume, and variety were the three original characteristics associated with big data. However, leaders in the space have added value, recognizing the opportunity and need to use big data for business transformation—both to fulfill goals and metrics, uncover risks and opportunities that no one realized, and much more. Analytics technologies have evolved and can now augment analysts’ abilities to find correlations, identify outliers, and predict outcomes with data. For example, sales teams can use a platform to connect data from social media, eCommerce, and sales for a full picture of the customer journey—from awareness to conversion. As another example, AI technologies like Explain Data can suggest possible explanations for outliers for analysts to drill into.

4. Variety

As digital transformation occurs across different industries and all sizes of organizations, that means there are more types of data to store and analyze, plus more sources to pull from. The variety of structured has exploded and it has become more important to leverage metadata as well as ways to wrangle unstructured data. Location data In the following section, we will review the types of big data that exist.

Create beautiful visualizations with your data.

Try Tableau for free

Graphic of visualizations

Types of big data

Big data is crucial because of its untapped potential, but recent technology such as visual analytics finally allows businesses to discover critical, even surprising insights that give us a clearer view into processes and human behaviors. On some occasions, these processes and behaviors must be refined for the sake of the business and its future success. And recognizing that no organization is exactly the same, the technology providers that invest ahead of the curve in big data—whether that’s creating partnerships in the data ecosystem or developing capabilities that improve data access and connectivity—they’re the ones to watch and embrace.

Structured data

Structured data is the neatly organized data you keep in databases, datasets, and spreadsheets. It’s easy for traditional analytics tools to read this data. Organizing unstructured data into structured data is time-consuming, but possible with the right solution. It involves data cataloging, data mapping, and data transformation. You can learn more about these processes here.

Unstructured data

Unstructured data, or raw data, is increasing at a higher rate compared to structured data. Platforms like Facebook generate hundreds of terabytes of information per day. Unstructured data can also include survey data from customers, notes, and emails. Because unstructured data is growing, big data technologies that can seamlessly analyze this data will be crucial to businesses. Solutions like Hadoop are very adept at ingesting raw data for analysis.


Semi-structured data has some organizational structure, but isn’t easy to analyze as-is. With some organizing or cleaning, semi-structured data could be imported into a relational database just like structured data. Semi-structured data and structured data can be analyzed and visualized with solutions like Tableau. With a combination of solutions like Hadoop and Tableau, all three of these types of data can be used for analysis.

Three best practices to follow with big data

It’s easy to be overwhelmed by big data, but the good news is that technologies and analytics platforms are becoming more efficient and comfortable to use. Industries and teams such as sales, IT, and government agencies have used their big data to discover trends and reduce analysis time. To effectively use big data, focus on the following best practices:

1. Build with flexibility and long-term sustainability in mind

A big data best practice is to always think about long-term solutions. As discussed, big data is growing at a fast, steep trajectory; so your data management solution and strategy should scale with it. Technology will evolve and work together more easily. Don’t be afraid to upgrade to new innovative solutions. Example: Abercrombie & Fitch saw the benefits of upgrading from spreadsheets and deployed Tableau fast so they could use match shopper insights with inventory.

2. Combine the right technology solutions for your needs

Secondly, be aware that companies often need more than one solution to manage their big data from first ingestion to final data visualizations. This isn’t a bad thing. The best big data platforms can talk to each other and form a symbiotic relationship. Example: PepsiCo worked with Tableau and Trifacta to wrangle disparate data and uncover insights.

3. Empower people to see and understand data

Third, keep in mind that data cultures empower their business teams to use and play with data. With big data, all hands are on deck. That’s the only way to keep up with the volume and velocity of data. Example: Charles Schwab knew this, so they democratized data analysis across hundreds of branch locations. Read more about best practices for big data.