Big Data vs. Traditional Data

Big Data vs. Traditional Data

Big data provides businesses with immense opportunities, including more significant insights into customer behavior, more accurate forecasts about market activity, and improved efficiency overall.

People and businesses are generating more and more data every year. According to an IDC report, the world created just 1.2 zettabytes (1.2 trillion gigabytes) of new data in 2010. By 2025, it could increase to 175 zettabytes (175 trillion gigabytes) or more.

As businesses tap into this flourishing resource via predictive analytics and data mining, the market for big data will grow, too. Statista research predicts the big data market will double between 2018 and 2027 from a value of $169 billion to $274 billion.

But what are the key differences between big data and traditional data? And what implications do they have on current data storage, processing, and analysis technology? Here, we’ll explain the different purposes each type of data serves while emphasizing the importance of a strategy that plans for success with both big data and traditional data.

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What Is Traditional Data?

Traditional data is structured, relational data organizations have been storing and processing for decades. Traditional data still accounts for the majority of the world’s data.

Businesses can use traditional data for tracking sales or managing customer relations or workflows. Traditional data is often easier to manipulate and can be managed with conventional data-processing software. However, it generally provides less sophisticated insights and more limited benefits than big data.

What Is Big Data?

Big Data can refer to both a large and complex data set, as well as the methods used to process this type of data. Big data has four main characteristics, often known as “the four Vs”:

  • Volume: Big data is...big. While big data isn’t only distinguishable by its size, it’s also typically very high volume in nature.
  • Variety: A big data set typically contains structured, semi-structured, and unstructured data.
  • Velocity: Big data generates quickly and is often processed in real time.
  • Veracity: Big data isn’t inherently better quality than traditional data, but its veracity (accuracy) is extremely important. Anomalies, biases, and noise can significantly impact the quality of big data.

The Differences Between Big Data and Traditional Data

Several characteristics are used to distinguish between big data and traditional data. These include:

  • The size of the data
  • How the data is organized
  • The architecture required to manage the data
  • The sources from which the data derives
  • The methods used to analyze the data


Traditional data sets tend to be measured in gigabytes and terabytes. As a result, their size can allow for centralized storage, even on one server. 

Big data is distinguished not only by its size but also by its volume. Big data is usually measured in petabytes, zettabytes, or exabytes. The increasingly large size of big data sets is one of the main drivers behind the demand for more modern, high-capacity, cloud-based data storage solutions.


Traditional data is normally structured data that’s organized in records, files, and tables. Fields in traditional data sets are relational, so it’s possible to work out their relationship and manipulate the data accordingly. Traditional databases, such as SQL, Oracle DB, and MySQL, use a fixed schema that is static and preconfigured.

Big data uses a dynamic schema. In storage, big data is raw and unstructured. When big data is accessed, the dynamic schema is applied to the raw data. Modern non-relational or NoSQL databases like Cassandra and MongoDB are ideal for unstructured data, given the way they store data in files.


Traditional data is typically managed using a centralized architecture, which can be more cost-effective and secure for smaller, structured data sets.

In general, a centralized system consists of one or more client nodes (e.g., computers or mobile devices) connected to a central node (e.g., a server). The central server controls the network and monitors its security.

Because of its scale and complexity, it isn’t possible to manage big data centrally. It requires a distributed architecture.

Distributed systems link multiple servers or computers over a network, operating as coequal nodes. The architecture can scale horizontally (scale “out”) and will continue functioning even if an individual node fails. Distributed systems can leverage commodity hardware to reduce costs.


Traditional data typically derives from enterprise resource planning (ERP), customer relationship management (CRM), online transactions, and other enterprise-level data.

Big data derives from a broader range of enterprise and non-enterprise-level data, which can include information scraped from social media, device and sensor data, and audiovisual data. These source types are dynamic, evolving, and growing every day.

Unstructured data sources can also include text, video, image, and audio files. Leveraging this type of data isn’t possible using the columns and rows of traditional databases. Because an increasingly significant majority of data is unstructured and comes from multiple sources, big data analysis methods are required to extract value from it.


Traditional data analysis occurs incrementally: An event occurs, data is generated, and the analysis of this data takes place after the event. Traditional data analysis can help businesses understand the impacts of given strategies or changes on a limited range of metrics over a specific period.

Big data analysis can occur in real time. Because big data generates on a second-by-second basis, analysis can occur as data is being collected. Big data analysis offers businesses a more dynamic and holistic understanding of their needs and strategies.

For example, suppose a business has invested in a training program for its staff and wants to measure its impact.

Under a traditional model of data analysis, the business might set out to determine the impact of the training program on a particular area of its operations, such as sales. The business notes the sales volume before and after the training and excludes any extraneous factors. It can, in theory, see how much sales have increased as a result of the training.

Under a big data model of analysis, the business can set aside questions regarding how the training program has impacted any particular aspect of its operations. Instead, by analyzing a mass of data collected in real time across the whole business, it can identify the specific areas which have been impacted, such as sales, customer service, public relations, and more.

Big Data vs. Traditional Data: Important Considerations for the Future

Big data and traditional data serve different but related purposes. While it may seem as if big data has greater potential benefits, it isn’t suitable (or necessary) in all circumstances. Big data:

  • Can provide a deeper analysis of market trends and consumer behavior. Traditional data analysis can be more narrow and too restricted to deliver the meaningful insights big data can provide.
  • Provides insights faster. Organizations can learn from big data in real time. In the context of big data analytics, this can provide a competitive edge.
  • Is more efficient. The increasingly digital nature of our society means people and businesses are generating vast quantities of data every day—and even every minute. Big data allows us to harness this data and interpret it in a meaningful way.
  • Requires advanced preparation. Leveraging these benefits requires that organizations prepare for big data through new security protocols, configuration steps, and increases in available processing power

The rise of big data doesn’t mean that traditional data is going away. Traditional data:

  • Can be easier to secure, which may make it preferable for highly sensitive, personal, or confidential data sets. Because traditional data is smaller, it doesn’t require distributed architecture and is less likely to require third-party storage.
  • Can be processed using conventional data-processing software and a normal system configuration. Processing big data generally requires a higher-configuration setup, which can increase resource usage and costs unnecessarily when traditional data methods will suffice.
  • Is easier to manipulate and interpret. Because traditional data is simpler and relational in nature, it can be processed using normal functions—and may even be accessible to nonexperts.

Ultimately, this isn’t a question of choosing between big data and traditional data. As more and more companies generate large, unstructured data sets, they’ll need the right tools in place. Understanding how to use and support both models is a necessary part of updating your strategy to be ready for a big data future.

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