What is big data?

Big data refers to massive, complex datasets — often measured in terabytes or petabytes — that are too vast for traditional database tools to handle. Instead, these datasets are analyzed using advanced computational techniques to uncover patterns, trends, and insights, especially about human behavior and interactions.

As data grows exponentially, it’s found everywhere — from traffic signals to point-of-sale systems. The sheer volume, speed, and variety of this data demand real-time insights for businesses to stay competitive. Whether improving decision-making or enhancing operational efficiency, big data provides a significant edge.

Explore the origins, benefits, challenges, and future of big data in this guide.

• What are the origins of big data?
• What are the three Vs of big data?
• Why is big data important?
• How does big data work?
• Big data benefits
• Big data challenges
• What are some common big data use cases?
• Big data: Key takeaways
• How will the use of big data continue to evolve?

What are the origins of big data?

Big data originates from database management. Though data has been around for millennia, the term became necessary to convey large amounts of data once its volume, velocity, and variety exceeded human capability and comprehension. When floods of digital information started coming in, companies needed to create tools to successfully store and manage it at volume, and gain useful insights from it.

Many organizations in the IT space, especially those in Silicon Valley, have focused on creating frameworks to deal with big data. These were created to deal with scenarios where there is so much data it can’t possibly be processed by a small number of machines.

Today, there are three common types of big data:

• Structured data. Refers to data displayed in well-defined tables

• Unstructured data. Includes data points like logins, website clicks, page views, or video views

• Semi-structured data. Contains a mix of structured and unstructured

What are the three Vs of big data?

1. Variety (the different data types or formats). Refers to the various compositions of datasets. Structured, unstructured, and semi-structured data are examples of variety within data.
2. Velocity (the speed at which data becomes available). Describes how quickly data becomes available to the organization collecting it. Adobe, for example, collects over 250 trillion transactions a year, which equals around 475 million transactions a minute.
3. Volume (the amount of data collected). Refers to the pure amount of data collected. For example, if YouTube users upload 380,000 hours of video an hour, that is a high volume of data. If an organization is dealing with 380,000 emails an hour, the volume of the data is significantly less, but the velocity is still high.

But let’s not forget:

• Variability. Deals with establishing context and comprehending how data is constantly changing. If the same process constantly gives a different result, that’s variability.

• Veracity.  Refers to accuracy. Companies need to ensure data quality. Untrustworthy data is useless data.

• Value.  The culmination of the previous five Vs. It’s the profit your company sees from the data.

Why is big data important?

Companies must harness the power of big data to understand the big picture. The more data an organization has, the more well-informed decisions it can make. Companies want to understand how their customers interact with their brand and, for organizations with enormous global audiences, that requires large volumes of data.

One increasingly important use of big data is understanding customer needs. Providing a premier customer experience and evolving to meet the needs of the customers is no simple or easy task. Organizations need to understand where their customers come from, what they do on the website, how much time they spend on the website, and how often they complete a transaction or convert.

Behavioral data is collected from customer behavior on websites and channels such as mobile, email, and so on. Transactional and personal information may also be collected.

Understanding this data can give you important insights into improving sales velocity and how to optimize digital experiences. Many decisions around optimization boil down to the amount of data available and the insights that can be pulled from that data.

How does big data work?

Big data integration.

The first step, data processing and collection, involves creating an infrastructure for collecting all the data points coming in. The infrastructure will depend on the type of data, but the raw data always persists somewhere so that further analysis can happen as needed.

During this step, you’ll need to integrate the collection of data from various sources and applications. This requires collecting, processing, and formatting the information correctly so your data analysts can get to work.

Big data management.

The next question is how to store and organize data. It’s important to clearly determine where the data should live and how to catalog it so other systems know it exists. Common ways to do this are in data lakes and data warehouses.

Data is only as useful as the metadata that describes it. If an organization has large volumes of data, but no way of discovering it or informing someone what it’s about, the data has no benefit. As far as data storage, big data is typically stored in the cloud, although servers are another popular route.

Big data analytics.

After data is stored and managed, it can then be analyzed for insights and patterns. The insights derived from big data analytics can then be visualized to inform stakeholders and make recommendations for the organization’s next steps.

This includes using advanced analytics engines like Apache Spark or Databricks, which make it easier to manage large amounts of stored data. Companies also use big data technologies built around messaging, like Kafka, which specializes in processing streaming data that is continuously generated. An organization may also choose to build and manage their own custom framework.

Big data benefits.

1.     Improve operations. With the right data analysis tools, you can optimize business processes, streamline resources, and reduce costs.

2.     Detect waste and fraud. Big data can show you patterns and insights that may otherwise have gone undetected. With data analytics, you can be proactive and mitigate risk.

3.     Discover customer insights. Companies can learn more about their customers’ behaviors and appropriately personalize products in their marketing campaigns.

4.     Gain a competitive advantage. You’ll have insider knowledge of competitor and market trends and insights, keeping you nimble to adapt quickly to changing customer demands.

Big data challenges.

1.  Difficult to manage. Data collection itself is not enough. Organizations must be able to access, analyze, and shape the data. Unstructured and semi-structured data is often difficult to work with. Without proper management, the data can eat into costs without really providing any value.

2.  Takes time to master. Better big data management comes with maturity. If an organization is starting to explore data for the first time, they may want to slow down and make sure they are asking the right questions. There can also be biases or anomalies in the data, which may not be apparent when first using big data.

3.  Data protection considerations. Companies also must be careful about how they use the data they collect. For example, they may collect personally identifiable information (PII), but they may not want to or have permission to use that information for certain marketing actions.

4.  Ensuring the correct framework. Having a proper framework for data governance will help prevent mistakes of improper data access and use. It helps to maintain compliance with regulations by ensuring that data is properly labeled for its intended use.

What are common big data use cases?

1. Operations. You can use big data to optimize your supply chain through demand forecasting, real-time inventory management, and predictive maintenance.
2. Machine learning. Big data can train machine learning models for predictive analysis. The more data it has access to, the more accurate the predictions.
3. Security. Detect threats to confidential intelligence and fraud of any sort by leveraging big data and applying machine learning algorithms.
4. Product development. You can use big data to inform how your products evolve. Analytics such as test markets, focus groups, and social media can be extremely useful for understanding — and emphasizing with — your customers’ pain points.

Big data: recap.

• Big data is usually stored in computer databases. It’s analyzed using software specifically designed to handle large, complex datasets.

• It can be collected from all sorts of sources. For example, publicly shared comments on social networks, voluntarily gathered from apps, or manually retrieved through digital check-ins.

• Big data can be structured. It’s often organized neatly within the columns of a database and is relatively easy to enter, store, query, and analyze.

• It can also be unstructured. For example, as more free-form and less quantifiable data, like emails, social media posts, video files, and web pages.

• Big data is endlessly useful. Nearly every department in a company can use findings from big data analysis.

• It is crucial companies protect individuals while collecting big data.  Data breaches can have serious consequences, so companies must take steps to secure their data.

How will the use of big data continue to evolve?

Big data is evolving rapidly, driven by the demand for real-time insights, decision-making, and action. Gone are the days when companies could afford to sit on data for 24–48 hours. Today, success hinges on reacting instantly to customer behavior.

This shift from batch processing to real-time analytics is powered by advanced technologies like machine learning and AI, which take this further, enabling faster, smarter data analysis.

Backed by Adobe Sensei, Customer Journey Analytics uses AI to deliver predictive insights based on the full scope of your data. When you’re ready to get started, Analytics turns real-time data into real-time insights, for effective data solutions.

Watch an overview video or request a demo to learn how Adobe Analytics, Customer Journey Analytics, and Product Analytics can optimize your big data’s potential.

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