Guide to Big Data

The more data your business generates and collects, the more important it is that you understand big data, which has become both a necessity and a competitive advantage. What exactly is big data, and why does it matter?

Big data refers to the extremely large and complex datasets that traditional data-processing software can’t manage; at least, not efficiently. While the size of the data is important, it’s also about what’s hidden within. With the right tools and strategies, such as AI and agentic AI, you can transform raw data into actionable intelligence that supports personalized customer experiences.

Explore what big data is, its benefits and challenges, and best practices to help you turn big datasets into opportunities for improved decision-making and increased revenue.

Big data is a dataset so large and fast-moving that traditional data-processing systems can’t handle them. Think of it as an overflowing library where books are constantly being added and updated. There might be a lot of useful information there, but it’s difficult to find among all the options.

Big data’s growing importance comes from its sheer scale and impact on industries. Today, many organizations rely on big data to anticipate customer needs and optimize daily operations.

Big data isn’t just defined by its size. It’s characterized by four dimensions that shape how you manage and interpret it: volume, velocity, variety, and veracity. These foundational “4 Vs” get to the heart of the potential of big data.

• Volume: Every second, people and systems generate an unprecedented amount of information. For example, roughly 333 billion emails are sent daily , and streaming platforms process millions of views and downloads. This large scale makes it harder for traditional storage and analytics tools to keep up.
• Velocity: The speed at which data is both created and processed is an important factor in its usability. Think of the data generated by financial markets or online shopping trends during a flash sale. Each of these situations require instant analysis to make timely decisions about stock trades or inventory restocking. Big data systems must process information at lightning speeds, ensuring insights lead to action.
• Variety: Big data comes in many formats; a retail company might analyze transactional data alongside customer reviews and product images. Using platforms that integrate data from multiple sources makes it easier to consolidate and interpret these varied datasets so that you can see a comprehensive view of customer behavior.
• Veracity: Maintaining data quality and accuracy is important for finding trustworthy insights. Removing duplicate entries and filling in incomplete records can significantly improve the reliability of your analytics.

Big data encompasses several types, each serving a specific purpose — and presenting specific challenges. Understanding these types is crucial if you want to get the most out of your data assets. Below are some examples.

Structured data is the most organized type of data. It can be arranged in rows and columns, such as in an Excel spreadsheet or a relational database. Examples include:

• Customer names, contact details, and purchase histories in an ERP or CRM
• Financial transaction records in spreadsheets

For instance, a sales team might analyze structured data to track quarterly revenue and forecast buying trends.

Unstructured data lacks a defined format, which can make it harder to process. However, it is still full of useful insights. Examples of unstructured data include:

• Social media posts, images, and videos
• Customer feedback from surveys or product reviews

You can analyze unstructured social media content to gauge customer sentiment before launching a new product.

Semi-structured data sits between structured and unstructured. It typically contains tags or markers that provide some organization. Examples include:

• JSON or XML files
• Email metadata, such as sender, subject, and timestamp

For instance, a marketing team might parse JSON data from an email campaign to measure open and click-through rates.

Metadata is data about data, providing context and meaning. Examples of metadata include:

• Photo metadata such as date, location, and camera settings
• Document properties, including file size, format, and author

Metadata helps you organize and find data by making datasets unique and providing a framework for how you use them.

Time series is data that you collect and index based on time. Doing so allows you to perform trend analysis over intervals. Examples of time-series data include:

• Stock prices and market trends
• IoT sensor data, like temperature readings

Monitoring current sensor data in manufacturing to prevent equipment failures is an example of this data in action.

Geospatial data provides information about geographic locations and relationships. Examples include:

• GPS coordinates and maps
• Satellite imagery

You might use geospatial data to optimize delivery routes, improving efficiency and reducing costs since drivers know exactly where to go.

Streaming data refers to data generated and processed as soon as it comes in. Examples include:

• Live customer interactions on a website
• Sensor data from connected devices

Detecting fraudulent transactions as they occur using AI-powered fraud detection tools is an example of streaming data at work.

Big data acts as the cornerstone of innovation and data-driven decisions. Organizations across various industries invest heavily in big data because of its transformative potential. Here are some of the top reasons big data matters — and how it powers innovation for personalized customer experiences.

Data-driven decisions aren’t just smarter — they’re faster and more effective. By analyzing large amounts of data, you can uncover trends in customer interactions and mitigate risks of cyberattacks with greater precision. For example:

• Retailers optimize inventory by predicting demand patterns.
• Financial institutions use analytics to detect fraud quickly.

As you use your data insights, you can better adapt to challenges and find opportunities to improve your processes that might otherwise go unnoticed.

AI and agentic AI thrive on data, and big data provides the fuel it needs. Machine learning algorithms, in particular, rely on massive datasets to improve accuracy in personalization. They also use advanced capabilities like natural language processing (NLP) to analyze customer sentiment and computer vision to identify product preferences from images or videos. Big data’s significance is tied to its ability to empower you to think bigger, move faster, and deliver more value to your business and your customers once you analyze and act on the data. As you integrate big data into your operations, you can unlock new growth opportunities, including expanding into untapped markets with predictive analytics.

Big data goes beyond merely providing information to create measurable outcomes. By effectively using big data, you can transform how your business operates on a daily basis. This transformation may include:

• Enhanced customer experiences: Big data helps you understand your customers on a deeper level. As you analyze customer preferences and feedback and use agentic AI to act on the data, you can deliver tailored experiences that stand out from the competition.
• Faster decision-making: Access to the most up-to-date data allows you to respond to opportunities and challenges quickly and effectively. With big data analytics, decision-making shifts from reactive to proactive.
• Increased efficiency and cost savings: Big data can help you identify any inefficiencies and automate routine processes.
• Improved risk management and compliance: By monitoring and analyzing data continuously, you can detect anomalies and address potential risks before they escalate.

While big data has the potential to transform your business, using it comes with certain hurdles. Addressing these challenges is the first step in making sure your big data initiatives deliver value rather than wasting valuable resources.

• Integration complexity: Big data often comes from a wide range of sources, such as CRMs, ERPs, and social media. Integrating these data sources into a data platform where it can be analyzed requires advanced technology and careful planning.
• Extracting actionable insight from data: Collecting data is one thing; making sense of it is another. Without the proper analytics or AI tools, you may find it difficult to convert raw data into insights that lead to clear actions.
• Data literacy and skill gaps: A shortage of skilled employees who analyze and interpret big data can be a challenge. Upskilling teams or hiring specialists can help bridge this skills gap so your organization can effectively use the insights gained from big data.
• Ethical concerns and data privacy: As you collect and use more data, it is important to prioritize compliance with regulations such as GDPR and CCPA. Mishandling data can lead to reputational damage and legal repercussions.

By recognizing these challenges and adopting solutions such as data integration platforms, you can get more return on your big data investments.

The power of big data ultimately depends on the strategy behind it. A strong data strategy unifies and activates data from multiple sources, which creates a single source of truth for consistency and accessibility. If you’re a user of Salesforce Data 360, you can integrate customer data across marketing, sales, and service platforms to create a unified customer profile. Advanced analytics lead to actionable insights that allow you to forecast trends and make data-driven decisions. You can build scalable strategies that maximize the value of your data assets and overcome common challenges.

Recent advancements in agentic AI are unlocking the potential of big data. For example, agentic AI can simplify your operations and optimize healthcare outcomes. The applications for big data are both vast and varied, but here are some examples of how it can transform your business.

Big data is revolutionizing healthcare by improving diagnostics and patient care. Agentic AI can not only analyze large amounts of patient data to identify patterns and provide early diagnoses, but take action too–scheduling patient meetings and assisting clinicians.

Big data can power hyper-personalized experiences for customers, which can strengthen brand loyalty. Retailers and e-commerce companies can use agentic AI to segment audiences and deliver targeted campaigns, increasing engagement and ROI. And e-commerce platforms may recommend products based on the latest browsing and purchase data for a specific customer.

Manufacturers can use big data to streamline processes and reduce costs. Predictive maintenance, for example, can use data from IoT sensors to anticipate equipment failures before they happen, preventing costly downtime. Big data can also optimize supply chains by analyzing demand patterns and improving inventory management for faster delivery times.

Financial institutions often use big data to improve decision-making. For example, big data can analyze credit histories and market trends to improve loan underwriting processes, which leads to more accurate lending decisions. Advanced analytics helps banks identify upcoming investment opportunities, providing a competitive edge while maintaining compliance with industry standards.

To get the most out of your data, you can adopt strategies that ensure data is collected, managed, and analyzed effectively. Here are some best practices to guide your big data initiatives.

Start with a clear understanding of what you want to achieve. Whether it’s improving customer engagement or making operations more efficient, matching big data initiatives with specific goals supports both focus and measurable outcomes.

Poor data quality leads to inaccurate results. Implement strong data governance policies to make sure everything is complete, accurate, and consistent.

Protecting sensitive information is critical to building trust and maintaining compliance with regulations such as GDPR and CCPA.

Encourage collaboration between teams by promoting the value of data in decision making. Fostering a data-driven culture helps employees approach challenges with insights backed by data. In this type of culture, you are likely to see smarter strategies and more impactful results across departments, such as sales and marketing. This cultural shift positions your company to adapt quickly to changing market demands.

As data grows, it is important that your systems do as well. Investing in extensible architectures and cloud-based solutions can help you handle increasing data volumes and complexity.

Wondering how you can harness the power of big data? Connect with Salesforce to learn how our big data solutions can support your business.