What is the difference between big data and business intelligence?
Big data refers to large data sets that typically exist within organizations. Business intelligence, on the other hand, refers to the way organizations analyze this data to produce insights that can inform business decisions and processes.
Both of these terms are playing an increasingly large role in business operations today, so let’s take a look at both and see what the difference between big data and business intelligence is, how they are used, and what their benefits are.
Don’t just use the data your organization collects; protect it. Watch Impact’s webinar, Keys to Cybersecurity in Manufacturing: Prevent Downtime, Stop Threats, to learn more about what you can do to bolster your cybersecurity.
Big Data
Big data refers to the massive, constantly growing volumes of information that organizations collect, often faster than they can interpret or use it effectively. It’s not just the size that makes it "big," but also the complexity and variety. These datasets often exceed the capacity of traditional data processing tools, requiring more advanced methods to extract value.
A classic example is the flood of data generated by social media platforms. Every like, share, comment, impression, and click-through adds a new piece to the puzzle. Multiply that across millions of users and thousands of interactions per second, and you get an intricate web of behavioral indicators. On their own, these fragments may seem insignificant, but together, they form a rich landscape of data.
When properly harnessed, big data can unlock powerful insights into customer behavior, market trends, operational inefficiencies, and more. But without the right tools or strategy, it often remains just that big and frustratingly underused.
The true potential of big data lies in turning raw information into actionable intelligence. This requires more than just storage—it demands infrastructure capable of real-time analytics, machine learning models to detect patterns, and data governance frameworks to ensure accuracy and compliance.
For example, businesses might use big data to predict customer churn, optimize supply chains, or even personalize marketing down to the individual. But the challenge isn’t just technical—it’s strategic. Organizations must learn to ask the right questions of their data, or risk drowning in a sea of information with no clear direction.
Structured and Unstructured Data
To truly grasp the power of big data, it helps to break it down into two core categories: structured and unstructured data. These distinct types each play a critical role in shaping the insights organizations can extract.
Structured data is the neatly organized, easily searchable information you’d expect to find in databases or spreadsheets. It’s numerical, orderly, and designed for quick analysis—think sales figures, inventory counts, or timestamps. Because it fits into clearly defined fields, structured data is ideal for dashboards, reports, and algorithmic processing.
Unstructured data, on the other hand, is far messier—and far more abundant. It includes everything from customer service transcripts and social media posts to images, videos, voice recordings, and IoT sensor readings.
Unlike structured data, this information doesn’t slot neatly into rows and columns. It’s qualitative, rich in context, and often requires advanced tools like natural language processing or computer vision to interpret.
Both types are essential. Structured data tells you what is happening. Unstructured data helps you understand why, and together, they unlock the full story hidden in your data.
Growth of Big Data
Big data didn’t appear overnight. It grew alongside our increasing reliance on digital technology. At first, organizations collected manageable amounts of information, basic transaction records, website visits, or customer contact details. But as cloud computing, mobile devices, social media, and IoT sensors became mainstream, the volume, velocity, and variety of data exploded.
Every swipe, click, purchase, or GPS ping adds to a company’s digital footprint. And it's not just humans generating data. Machines talk to machines, devices monitor environments, and software logs activity second by second. Over 90% of the world’s data emerged in just the last few years, and it continues to grow at an unprecedented rate.
The scale is staggering, but the real story lies in the shift from collecting data to using it. Businesses no longer store information just for recordkeeping; they analyze it in real time to predict trends, personalize experiences, and make smarter decisions faster. As data keeps multiplying, organizations must find ways to keep up and extract meaningful value.
Business Intelligence
Business intelligence refers to digital tools that are used to analyze data, both structured and unstructured, into actionable insights to inform decision-making.
For most organizations, business intelligence will be most familiar in the context of structured data, though advances in the use of AI and machine learning mean that unstructured information is more commonly being deciphered for use.
Use of Business Intelligence within Organizations
Organizations use business intelligence to stay agile, informed, and ahead of the curve. Teams rely on it to track performance in real time, monitor KPIs across departments, and quickly identify what’s working and what isn’t.
When a sales team sees a sudden dip in conversions or a spike in interest from a new region, BI helps pinpoint the cause and guide the next move.
Executives use BI dashboards to cut through data silos and get a unified view of the business. With insights from customer behavior, supply chain efficiency, marketing engagement, and financial performance, leaders can make faster, more confident decisions. Instead of reacting late, they respond in the moment, with precision.
BI also empowers individuals beyond the C suite. Marketers test messaging and refine campaigns based on what the data reveals. Product teams spot usage trends and prioritize features that matter most to customers. Even frontline employees can access simplified dashboards to track progress toward goals or improve day-to-day workflows.
At its core, BI makes data useful. It doesn’t just surface what’s happening; it clarifies why it’s happening, and helps teams act on it before opportunity passes them by.
Benefits of Using Business Intelligence
Business intelligence gives organizations more than just access to data; it transforms that data into a strategic advantage. It empowers teams to move quickly, align more effectively, and make decisions grounded in real-time insights.
Some of the most impactful benefits include:
- Faster, smarter decision making – Real-time dashboards and automated reports deliver timely insights, helping teams respond to challenges and opportunities without delay.
- Improved cross-team visibility – Centralized data keeps departments aligned, breaks down silos, and ensures everyone works from the same set of facts.
- Early trend detection – BI tools surface patterns in customer behavior, operations, and the market, allowing businesses to act before small changes become major issues.
- Greater efficiency and cost savings – Automated analysis highlights bottlenecks, redundancies, and low-performing areas, allowing organizations to streamline operations.
- Enhanced performance tracking – Teams can monitor KPIs in real time, set clear benchmarks, and continuously optimize their efforts.
With the right BI tools in place, businesses don’t just keep up—they get ahead.
How Does Business Intelligence Relate to Unstructured Data Sets
As we noted earlier, the proportion of structured data as compared to unstructured data is shrinking at quite a rapid rate.
This means not only that businesses that haven’t already done so should look into a strategy that incorporates BI adoption, but also that the leveraging of unstructured data will become a significant hurdle to overcome—if not now, then most certainly in the future.
Since typical BI tools are meant for structured data, artificial intelligence is used to generate actionable information from unstructured sources, which can then be effectively analyzed.
Take, for example, a business that wants to get a better understanding of its most frequent customer complaints.
Service calls can be transcribed through a solution like Dialpad, and this transcription can be assessed with text analysis software to determine commonalities (like words or phrases relating to a particular problem or service) across a wide range of calls.
This data can then be aggregated, structured, and analyzed through business intelligence.
That was a very basic example, but the use of AI for analytics purposes in business will be key for organizations going forward.
Final Thoughts on Big Data vs Business Intelligence
Big data and business intelligence are often mentioned together, but they serve very different roles in the data ecosystem. Big data represents the vast, complex volumes of information organizations collect every second, from customer interactions and operational systems to social media and beyond. It’s raw potential, waiting to be tapped.
Business intelligence, on the other hand, is the system that brings structure, clarity, and direction to that chaos. It’s how organizations make sense of their data, extract insights, and turn information into action. Where big data tells you what’s out there, BI helps you understand what matters, and what to do about it.
When used together, big data and business intelligence become a powerful combination. One supplies the fuel, the other steers the engine. Organizations that know how to balance both are better equipped to make decisions with confidence, adapt quickly, and unlock real competitive advantage.
You should put as much effort into protecting data as you do collecting it. Watch Impact’s webinar, Keys to Cybersecurity in Manufacturing: Prevent Downtime, Stop Threats, to learn more about the important role cybersecurity plays in data protection.
