Your organization might collect massive amounts of data, but how quickly can you extract value from it? Even if you use one of the popular business intelligence platforms, the gap between data collection and actionable insight can cost you opportunities and competitive advantages.
Big data and AI close that chasm by turning raw information into immediate answers. Here's what that transformation actually looks like and how it changes the way you work.
How can you think about big data and AI
Big data and artificial intelligence (AI) work together as fuel and engine. Big data provides the massive amounts of information that AI platforms need to learn patterns and make predictions, while AI gives you the tools to analyze datasets so large and complex that traditional methods would take forever.
What is big data?
Big data refers to extremely large datasets that grow fast and come from many sources. It's often defined by the "five Vs":
Volume: The sheer size of data being collected
Velocity: The speed at which data is generated and processed
Variety: Different types and formats of data
Veracity: The accuracy and trustworthiness of data
Value: The usefulness and insights you can extract
These fundamental characteristics make big data challenging to work with, but they're also integral parts of how big data helps your business win. Big data can provide an up-to-the-minute view of many different aspects of your business, but only when it's harnessed correctly.
What is artificial intelligence?
Artificial intelligence is an umbrella term that includes technologies which perform tasks requiring human-like intelligence:
Machine learning: Systems that learn from data without explicit programming
Natural language processing: Understanding and generating human language
Deep learning: Neural networks that recognize complex patterns
These AI technologies work together to transform big data into actionable insights. When applied to massive datasets, they can identify opportunities and risks that would be impossible to spot manually.
The power of combining big data and AI
When you combine artificial intelligence with big data, you get powerful AI analytics systems that can spot trends in customer behavior, predict equipment failures, or detect fraud instantly. (We’ll talk more about some important applications later in this guide.)
As your AI processes additional examples over time, it refines its understanding and reduces errors. This creates a compounding effect where better data leads to better predictions, which in turn helps you collect even more valuable data. Next, let’s look at how.
How big data and AI work together
When you understand how big data and AI work, you’ll quickly see why this combination is so powerful. The process creates a continuous cycle where each step makes the next one better, with AI learning from massive datasets to deliver increasingly accurate insights.
Here’s a basic idea of how the process works:
1. Data collection and storage
You start by gathering information from everywhere: customer clicks, sensor readings, social media posts, and transaction records. Modern cloud platforms like Snowflake and Google BigQuery store this data in ways that make it easy to analyze later.
Data volume matters. AI models become more precise as they’re trained on larger datasets, distinguishing between meaningful patterns and random noise to give you reliable predictions you can trust. When you harness it at scale, data becomes more than a static resource: It's a dynamic learning environment where AI continuously refines its understanding of your business.
2. AI model training and deployment
Data scientists use your collected information to train AI models. These models learn to recognize patterns, predict outcomes, and understand context. The training process requires time and computing power, but once it’s complete, the models can process new data almost instantly.
While you or your analysts might spot simple trends in spreadsheets, machine learning models can examine billions of data points simultaneously. Ultimately, this lets them find connections between seemingly unrelated factors that are often invisible to human analysis.
3. Insight generation and delivery
AI is the secret sauce that's enabled unprecedented levels of personalization at scale for industry behemoths like Amazon and Netflix. Here's what that means for business intelligence: Instead of waiting for reports, you can ask questions and get visual answers immediately. This moves you from passively viewing data to actively having a conversation with it.
Consider Verivox, a comparison platform that struggled with slow time-to-insight and static dashboards. After embedding ThoughtSpot's AI-powered analytics directly into their platform, they saw immediate results: user adoption increased, teams accessed instant insights, and they successfully monetized their data assets.
4. Continuous learning and improvement
Every question you ask and decision you make creates new data. This feedback loop helps AI models become more accurate and personalized over time, making your next interaction even more valuable.
The continuous improvement also goes beyond individual queries. AI analysts let your team automate routine analytical tasks, freeing you to focus on strategic work rather than manual data preparation and report generation.
This four-step cycle—collect, train, generate insights, and improve—repeats continuously, creating a system that becomes smarter and more valuable with every interaction.
Examples of big data and AI in action
Seeing real applications helps you understand how big data in AI creates value across different industries.
Improving healthcare outcomes
If you’re a professional in the healthcare space, think about the data flowing through your system right now: electronic health records, medical imaging, lab results, wearable device readings. That's where healthcare BI becomes essential to your operations. Every day brings new ways to turn AI assistance into better patient outcomes, from predicting which patients face readmission risks to recommending treatment plans tailored for patients' genetic makeup.
Retail optimization
Your retail operation generates data at every touchpoint: transactions, website clicks, supply chain movements. AI algorithms work through these patterns to tell you what customers will want next week, when to adjust your pricing for maximum impact, and which products to recommend to each shopper. This is AI marketing analytics in action, driving a strategy where you’re planning two moves ahead and leading the conversation.
Financial fraud detection
Banks process millions of transactions daily, making manual fraud detection impossible. AI systems analyze transaction patterns, location data, and spending behavior instantly to flag suspicious activity before significant damage occurs.
Machine learning models continuously adapt to new fraud patterns, reducing false positives while catching sophisticated schemes that evolve faster than rule-based systems can track. This real-time protection saves financial institutions millions while maintaining seamless experiences for legitimate customers.
Examples of big data analytics solutions
There's a whole ecosystem of big data tools out there, but they typically fall into several key categories, which include:
Data storage platforms
Cloud data warehouses: Snowflake, Google BigQuery, and Amazon Redshift store petabytes of structured data
Lakehouses: Databricks combines the flexibility of data lakes with the performance of warehouses
Streaming platforms: Handle data as it arrives instantly from various sources
Processing frameworks
Batch processing: Apache Spark handles large-scale data processing jobs
Stream processing: Apache Flink processes data in real time
Workflow orchestration: Platforms like Apache Airflow manage complex data pipelines
Analytics and BI platforms
Traditional BI tools: Platforms like Tableau and Power BI offer visualization and reporting capabilities
AI-powered analytics: ThoughtSpot Analytics connects directly to cloud data warehouses, letting you explore billions of rows using natural language search
Embedded analytics: Platforms like ThoughtSpot Embedded let you integrate analytics capabilities directly into business applications
AI and ML platforms
Model development: Cloud-based machine learning services for building and training models
MLOps platforms: Tools for deploying, monitoring, and managing AI models in production
AutoML platforms: Automated machine learning platforms that handle model selection and tuning
Your specific needs will determine which platforms make sense for your organization. You don't necessarily need every tool listed here, so focus on building a stack that addresses your current challenges and scales with your ambitions.
Agentic AI for big data
The next evolution of analytics is happening right now in the field of agentic AI: autonomous systems that proactively find insights, suggest actions, and execute tasks on your behalf. Here's how to start implementing this exciting technology in a way that makes sense for your business:
1. Align agentic AI with business goals
Start by understanding how agentic AI can connect to your BI and analytics strategy. Define clear ROI expectations and identify specific use cases where autonomous analysis creates measurable value. Some common choices include automated anomaly detection, proactive trend identification, and continuous performance monitoring.
2. Keep humans in the loop
Always maintain human oversight in AI-assisted workflows. AI systems can experience hallucinations and amplify biases present in training data. Implement review and approval workflows for critical decisions, ensuring your team validates AI-generated insights before taking action.
3. Implement a measurement framework
Track both immediate wins and long-term gains by measuring time saved, decision quality improvements, and business outcomes. Understand how your AI projects line up with your most important KPIs and develop a vision of what success looks like.
4. Upskill business teams
Data literacy is a mission-critical skill for teams working with AI in 2026 and beyond. Train your teams to work alongside autonomous systems, understand AI capabilities and limitations, and ask better questions that guide AI agents toward valuable insights.
5. Choose the right analytics platform
Evaluate platforms for native AI capabilities, intuitive interfaces, and seamless integration with your cloud data warehouse. Look for AI analytics platforms that support both human-driven exploration and autonomous agent workflows.
Put your big data and AI to work
Your cloud data warehouse holds answers to questions you haven't even thought to ask yet. ThoughtSpot connects directly to that data and lets you explore it the way you'd talk to a colleague—type your question in plain language, get instant visualizations, and follow the thread wherever it leads without submitting ticket requests or waiting for the next sprint.
Spotter AI Analyst anticipates where you're headed. It surfaces relevant insights before you ask, suggests angles you might have missed, and remembers the full context of your analysis. Instead of starting from scratch each time, you're building on every new discovery.
Try ThoughtSpot free and watch what happens when your team stops waiting for answers.
Frequently asked questions about big data and AI
1. What is the main difference between big data and artificial intelligence?
Big data refers to the vast amounts of information itself, while AI is the technology that analyzes that information to find patterns and make predictions. Think of big data as the raw material and AI as the factory that processes it into useful products.
2. Do you need big data to use AI effectively?
No, many AI use cases work well with smaller, high-quality datasets. You can build effective models for specific tasks like sentiment analysis, simple classification, or predictive maintenance without massive data volumes. However, big data enables more powerful models, especially deep learning systems that require millions of examples to recognize complex patterns.
3. Is big data and AI only for large enterprises?
Cloud-based big data analytics solutions and AI services have democratized access to these capabilities. Startups and mid-market companies can now tap into the same powerful tools that enterprise organizations use via modern platforms that scale with your business.
4. How do you keep big data and AI systems secure?
Security requires multiple layers including data encryption, access controls, audit trails, and regular monitoring. Modern platforms like ThoughtSpot include built-in governance features such as row-level security and role-based permissions to protect sensitive information while still enabling exploration.
