A Guide for Business Operations Leaders
A data platform is a centralized system that collects, stores, processes, and analyzes data from multiple sources. An example of a data platform is Snowflake, a cloud-based solution that allows businesses to store massive datasets, run analytics in real time, and securely share data across teams and partners.
But that's just scratching the surface. Let's dig deeper into what makes platforms like Snowflake, Databricks, or Google BigQuery the backbone of modern business operations—and why understanding them might be the competitive advantage your company needs most right now.
What Is a Data Platform?
A data platform is a unified technology ecosystem designed to manage the entire data lifecycle—from ingestion and storage to analysis and visualization.
Think of it as the "nervous system" of your organization. Every click, sale, sensor reading, and report flows through it. The platform ensures your data doesn't just exist—it works for you.
A data platform centralizes how organizations collect, manage, and use information. It integrates tools for data ingestion, storage, transformation, analytics, and governance. The goal is simple: to make data accessible, reliable, and actionable across departments while enabling better, faster decision-making.
Why Should Business Operations Leaders Care?
Because data chaos costs money.
You've probably seen it—marketing has their dashboards, finance has their spreadsheets, operations pulls reports from another system entirely. Everyone's using "data," but no one agrees on the numbers. That disconnect creates inefficiencies, missed opportunities, and decision paralysis.
Here's the uncomfortable truth: according to industry research, 90% of business intelligence licenses go unused because the tools are too complex for most employees to use effectively. Companies invest millions in data platforms, but the insights remain locked behind technical barriers.
A strong data management platform brings it all together. It creates a single source of truth, streamlines reporting, and transforms raw data into strategic insight. But here's the catch—having a powerful data platform is only half the battle. You also need to make that data accessible to the people who actually make business decisions.
In fact, according to McKinsey, companies that effectively use data platforms are 23 times more likely to acquire customers and 19 times more likely to be profitable than those that don't.
So, let's look at what that looks like in practice.
What Is an Example of a Data Platform?
Here's where theory meets reality. Let's explore three of the most widely used data platforms: Snowflake, Databricks, and Google BigQuery.
Each approaches data differently, but all share a common goal—turning data into decisions.
1. Snowflake: The Scalable Cloud Data Warehouse
Snowflake is a cloud-native data platform that separates storage and computing, allowing organizations to scale seamlessly as data grows. It supports structured and semi-structured data and enables instant data sharing across business units.
Snowflake operates entirely in the cloud (on AWS, Azure, or Google Cloud). Unlike traditional on-prem databases, you don't buy servers or manage infrastructure. You simply pay for what you use.
It's known for its elastic scalability—meaning if you need to analyze petabytes of sales data during peak season, Snowflake automatically scales up computing power. When traffic slows, it scales back down.
Example: Saxo Bank, a global financial institution, uses Snowflake to unify customer and transaction data from 20+ systems. The result? Real-time analytics that help compliance teams detect anomalies faster and marketing teams personalize outreach—all using the same unified data set.
2. Databricks: The Unified Analytics and AI Platform
Databricks is a data platform built on Apache Spark that unifies data engineering, analytics, and AI under one roof. It uses a "lakehouse" architecture, combining the flexibility of data lakes with the reliability of warehouses.
Databricks shines when data is large, complex, or unstructured—like IoT sensor logs, clickstream data, or text. It helps teams collaborate in notebooks, train machine learning models, and build pipelines without switching tools.
Example: Shell uses Databricks to monitor millions of IoT signals from drilling equipment. By feeding this data into predictive maintenance models, Shell reduced downtime by up to 15%.
Takeaway: If Snowflake is the warehouse for clean, structured data, Databricks is the lab—where teams experiment, analyze, and train AI models using raw data.
3. Google BigQuery: The Serverless Powerhouse
Google BigQuery is a serverless, fully managed data platform that allows lightning-fast SQL-based queries on large datasets.
It's the analytical engine behind Google Cloud's data ecosystem. BigQuery lets you run real-time analytics without managing servers, making it ideal for enterprises that need speed and scale.
Example: Spotify uses BigQuery to process over 100 billion events per day, powering its recommendation algorithms and artist insights dashboards.
Key benefits:
- Integrates with Looker Studio for easy data visualization
- Enables real-time analytics for operational dashboards
- Offers built-in machine learning through BigQuery ML
How Does a Data Platform Work?
A data platform works by connecting various data sources, transforming the information into usable formats, storing it securely, and making it accessible for analytics or reporting.
Most modern data platforms follow this layered architecture:
Imagine your business as a living organism. Data ingestion brings in "nutrients" from every organ (sales, operations, HR). Processing digests it into usable form. Analytics converts it into energy—insight that fuels decisions.
Without this continuous cycle, your organization risks running on guesswork.
The Last Mile Problem: Making Data Platforms Accessible
Here's where many organizations stumble. You've invested in Snowflake or BigQuery. You've got petabytes of perfectly organized data. Your data team is stellar.
But your marketing manager still exports to Excel to answer simple questions. Your sales reps ignore the CRM dashboards because they "don't make sense." Your customer success team waits three days for the analytics team to tell them which accounts are at risk.
This is what industry insiders call "the last mile problem"—getting insights from powerful data platforms into the hands of the people who actually need them.
The Discovery Layer: Where Business Meets Data
This is where platforms like Scoop Analytics come in. While Snowflake, Databricks, and BigQuery handle the heavy lifting of data storage and processing, discovery tools bridge the gap between technical infrastructure and business users.
Think of it this way: your data platform is like having all the world's information in a library. But if you need a PhD in library science just to find a book, most people will never use it.
Scoop Analytics connects to your existing data platforms—whether that's Snowflake, BigQuery, or your CRM—and lets business users ask questions in plain English. "Why did revenue drop last month?" becomes a 45-second investigation that would normally take a data analyst hours to complete.
The key innovation? It's not just answering "what happened" (that's what dashboards do). It's investigating "why it happened" through multi-hypothesis testing and explainable machine learning—all explained in business language, not technical jargon.
For example, instead of your sales manager waiting days for an analyst to segment your pipeline, they can simply ask: "Which deals are most likely to close this quarter?" The system runs sophisticated ML algorithms (the same ones data scientists use) but explains the results like a business consultant would: "Deals with 3+ stakeholder meetings and executive engagement have an 89% close rate."
How Do You Choose the Right Data Platform?
Choosing the right data management platform depends on your size, complexity, and goals. Here's a decision framework:
1. Assess Your Data Volume and Variety
- Structured (sales, CRM): Snowflake or BigQuery
- Unstructured (video, IoT, social): Databricks or AWS Lake Formation
2. Evaluate Integration Needs
- Do you already use AWS or Azure? Stick within the same ecosystem for tighter compatibility.
3. Prioritize Security and Compliance
- Financial and healthcare firms benefit from platforms with built-in compliance (e.g., IBM Cloud Pak for Data).
4. Consider Cost vs. Control
- Serverless platforms (like BigQuery) minimize management overhead.
- Hybrid platforms (like Cloudera) offer greater customization for regulated industries.
5. Think About Accessibility
- Will your business users actually be able to use the insights?
- Consider adding a discovery layer that makes your data platform investment accessible to non-technical users.
6. Think About Future Growth
- Choose a platform that supports AI/ML workloads even if you're not using them today.
The Hidden Power of Data Platforms: Automation and AI
Here's a surprising fact: by 2026, 70% of new enterprise data platforms will feature autonomous optimization—systems that tune themselves automatically based on usage patterns.
Platforms like Domo and Databricks are already doing this. They detect slow queries, scale compute power, and even suggest more efficient data models.
But there's another level of automation happening—intelligent systems that not only optimize the platform but also democratize access to its insights. AI-powered discovery tools are making it possible for anyone in your organization to interact with data platforms without knowing SQL, Python, or data modeling.
That means fewer late nights for your analytics team, faster insights for your executives, and actual ROI from your data platform investment.
How Are Companies Using Data Platforms Today?
Let's explore three industries transforming operations with data platforms:
1. Manufacturing
A global auto manufacturer uses AWS and Databricks to predict equipment failures before they happen. The result: a 20% reduction in unplanned downtime and millions saved in production costs.
They've extended this capability by making predictive maintenance insights accessible through natural language queries. Plant managers can simply ask, "Which machines need attention this week?" and get ML-powered recommendations without waiting for data scientists.
2. Retail
Target uses Google BigQuery to process real-time sales and inventory data across thousands of stores. It powers their "buy online, pick up in-store" model, aligning inventory with local demand.
Regional managers use conversational analytics to quickly understand store performance: "Why did the Northeast region underperform last month?" The system investigates multiple hypotheses—weather patterns, competitive openings, promotional timing—and synthesizes findings in seconds.
3. Finance
Capital One moved its analytics from on-premises databases to Snowflake. The migration reduced report generation time from hours to minutes, freeing analysts to focus on strategy, not spreadsheets.
They've complemented this with AI-powered investigation tools that let risk managers explore complex patterns across millions of transactions. Questions like "What factors predict fraud in our merchant portfolio?" now get answered with explainable machine learning instead of manual analysis.
FAQs
What is the best data platform for small businesses?
Cloud-native, pay-as-you-go options like Snowflake or BigQuery are ideal for small teams. They offer enterprise-grade capabilities without upfront hardware costs. However, remember that having the platform is just the first step—you'll also want to consider how your team will actually access and use the data once it's there.
What are some open-source data platforms?
Apache Hadoop, Apache Spark, and PostgreSQL-based warehouses are open-source alternatives for organizations that prefer more control over their infrastructure.
Can a data platform support AI and machine learning?
Absolutely. Platforms like Databricks and Google BigQuery ML allow teams to build predictive models directly within the data environment—no separate AI stack required. Modern discovery tools can also run sophisticated ML algorithms on top of your data platform, making advanced analytics accessible even to non-technical users.
How secure are cloud-based data platforms?
Most leading platforms comply with GDPR, HIPAA, and SOC 2 standards. They offer encryption, role-based access control, and audit logging.
What if my team doesn't have technical skills to use these platforms?
This is the most common challenge. While data platforms provide powerful infrastructure, you'll need to consider the accessibility layer. Tools that translate between business questions and technical queries can make your data platform investment accessible to everyone who needs insights, not just your data team.
How do I maximize ROI from my data platform investment?
Focus on adoption, not just capability. The most powerful data platform in the world delivers zero ROI if only three people use it. Look for ways to democratize access—whether through user-friendly BI tools, natural language interfaces, or training programs that meet users where they are.
Conclusion
Data is no longer a byproduct of business—it is the business.
And the right data platform turns it into your greatest asset. Whether you're using Snowflake for reporting, Databricks for AI, or BigQuery for real-time analytics, the principle is the same: connect everything, automate intelligently, and act faster than your competitors.
But here's the critical insight many companies miss: having a powerful data platform is only valuable if people actually use it. The companies winning today aren't just those with the biggest data infrastructure—they're the ones who've solved the accessibility problem, making sophisticated analytics available to everyone who needs it.
The traditional approach of hiring more data analysts to bridge the gap doesn't scale. Instead, forward-thinking organizations are investing in both infrastructure (data platforms) and accessibility (discovery tools that democratize insights).
