7 Common mistakes to avoid when building a data platform

Factors to consider when building a data platform

Building a data platform isn’t just another IT project. It’s the backbone of how your company will make decisions for years to come. Get it wrong, and you’ll end up with silos, slow insights, and a system your teams avoid. Get it right, and you’ll have a living, breathing foundation that scales with your business, empowers every department, and actually drives outcomes. 

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Here’s what you need to think about before you dive in:

• Understand your data needs: Start with clarity, not tech. Are you tracking customer behavior, monitoring supply chains, or crunching sales data? Each has different needs for storage, refresh rates, and processing power. For instance, e-commerce might demand real-time cart abandonment data, while finance can work with monthly close reports
  
• User-friendly interface: The most powerful platforms make it easy for both data scientists and business teams to ask questions, pull insights, and act without friction
  
• Customization: No two businesses look the same, and your data platform shouldn't either. Customization ensures it adapts to your workflows, dashboards, and even terminology
  
• Cost: Don’t just think about the upfront price tag. Look at long-term value. A platform that saves hours of manual reporting, cuts inefficiencies, and unlocks better decisions will easily pay for itself. Cheap but rigid solutions will cost you more down the line in lost opportunities
  
• Support and maintenance: Building the platform is just step one. What happens when something breaks or regulations change? Vendors with reliable support teams and proactive updates turn a good platform into a great one. Without this, you’re on your own
  
• Future-proofing: Tech evolves faster than you think. Your platform should be flexible enough to plug in AI models, integrate IoT data, or adopt new compliance requirements. Think of it less as a finished product and more as an ecosystem that grows with your business

7 Common data platform building mistakes

Building a data platform is not about picking the right tech, plugging it in, and watching the magic happen. In reality, it's more complicated than most leaders expect. Many platforms fail not because the technology wasn’t good enough, but because the business context wasn’t considered. 

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Let’s unpack the most common challenges that trip companies up (and how to sidestep them).

1. Over-focusing on tech, under-focusing on people‍

It’s tempting to treat your platform like a pure tech project. But platforms fail when they don’t serve the people who need them. If the platform doesn’t meet the needs of business users or give them access to the right data on time or simplifies its usage, it won’t drive decisions. A successful data analytics services platform must be designed with the end-user experience at its core.

2. Departmental data silos‍

Enterprises are naturally complex, with each department running its own systems. When those systems aren’t integrated, you end up with silos.Marketing sees one version of customer data, finance sees another, and operations sees a third. Without a unified view, collaboration breaks down and decisions become inconsistent.

3. Poor-quality data‍

Imagine trying to forecast sales with half the transactions missing or customer records duplicated three times. It leads to bad strategies, poor customer experience, and even regulatory risks. Poor-quality data erodes trust, slows adoption, and can create real financial and reputational risks.If the underlying data is inaccurate, incomplete, or inconsistent, the analytics that sit on top will mislead more than they inform. That’s why poor quality data cannot build a good data platform.‍

4. Weak data governance‍

Data governance is about setting rules: who owns what data, how it’s defined, how it can be used, and who has access. Without governance, you get turf wars, duplicate definitions, and “whose numbers are right?” debates in every meeting. Strong governance ensures clarity, compliance, and trust in the authenticity of your data.

5. Underestimating legacy complexity‍

Most enterprises don’t start from scratch; they already have decades-old systems and messy integrations. The complexity of connecting these legacy systems with a new platform is not easy. Legacy systems are messy, integration is costly, and vendor lock-in is real. Companies often realize too late that migrating their old landscape is so hard that the whole project stalls‍

6. Lack of prioritization‍

Modern data platforms can power everything from BI dashboards to AI models. But trying to build everything at once spreads teams too thin and creates bloated platforms full of unused data. Prioritization is key: focus on a few high-impact use cases, deliver quick wins, then scale gradually.‍

7. Weak business case‍

Leaders don’t fund “data platforms”, they fund business outcomes. If you can’t tie the platform to faster decision-making, reduced costs, better compliance, or improved customer experience, it will be seen as a cost center instead of a strategic asset.

What are the qualities of a good data platform?

A data platform connects the dots, turning scattered numbers into clear, actionable insights. For it to actually deliver value, it needs to be functional and built right.

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Here’s what separates a good data platform from one that leaves teams frustrated and stuck:

• Scalability: Today’s “small” problem will be tomorrow’s mountain of data. A good data platform grows with you and easily handles more users, more queries, and more sources without slowing down. Think about Netflix: as its user base exploded, its platform scaled seamlessly to keep recommendations relevant in real time
  
• Security: Data breaches are costly and destroy trust. The right platform keeps your data locked tight, with encryption, audits, and safeguards that give your teams peace of mind
  
• Integration capabilities: A platform that can’t play well with others will turn into another silo. High-performing data platforms integrate smoothly with your CRM, ERP, marketing tools, and more. For example, a retail chain connecting POS systems with supply chain software avoids stockouts and optimizes promotions
  
• Real-time processing: Waiting for reports to catch up can cost opportunities. A good platform processes data as it flows, so decisions are based on what’s happening now, not what yesterday’s numbers show you
  
• User-friendly interface: Not everyone is a data expert. The platform should be easy to navigate, with helpful tools and guides that let anyone from operations to leadership ask questions and find answers without frustration
  
• Robust data governance: As data grows, so does the need for control. The platform should offer tools that track where data comes from, who can access it, and how it’s used, keeping everything organized and compliant
  

5X helps you overcome data platform building challenges

Poor planning, vendor lock-in, and scalability failures are the stumbling blocks that trip up even the most seasoned teams. But it doesn’t have to be that way.

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Instead of betting everything on a rigid vendor solution, 5X gives you a smarter starting point. With an enterprise-grade foundation that’s modular, open-source, and built to scale, you can avoid the risks and delays of building from scratch. It’s flexible enough to adapt to your workflows, secure enough to protect your data, and robust enough to grow as your business expands.

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5X helps you focus on solving business problems, not wrestling with infrastructure. Whether you’re scaling rapidly, integrating complex systems, or trying to ensure seamless collaboration across teams, our platform helps you succeed without compromise.

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