How to run a data platform evaluation and avoid costly mistakes

If you’re in charge of data or business operations, you’ve probably been stuck staring at dashboards that don’t tell the full story, dealing with systems that barely keep up, and trying to figure out whether to patch, replace, or completely rebuild your data platform. You’re not alone.

Evaluating a data platform shouldn’t feel like navigating a maze blindfolded. But all too often, teams rush in, only to realize later that they’ve chosen tools that don’t meet their basic requirements. 

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Read this blog to learn how to navigate data platform evaluation without the stress, so you can clear the noise, make smarter decisions, and get your data strategy back on track.

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Why is it important to evaluate a data platform?

It’s easy to assume any data platform will get the job done, but that’s where things go sideways. 

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Picking the wrong platform can mean wasted time, spiraling costs, and tools that don’t fit how your teams actually work. A platform that doesn’t support your data types, scale with your growth, or integrate with other systems will slow down development, frustrate users, and stunt innovation.

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Evaluating your data platform upfront helps you avoid these pitfalls. A well-chosen platform keeps your development teams efficient, reduces firefighting, and makes data a real asset, not another hurdle to cross. When you choose the right cloud environment, support diverse data, separate storage from compute, simplify sharing, and extend applications, your teams can move faster, experiment more, and deliver value without constantly wrestling with infrastructure. 

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Important features to look for in a data platform

Choosing the right data platform can make or break how your teams build, innovate, and collaborate. Here’s what you need to look for in a data platform to empower your team and drive real results:

1. Selecting the right cloud environment

It’s tempting to just go with whatever cloud your IT team knows best. But picking the right cloud environment means giving your teams the freedom to build where it makes the most sense for customers, for costs, and for future growth. A platform that works across clouds lets developers avoid being locked into one vendor or scrambling to migrate later. That way, they can focus on solving problems, not troubleshooting infrastructure.

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2. Supporting multiple data types

Data today isn’t just neat rows and columns. It includes messy information such as sensor readings, customer chats, app logs, and social media feeds, all flowing in from different sources. A platform that can handle structured, semi-structured, and unstructured data helps developers avoid wasting time transforming or stitching data together. Instead, they can build smarter applications, personalized recommendations, and real-time alerts without hitting compatibility roadblocks.

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3. Separating storage and compute resources

When storage and compute are tied together, you’re stuck paying for more than you need. Separating them gives your teams control over how they scale—whether they’re storing huge datasets or running heavy analytics. This flexibility means faster experimentation, more efficient cost management, and fewer worries about overloading systems when usage spikes. Developers can innovate at speed without constantly juggling performance and expense.

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4. Simplifying data sharing

Sharing data shouldn’t feel like a scavenger hunt. Exporting files, dealing with outdated spreadsheets, and sharing data should be simple for all stakeholders involved. A platform that makes data sharing seamless helps developers collaborate effortlessly across teams or with external partners. Everyone gets the data they need, when they need it, without wasting hours on approvals or cleanup. That’s how cross-team projects actually get done.

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5. Extending data applications

Your platform shouldn’t be a closed box that limits what your teams can build. The best ones let you plug in new tools, libraries, or datasets as your needs evolve. Developers can experiment with new models, connect with third-party apps, or build custom workflows without waiting on IT or worrying about breaking something. A flexible, extendable platform turns good ideas into reality faster and keeps innovation flowing as business needs change.

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How to choose the right data platform for your business

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Choosing the right data platform isn’t about picking the flashiest tool, it’s about finding one that actually solves the problems your teams face every day.

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A thoughtful selection process means your AI/ML projects won’t get stuck, and your teams will spend less time fighting with tools and more time solving real problems. Here’s how you can approach it.

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1. Start with your goals and data needs

First, get clear on what you want to achieve with AI and ML. Define the problems you're trying to solve and the opportunities you want to explore. Aligning your platform with these objectives ensures it supports the workflows and metrics that matter most. At the same time, dig into your data starting with what types you have, how fast it’s generated, and how often it needs to be processed. Understanding this helps you pick a platform that’s not overbuilt or underpowered but just right for your current and future workloads.

2. Ensure scalability, integration, and future-readiness

Your platform should grow as your business grows, without slowing down. Whether it’s handling more users, more queries, or larger datasets, scalability keeps projects from hitting performance roadblocks. Similarly, seamless integration with other tools is essential. If your data doesn’t flow smoothly between systems, you’ll lose time and accuracy. Look for platforms that can adapt to new technologies, support open standards, and offer built-in tools for data governance.

3. Prioritize data quality, speed, and security

AI models are only as good as the data they learn from. Invest in platforms that offer data cleaning, validation, and transformation tools so your teams aren’t stuck troubleshooting flawed datasets. Speed matters too. Whether you need real-time insights or batch updates, make sure your platform can process data fast enough to keep up with your business. And don’t forget security. Encryption, access controls, and compliance with data protection regulations are non-negotiable. A secure platform builds trust and keeps projects running smoothly without legal or reputational headaches.

4. Think about cost, support, and usability

A data platform should empower your teams, not overwhelm them. Look for user-friendly interfaces that make it easy for everyone, from engineers to analysts, to pull insights without a steep learning curve. Consider long-term costs, not just setup fees. Platforms that save time and prevent errors often pay for themselves. And ensure there’s strong support when something goes wrong, you need a partner who can help quickly, not leave you troubleshooting alone.

5. Test, document, and iterate

Before committing, prototype and test the platform with real datasets and workflows. This helps you catch potential issues before they derail projects. Once you’ve chosen a platform, invest in documentation and training so your team knows how to get the most out of it. Encourage feedback and make adjustments often when your data needs begin to evolve.

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Skip the data platform evaluation fatigue with 5X

Evaluating your existing data platform shouldn’t feel like running a never-ending marathon of vendor comparisons, technical jargon, and cost spreadsheets. Yet, that’s exactly how many teams end up losing sleep, energy, and focus. The problem isn’t that solutions don’t exist. It’s that piecing them together from multiple vendors adds unnecessary complexity, delays, and risk.

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That’s where 5X comes in. Instead of drowning in endless choices, 5X gives you a shortcut: a modular, enterprise-grade data foundation that’s ready to go. It’s designed to cover scalability, security, governance, and flexibility. So you don’t have to stitch together disparate tools or worry about lock-in or over-customization. Teams can skip the chaos and get straight to solving real business problems.

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Choosing 5X means choosing a smarter, faster way to get data working for you. See how other data teams simplified their evaluations and launched new projects without the headaches by exploring 5X customer stories. It’s the way to move from evaluation fatigue to operational clarity.

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