Let’s cut to the chase: The data landscape in 2025 isn’t for the faint hearted. If you're not battle-ready in the era of AI, you're going to get crushed. Most companies are still playing checkers while the competition is moving to 4D chess. We've seen promising data strategies collapse due to minor oversights that exploded into major roadblocks — usually the ones that seemed “manageable” until they weren’t.

The hurdles in building a reliable data infrastructure aren’t shrinking. They’re becoming more intricate, more expensive, and carry greater consequences if ignored.

We’ve identified 5 key risks enterprises face when working with big data — and the best part? They’re all fixable if you know where to look.

1. Lack of Control Over Data Processing

Why It Matters

Organizations often struggle to balance control and flexibility when processing data. Centralizing data in a datalake or warehouse can lead to bottlenecks, vendor lock-in, and increased cloud costs. Moving data into the lakehouse is usually free, but getting it back out if you decide to switch can be a real struggle.

Common Issues

• Inability to access or control data without centralizing it.

• Delays are caused by data silos and lack of transparency.

• Data security risks associated with fragmented sources and tools.

How to Mitigate It

• Leverage decentralized data processing frameworks to maintain control.

• Enable secure, role-based data access to ensure compliance.

• Datatailr provides seamless data connectivity without requiring migration and centralization, so your data stays where it is, and you have full control over it.

2. Delays in Moving from R&D to Production

Why It Matters

The time taken to move from research and development (R&D) to production can significantly impact business goals. Delayed approvals for compute resources and prolonged deployment cycles hinder innovation. Countless hours are spent in the coordination between IT/DevOps and data teams to deploy code, slowing down progress.

Common Issues

• Prolonged approval cycles for compute instances, permissions to push code to production, etc.

• Lack of easy deployment processes from the development environment to pre-production/UAT to production environment.

• Difficulty scaling proofs of concepts to production environments.

How to Mitigate It

• Simplify how compute resources are assigned and scaled/turned off.

• Allow quants/analysts/data scientists to accelerate code to production themselves, with IT/DevOps investing only a fraction of their time to support and have an effective rollback process for unwanted changes.

• Empower data teams to work independently, eliminating the complexity of building pipelines and dashboards in the cloud.

3. Lack of Cost Transparency and High TCO

Why It Matters

Managing the total cost of ownership (TCO) of data pipelines while maintaining performance is a challenge for many organizations. Usage-based costs can spiral out of control without proper transparency and governance. One mistake or forgetting to turn off a cluster can result in thousands of dollars in cloud charges.

Common Issues

• Unpredictable cloud costs leading to budget overruns.

• Industry solutions are usage-based and often lack visibility into resource utilization and costs.

• Complex pricing models make it difficult to predict costs, even if you know your usage.

How to Mitigate It

• Choose a solution with an efficient autoscaler and real-time monitoring — pay only for what you use.

• Implement different tools to provide full transparency and predictability of total cost of ownership (TCO).

• Look for a user-based pricing model with your data platform instead of a usage-based model for predictable and transparent costs.

4. Challenges with Reproducibility and Auditability

Why It Matters

In industries like finance, ensuring reproducibility and auditability is essential for regulatory compliance. Organizations could face an audit anytime and have to prove how data-driven decisions were made. If a compliance officer asks how a result was achieved after 6 months, you need to be prepared to show them your code, logs and potentially reproduce those results.

Common Issues

• Inability to reproduce results consistently due to lack of version control.

• Lack of documentation on data transformations and processes.

• Difficulty proving compliance during audits.

How to Mitigate It

• Implement automated version control for data workflows and models.

• Use platforms that automatically track and document changes.

• Leverage Datatailr ’s automated version control to ensure complete reproducibility and compliance.

5. Inability to Scale Elastically

Why It Matters

In the era of big data, organizations need the ability to scale their cloud instances up or down automatically to optimize costs. Without elastic scaling, organizations risk over-provisioning resources or incurring unnecessary costs while creating their artificial intelligence (AI) applications or feeding data to their machine learning (ML) models.

Common Issues

• Over-provisioning of resources leading to higher cloud costs.

• Inability to scale infrastructure to meet workloads — for example, you may need 2,000 virtual machines to run 100K jobs in parallel and shut them down quickly to save cost. The ability to do this dynamically and cost effectively is the real challenge.

• Dependence on IT to allocate compute capacity in real time.

How to Mitigate It

• Adopt elastic scaling frameworks that adjust to workload demands easily and automatically.

• Implement strict policies to control compute resources on a user or workflow level.

• Use Datatailr to optimize cloud costs with elastic scaling, strict policies and full observability on performance.

The Path Forward

As organizations embrace data-driven decision-making in 2025, avoiding these key risks will be essential for maintaining a competitive advantage. By prioritizing data security, governance, and cloud cost transparency, businesses can unlock the true potential of their data assets efficiently in the era of artificial intelligence and big data.

Ready to take control of your data strategy? Discover how Datatailr can empower your team to build secure, scalable, and future-proof data solutions with ease.