Three Winning Plays for AI-Ready Data

maio 14, 2025

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(Eugene Onischenko/Shutterstock)

If you’ve ever watched a hockey game, you know that a hat trick—scoring three goals in a single game—is a major feat. It requires precision, teamwork, and a deep understanding of the game. When it comes to artificial intelligence (AI), the same principles apply: Success isn’t just about having the best technology, but about ensuring the right strategies are in place to fuel that technology with high-quality data. AI is only as strong as the data that feeds it, yet many organizations still struggle with making their data AI-ready.

So, how do you achieve your own data hat trick? By focusing on three key plays: fostering an open ecosystem mentality, innovating at the application layer, and staying agile with data strategies. Let’s break down how each of these can elevate your AI game.

1. Adopt an Ecosystem Mentality: Play Nice, Win Big

Imagine a hockey team where every player tries to score without passing the puck. Chaos, right? The same applies to data. Many enterprises operate in walled gardens, where data is locked inside proprietary systems that don’t play well with others. This approach stifles innovation and limits AI’s potential.

An ecosystem mentality prioritizes open integrations, allowing data to flow freely between systems. Companies that embrace this approach understand that no single vendor can provide all the answers. Instead of keeping data siloed within one platform, they leverage an interconnected network of technologies that enable real-time data sharing and analysis.

(Dnipro Assets/Shutterstock)

Think about how modern hockey teams use analytics. They pull data from multiple sources—player performance metrics, video analysis, and real-time game statistics—to make smarter, faster decisions. Businesses need to do the same. By integrating their data sources and allowing AI to tap into a broad ecosystem, they can create a richer, more accurate foundation for AI-driven insights.

2. Innovate at the Application Layer: Make Data Work for AI

Raw data alone doesn’t create value—how it’s processed and applied is what truly matters. This is where the application layer comes into play. In hockey, strategy is everything. You can have the fastest skaters and the best shooters, but if they don’t work within a cohesive game plan, their talent is wasted. Data works the same way; without an intelligent application layer, even the most comprehensive datasets remain underutilized.

The application layer is where data is refined, transformed, and made useful for AI. It should facilitate seamless movement between different platforms, ensuring that AI models get the right data at the right time. Organizations focusing on this layer can turn fragmented, inaccessible data into structured, meaningful insights that AI can act upon.

For example, a retailer wants to use AI to optimize inventory management. Without an effective application layer, their AI system might struggle to make sense of inconsistent data coming from supply chain systems, point-of-sale transactions, and customer demand forecasts. By building an application layer that harmonizes these datasets, the retailer can ensure AI gets a clear, real-time view of inventory levels, reducing waste and improving sales.

3. Stay Agile: Break Free from Outdated Data Pipelines

Hockey players don’t have time to second-guess their moves. The game moves too fast, and agility is key to success. The same is true for data strategies. Traditional extract, transform, load (ETL) or even newer ELT methods were designed for a batch-processing world that no longer aligns with the speed and scale of modern AI-driven business needs.

(Nazarova Mariia/Shutterstock)

Rather than relying on rigid pipelines that slow down decision-making, organizations should embrace a more flexible approach—one that eliminates unnecessary data transformation steps and allows for direct access to detailed, operational data in real-time. This shift removes bottlenecks and empowers business users and AI applications to access insights without waiting on complex engineering workflows.

Think of it like adjusting your game plan mid-match. Instead of following a rigid strategy that no longer fits the evolving dynamics of the game, successful teams stay flexible, react to new information in real-time, and make quick, decisive plays. The same principle applies to AI-ready data: companies that move away from cumbersome data preparation processes and embrace real-time, adaptable data strategies will gain a competitive edge.

Bringing It All Together: Your AI Game Plan

Winning in AI isn’t just about having cutting-edge machine learning models. It’s about setting up the right data strategies that empower those models to perform at their best. By adopting an open ecosystem mentality, innovating at the application layer, and staying agile with data strategies, organizations can ensure their data is AI-ready and primed for success.

Much like a hockey team fine-tunes its strategy to stay ahead of the competition, businesses must continuously refine their data game plan. AI is evolving fast, and those who prioritize a strong data foundation will be the ones lifting the trophy at the end of the season.

So, lace up your skates, refine your data strategy, and get ready to score big in the AI era.

About the author: Joe Cooper is the vice president of Global Alliances at Incorta, where he leads strategic partnerships with global enterprise platforms like Google Cloud and Workday. Prior to Incorta, Cooper held senior roles at IBM and Alteryx, where he was instrumental in building the Canadian business from the ground up — establishing market presence, growing the customer base, and driving double-digit growth across key verticals. With deep expertise in data integration, analytics, and AI-driven business intelligence, Cooper helps Fortune 500 companies modernize their data ecosystems and unlock real-time insights that power faster, smarter decisions. A former competitive hockey player, Cooper brings the same grit, leadership, and team-first mentality from the rink into the boardroom. He now coaches youth hockey and remains passionate about developing talent both on and off the ice.