Enabling AI with real-time data integration

Deep within nearly every enterprise lies a massive trove of organizational data. An accumulation of transactions, customer information, operational data, and all sorts of other information, it holds a tremendous amount of value. That data, when paired with artificial intelligence (AI) models, can give businesses new insights into the way they make decisions and where to find opportunities for growth.

But that data is also spread out across platforms ranging from mainframes to cloud to distributed environments. While not uncommon in modern enterprises, this reality requires IT leaders to ask themselves just how accessible all that data is. Particularly, are they achieving real-time data integration? Impactful AI hinges on the answer to this question.

For AI to deliver accurate insights and enable data-driven decision-making, it must be fed high-quality, up-to-date information. This is where real-time data integration becomes critical.

Combating this challenge starts with synchronization. By ensuring data is synchronized across platforms and systems as it changes, organizations can create a consistent, accurate foundation that AI can trust.

Understanding the challenge of data integration

So, what makes data integration so difficult? It’s an enterprise’s own data, so it should be readily accessible, right? The truth is not that simple. In many organizations, data exists in a number of locations including mainframe, cloud, and distributed environments. Often, data experts don’t have an understanding of what data lives in which system, and how it’s all related.

The age-old problem of siloed data means the understanding of data is also siloed. If organizations are going to get the full value from their data, they first need to put it all into a common context. Automated metadata scanning and stitching can provide that context as a first step in any integration effort. This context is essential to discovering data that might be useful to AI initiatives, and also to making sure it is aligned properly with other data to create a comprehensive business understanding.

As AI continues to permeate every aspect of IT operations, the key to successful implementation is still rooted in solving foundational data challenges. Achieving real-time data integration requires an emphasis on modernizing data infrastructure. And that modernization is built on the right solutions and tools to support IT teams.

Unlocking AI with real-time data

When AI models and tools have access to real-time data, the impact on business performance is substantial. Operational decisions become more precise, customer interactions more relevant, and forecasting models more accurate. Organizations can reduce costs by avoiding overproduction or resource misallocation, and they can increase agility by responding faster to market changes.

To enable AI in a meaningful way, organizations need real-time, bi-directional data synchronization. This means data updates in one system are instantly reflected in all connected environments—be it analytics dashboards, AI models, or customer-facing applications.

So, where should enterprise leaders look to accomplish this? One place to start is with tools that provide no-code, bi-directional data movement that works seamlessly between mainframe, distributed, and cloud platforms, meaning changes made in one system are instantly reflected across the organization. That’s what solutions like Rocket DataEdge, brings to IT teams.

This robust suite brings capabilities that span data replication, synchronization, data intelligence, and visualization, to name just a few. All of these solutions work to ensure AI models consistently operate with full visibility into an organization’s data landscape.

Intelligent integration tools can also discover and map enterprise data automatically, creating a contextual understanding of information across the hybrid cloud. This enhances the relevance of the data being used by AI, making it easier to pinpoint what matters most to decision-makers and models alike.

The path to impactful AI runs through real-time data integration

The quality of any AI model is directly linked to the data that fuels it. Incomplete datasets and siloed information leave those models with only a partial picture of what’s actually happening within an organization. That means the output is likely to be just as faulty.

Real-time data integration ensures business leaders are making decisions based on the most up-to-date information, AI models are generating more impactful insights, and IT teams have greater visibility and support for managing complex IT environments.  

As businesses continue to invest in AI, the foundation they build on matters more than ever. Enabling real-time data integration ensures organizations are better equipped to support the proliferation of AI. 

Learn more about how Rocket Software is fueling AI with real-time data integration.