Governance, Context, Orchestration: How To Turn Raw Data Into Real Dollars

Many companies struggling to use AI to generate automation and intelligence that actually drives revenue face a similar roadblock: bad data.

If businesses can’t trust their data, they’ll miss new monetization opportunities and jeopardize existing operations. AI doesn’t magically make bad data better. What the technology will do is make bad decisions with low-quality or inaccurate data much faster than a human would.

“Every level in the enterprise is hard-pressed to figure out where to go on AI,” said Avinash (Avi) Deshpande, field CTO at Workato. “Success starts with really knowing and trusting the data.”

According to Avi, there are three key pillars to any AI-ready data strategy:

• Governance: Beyond controlling access, businesses need to be able to audit all the subsequent agent-to-system and agent-to-agent interactions. For example, routing them through secure proxies can enhance security and trust without impacting agility.

• Context: With so much data available, AI systems need to know the relevant information for every prompt. Enterprises must make sure data is enriched, routed, and consumed according to their unique operations.

• Orchestration: Ultimately, the AI agents need to be able to reach all the data they need to execute on prompts. It’s how businesses move from AI at the edge of operations, to embedding AI agents into end-to-end workflows.

Ultimately, building an AI-ready data estate is more than a one-off project; it’s a life-long transformation. And the goal is about more than just using the latest chatbot. As technology progresses at a rapid clip, it’s about making sure enterprises are ready to take advantage when new innovations emerge that can help streamline operations and grow market share.  

With over 25 years in data management, CIO News talked to Avi to get his advice on the foundational steps businesses should take to turn raw data into real dollars:

• Consolidate the ecosystem: Limited, incomplete data can be just as bad as poor-quality assets. Businesses have data in warehouses, SaaS applications, APIs, and on-premises systems, and more locations. Aim to consolidate these disparate sources into one scalable and adaptable platform. For example, working with Workato, Gonzaga was able to take student data from three different CRMs to build a master record that they can now leverage for personalized outreach and other use cases.

• Frame data around real-world problems: Data is the new oil. But while refining this resource is important, companies need to focus on what this fuel is powering. When technology leaders discuss data as missed business opportunities instead of a technical challenge, other executives will become vested in fixing the problems. For example, at Workato, one global retailer orchestrated data across several systems to reduce delivery timelines by 15% — saving money and improving customer satisfaction, returns any business leader would be thrilled to see.

• Rethink, don’t forget batch processing: Replacing overnight batch runs with real-time pipelines can accelerate time-to-value. But a hybrid approach balancing both batch and event-driven orchestration is key. Use batch for compliance-heavy reporting, for example, and event-driven for speed-sensitive operations like customer service.

• Break out of the structured: Companies need to expand support for all data types and formats, spanning structured, semi-structured, and unstructured, including: JSON files, IoT streams, PDFs, transcripts, and other data sources.

• Make data bi-directional: Long-standing capabilities like ETL are important. But so are newer features like reverse ETL, which ensures source data in sources like Salesforce are transformed when important changes are made downstream. With “intelligent orchestration,” companies can deliver high-quality, real-time data to all their systems, users, and AI models.

• Do APIs, think MCP: Rest APIs can quickly be converted to MCP servers so organizations can quickly build their own agentic AI systems. For example, in the past, APIs populated dashboards with critical information the sales teams needed to  understand the health of the pipeline and win over prospects. Depending on the insights, for example, they may offer heavier discounts or target certain markets. Now, MCP lets businesses build agentic AI systems that can instantly surface critical information — beyond just raw numbers on a chart — with just a natural language prompt. These in-depth, context-driven insights help field teams have deeper, more successful conversations with potential users.

Check out more about how Workato is helping companies unify and govern their data to build trusted, high-performance AI agents that both streamline operations and unlock new revenue opportunities.