Your Data & Analytics Backlog Just Became Achievable

By Anthony Sammartino, SVP of U.S. Practices, INSPYR Solutions

Most enterprises have two data and analytics roadmaps: the one they fund and the one they wish they could.

The second is often filled with work everyone agrees matters. Modernize aging pipelines. Improve data quality. Document lineage. Consolidate platforms. Strengthen governance. Give business teams access to more useful analytics.

These initiatives rarely stall because leaders cannot see their value. They stall because the effort required to deliver them competes with immediate business priorities, limited budgets, and scarce expertise.

Over time, the backlog becomes something the organization learns to live with.

I believe AI agents give us a reason to challenge that acceptance. They can change the economics of delivery enough to bring previously deferred work back into consideration.

That has implications well beyond how quickly a data engineer can write code.

Consider what actually makes a data initiative expensive. Before a team develops a pipeline, it must understand the source systems, profile the data, interpret business rules, and make architecture and modeling decisions. After development, it still needs testing, documentation, deployment, and ongoing governance.

Faster SQL or Python addresses only part of that effort.

Agentic delivery can support work across the lifecycle, from requirements analysis and source discovery through data modeling, pipeline development, validation, and documentation. With the right controls and expert oversight, agents can help reduce the repetitive work that consumes so much of a team’s capacity.

When that effort falls across multiple stages, the business case can change. An initiative that once demanded more time and resources than its expected return justified may become a practical investment.

For a practice leader, that is the more meaningful opportunity: helping clients accomplish more of the work their business needs.

It also calls for a fresh look at decisions made under the previous delivery model.

The legacy ETL modernization that was too labor-intensive. The cloud migration that kept slipping. The data-quality program that never secured enough resources. The platform consolidation, semantic-model rationalization, or analytics upgrade that could not compete for funding.

Those decisions may have been reasonable at the time. They deserve to be reassessed as the effort required to execute them changes.

I would encourage CIOs and data leaders to ask a different planning question: What would we do with our data if the cost and effort of delivery were substantially lower?

Start with an initiative that has a clear business outcome and a manageable scope. Re-estimate the work using an agent-accelerated approach, including the human review, integration, and controls it still requires. Then measure whether the approach improves delivery time, quality, and total cost.

The goal is to establish a credible basis for deciding what else can move off the backlog.

That credibility depends on trust.

A faster pipeline has limited value if its outputs are unreliable. A migration is incomplete if access controls are wrong. An analytics model cannot support sound decisions if no one can explain its definitions or trace its sources.

Architecture standards, security, governance, data quality, and testing remain essential. Agents can help make these disciplines part of ongoing delivery by generating documentation, supporting lineage analysis, and running repeatable checks as work progresses.

But accountability stays with people.

Experienced data professionals must own architecture decisions, business interpretations, exceptions, and approvals. They determine whether an output is technically correct, appropriate for its intended use, and ready for the business to trust.

At INSPYR Solutions, this is the opportunity we see for INSPYR Velocity in Data & Analytics: combining experienced professionals with AI-powered delivery to help make modernization and analytics initiatives more achievable while maintaining the oversight they require.

Most organizations already have data with untapped business value. Turning it into useful information requires investment in how that data is organized, engineered, governed, and made available.

As the economics of that work change, leaders have an opportunity to reconsider the boundaries of their roadmap.

The next conversation about your data backlog should include the projects you previously said no to.

Some of them may finally have a path forward.

Ready to take the next step?

INSPYR Velocity can help. Reach out to our team today to get started.