Across industries, businesses are pouring tens of thousands—sometimes hundreds of thousands—into data analytics tools with the hope of unlocking transformational insights. Yet, as Manish Agrawal, a recognized authority in enterprise analytics, warns, these investments often fail to produce meaningful returns. The reasons are deeper than faulty software—they reflect a fundamental mismatch between outdated platforms and the needs of modern, agile organizations.
Where the Investment Goes Wrong
Companies today rely heavily on Business Intelligence (BI), Extract, Transform, Load (ETL) processes, and Quality Assurance (QA) tools to make data-driven decisions. However, despite the sophisticated branding and high price tags, these tools often underperform in the real-world business environment.
“Most BI systems look impressive but are fundamentally passive,” says Manish Agrawal. “They provide static reports that describe what happened, but offer little in the way of predictive insights or strategic guidance.”
The problem extends beyond BI. Traditional ETL tools, designed years ago for simpler, batch-based data ecosystems, have not kept pace with real-time demands. They’re often rigid, resource-intensive, and overly reliant on engineering teams to keep them running.
Even QA solutions—meant to ensure the integrity of data—frequently miss the mark. “They work in silos, require constant manual updates, and react too late in the pipeline,” Agrawal explains. For businesses needing quick, confident decisions, these flaws are unacceptable.
Built for Yesterday, Not Tomorrow
The bigger issue, according to Manish Agrawal, is that most legacy analytics platforms were engineered in an era that predates today’s digital complexity. The modern enterprise operates in an environment shaped by:
Cloud-first infrastructures
Continuous data generation
Cross-functional workflows
The rise of Gen AI for intelligent automation
Tools built before these shifts now struggle to stay relevant. The result? Disconnected insights, long delays in decision-making, spiraling costs, and declining trust in the data itself.
The Executive Dilemma: High Costs, Low Return
C-level leaders often assume that pouring more money into tools will fix the problem. But Agrawal cautions against this reflex. “The average enterprise is spending over $100K on analytics infrastructure annually, yet seeing minimal ROI because they’re solving the wrong problem,” he says. “It’s not about adding more tools—it’s about choosing the right tools.”
What’s needed is a fundamental re-evaluation of the data stack—not based on surface features, but on how effectively tools enable smarter, faster, and more trusted decision-making across the organization.
Rethinking the Analytics Stack: What Really Matters
To break out of this cycle of wasteful spending, Agrawal suggests a clear roadmap for selecting analytics tools that actually deliver business impact:
1. AI-Native Capabilities
Modern platforms should incorporate Gen AI at their core—not as an afterthought. This includes capabilities like automated insights, anomaly detection, and natural-language querying that allow users to uncover patterns and opportunities faster than ever before.
2. User-Centric Design
For analytics to drive decisions, they must be accessible to non-technical users. Tools should empower product managers, marketers, finance teams, and other stakeholders—not just data scientists.
3. Transparent, Scalable Pricing
Enterprises should avoid vendors that tie cost to data volume. Instead, pricing models should align with business value delivered and scale as usage expands.
4. Architectural Flexibility
The platform must handle real-time data, structured and unstructured inputs, and scale seamlessly across cloud environments. It should also integrate easily with other tools in the tech stack.
5. Built-In Governance
Security and compliance features must be baked into the platform from the start, with robust data lineage, role-based access, and audit trails.
From Legacy to Leadership: A Gen AI Approach
Gen AI isn’t just an add-on—it’s the new foundation of analytics. From generating insights autonomously to enabling conversational data interaction, Gen AI is revolutionizing how companies extract value from data.
“Businesses need to move from just analyzing the past to predicting the future and prescribing action,” says Manish Agrawal. “Gen AI gives them that power—if it’s integrated the right way.”
Organizations that embrace this shift will find themselves better positioned to innovate, outmaneuver competitors, and respond to market changes in real time.
About the Expert: Manish Agrawal
With an extensive track record in data analytics and enterprise transformation, Manish Agrawal brings unmatched insight into what makes modern data strategies succeed. His career includes leadership roles at premier consulting firms like BCG and McKinsey, and he has helped countless organizations modernize their approach to data through practical, business-first strategies.
A strong advocate for bridging the technical-business divide, Agrawal is also a Gen AI thought leader, regularly publishing insights on how artificial intelligence is reshaping the data landscape. You can follow his thought leadership via his LinkedIn or Medium profiles.