Why Your Data Lake Feels Like a Swamp — And What To Do About It

By Ray Boyd – Accenture Strategy & Consulting, Consumer Goods

Take a close look at this image. Two sharply dressed business professionals. One crouched by a leaking side pipe, filling a cup from a weak, uncontrolled trickle. The other, calmly skimming the surface of a murky pool with a glass, hoping what they scoop up is useful. Neither walks away confident they’ve found what they need.

It’s a humorous — yet painfully accurate — metaphor for how many Consumer Packaged Goods (CPG) companies are engaging with their enterprise data today.

We've invested millions into building data lakes that promise a single source of truth, a centralized reservoir of insights ready to fuel better decisions. And yet, when our business leaders — the ones tasked with making decisions about pricing, promotions, assortment, and supply chain — try to draw value from that lake, what do they find?

• A murky, unstructured mass of raw data
• A trickle of static reports, too late to act on
• And a manual, inefficient process to connect insight to action

We’re not solving problems. We’re hoping for a clean cup of water from a lake we barely understand.

The Problem: Reports Are Not the Answer

Reports — even well-designed dashboards — are not enough. They’re the equivalent of dipping a glass into the side of the lake and hoping it contains the right insight, at the right time, with the right context.

And let’s be honest: it’s slow. Business users often spend more time:

• Finding the right report,
• Interpreting it,
• Asking a data analyst to re-run it,
• Manually extracting recommendations…

…than they do actually taking action.

In a world where speed, automation, and precision are essential to compete — this model is broken.

The Hidden Cost of Human-Driven Reporting

Let’s call out what no one wants to admit: putting a human between the data lake and the business problem creates systemic drag.

Every hour spent waiting for a refreshed report, interpreting a trend line, or emailing a request for a slightly different cut of the data is a lost opportunity to act. And in CPG, where execution at shelf, forecast accuracy, and retailer collaboration are measured in minutes and pennies, these inefficiencies stack up fast.

We’ve essentially built massive, expensive infrastructure that depends on human middleware.

A Better Question: What’s the Interface Between Data and Action?

It’s not enough to store data in one place. We have to ask:

What bridges the gap between the murky data lake and the high-velocity decisions required to run a CPG business?

The answer isn’t more reports. And it isn’t just training more business users on how to “fish” in the lake.

The real hero of this story? We’re starting to believe it’s agentic systems — systems that can sense, interpret, and act on data — or at least tee up a “next best action” for humans to review.

The Future: From Data Lakes to Decision Engines

Instead of pulling reports, imagine this:

• A system that detects an impending out-of-stock at a major retailer.
• It identifies the root cause — a missed demand signal from last week’s digital campaign.
• It proposes the next best action — expedite a shipment or shift stock from a nearby DC.
• And it not only alerts the sales lead, but also pings the planner, flags the error in the forecast, and logs the event for future learning.

That’s not a report. That’s an agentic loop.

And the data lake? It’s still there — but now it’s a power source, not a puzzle.

How Do We Get There?

Here’s what we believe needs to change:

1. Stop treating reports as the interface

Reports are useful, but they should support, not drive, decision-making. The primary interface should be recommendations, alerts, and actions embedded into workflows.

2. Treat insights like products, not one-off requests

Move away from ad hoc dashboards and toward data products that are modular, scalable, and reusable. Think “Promotion Optimizer,” not “Q3 Promo Effectiveness Report.”

3. Operationalize the feedback loop

Every action (or inaction) should be logged, measured, and learned from. Did the next best action get executed? Did it solve the problem? What was the value?

4. Adopt a ‘Golden Record’ approach to insights

Just like we do for product master data, we need to treat alerts and next best actions as structured, stored, and standardized data that can be analyzed across time, stores, and conditions.

5. Bring decision-making closer to the point of execution

Whether through automation, co-pilot models, or integrated alerts in frontline tools — insights need to show up where and when decisions happen.

The Punchline: Don’t Just Build a Lake. Build a Flow.

The image of the business user crouching to catch a trickle from a leaking lake is more than absurd — it’s tragic. Because in many organizations, that’s still our best shot at turning data into action.

But there’s a better way.

We need to move from passive data environments to agentic decision platforms — from storing and reporting to sensing and responding. Not someday. Now.

So the next time someone asks, “What’s our data lake strategy?” maybe we respond:

“The goal isn’t the lake. The goal is flow — from data to insight to action to value.”

Let’s stop fishing in the dark. Let’s start solving problems in real time.

#CPGTransformation #DataStrategy #AgenticSystems #DataToAction #RetailExecution #DecisionIntelligence #ModernCPG #DataProductThinking #NextBestAction #ThinkingBeyondTheStoreVisit

Originally published on LinkedIn on Jul 15, 2025; https://www.linkedin.com/feed/update/urn:li:ugcPost:7350930549227614210/