Data Lake or Data Swamp? Lessons from Building a Central Data Infrastructure
- Maria Alice Maia
- Jun 2
- 3 min read
There are few initiatives that can destroy more capital with less to show for it than a poorly planned data lake. The pitch is always seductive: a single, centralized repository for all your organization's data, unlocking unprecedented insights. The reality, all too often, is a beyond-the-budget "data swamp"—a murky, ungoverned, and unusable morass of data that provides no business value.
The technical task of building a data lake is the easy part. The hard part is ensuring it doesn't become a digital graveyard. This is not a theoretical problem. When I was tasked with leading the "Data Networks" project at a major tourism and travel holding —architecting a central data lake to serve eight distinct companies within the holding group—my primary focus wasn't on the technology, but on preventing this exact outcome.
Here is the no-nonsense playbook we used to turn a complex infrastructure project into a strategic asset that empowers decision-making.
The "Doing Data Wrong" Scenario: The Expensive Data Swamp
This story is common in large enterprises. A mandate comes down to become "data-driven." A massive budget is allocated for a new data lake. The IT and engineering teams work for months, connecting to dozens of data sources and successfully dumping everything into one place. The project is declared a technical success.
Six months later, nothing has changed. Business users can't find the data they need. Data scientists complain that the data is untrustworthy and spend 80% of their time just trying to clean it. The data lake, built with the promise of clarity, has become a source of confusion and frustration. It’s a swamp.
A Playbook for Building a Data Lake That Delivers Value
A successful data lake is not a technical problem; it's a socio-technical one. It requires as much focus on governance, people, and process as it does on pipelines and storage. Here are the core lessons from our "Data Networks" project.
1. Governance Before Ingestion. The fastest way to create a swamp is to start pumping in data without a plan. Before we moved a single byte of data at Grupo Águia, we established a clear and robust data governance framework. We convened stakeholders from across the eight companies to answer the critical questions first:
Who "owns" each data domain?
What are the quality and validation standards for each critical data source?
How will data be cataloged, defined, and documented so that a user in one company can understand the data from another?
Starting with governance ensures that every piece of data entering the lake is clean, documented, and trusted from day one.
2. Design for the End-User, Not Just the Engineer. A data lake that only engineers can use is a failure. Our design philosophy was centered on accessibility and empowerment. We knew our end-users were the directors and managers of the holding companies, not just data analysts. This meant building a user-friendly abstraction layer on top of the raw data, including:
Standardized and certified Business Intelligence dashboards.
Clear, accessible data catalogs and glossaries.
An internal education program to train leaders on how to test hypotheses and extract insights from the new infrastructure.
The goal was never just to store data; it was to make it easy to use for better decision-making.
3. Treat the Data Lake as a Product, Not a Project. A "project" has a beginning and an end. A "product" lives, breathes, and evolves with its users' needs. A data lake is a product. We treated it as such, establishing a permanent cross-company governance council (leveraging my role on the advisory board ) that acted as the "product leadership" for the data lake. This council was responsible for:
Prioritizing new data sources to be integrated.
Identifying and greenlighting high-value business use cases.
Maintaining the strategic roadmap for the data lake's evolution.
This product mindset ensures the data lake remains a living, relevant asset that grows with the business, rather than a static project that becomes obsolete the day it launches.
Building a data lake is easy. Creating lasting value from it is hard. It requires moving beyond a purely technical mindset to one of strategic governance and user-centric design.
Building a data lake is easy. Getting value from it is hard. Subscribe for practical lessons on data infrastructure and governance. If your organization is planning a major data project, let's talk. Schedule a 20-minute strategy call.
