Data lakes under control: Governance for business success

Mary Hartwell
Aug 20
4 min read

Updated: Aug 21

What exactly is a data lake?

A data lake is a large, central repository for all kinds of data - you can think of it as a huge digital warehouse. It stores structured data (e.g., invoices, customer data) as well as unstructured data (e.g., emails, sensor data, or images).

The advantage: everything is in one place. The danger: without organization, the data lake quickly turns into a so-called data swamp - chaotic, confusing, and difficult to use. Without a well-thought-out concept for data lake governance, this loss of control is a real threat.

With data lake governance, data unfolds its value – teams gain a reliable basis for decision-making through structured analyses.

Three people analyze graphs on a blue digital screen in a modern office. The mood is focused and analytical, with data visualizations visible. — Data only reveals its value with clear governance: structured data analysis provides teams with a reliable basis for decision-making.

Why data lake governance is important

Data lake governance-the application of data governance principles specifically to data lakes-means establishing clear rules and responsibilities for handling data:

Who is allowed to use which data?
How is data checked and described?
How do we ensure that a term such as “customer” means the same thing in all departments?

Governance can be compared to house rules in a shared apartment: without rules, chaos reigns; with rules, everything runs smoothly and fairly.

Typical risks without governance

Risk	Explanation for Beginners	Impact
Outdated Data	Old information continues to be used	Wrong decisions
Inconsistencies	Different definitions (e.g., customer, product)	Confusion, loss of trust
Incompleteness	Important data is missing (e.g., address without postal code)	Analyses become unusable
No Traceability	Origin of the data is not documented	Problems with compliance and audits

From raw data streams to added value

A data lake has a multi-layered structure – similar to a warehouse with different departments:

Bottom: everything is stored unsorted
Top: checked and ready-to-sell products

Layer	Content	Explained Simply
0 – Raw	Raw data	Like unopened boxes placed in a warehouse
1 – Cleansed	Cleansed data	Errors corrected, duplicates removed
2 – Conformed	Standardized formats	Sorted according to standards
3 – Enriched	Enriched data	Enhanced with additional info (e.g., price tag)
4 – Curated	Analysis- and reporting-ready datasets	Immediately usable for reports

Holistic data lake governance ensures that these layers mesh together cleanly and that the data remains reliably usable.

Real-time data - opportunity and risk

Modern systems deliver data in real time, often within milliseconds. But fast does not necessarily mean accurate. For example, an incorrect price tag is transmitted to all stores at the same time - the error spreads faster than ever before.

Data lake governance ensures that data is not only processed quickly, but also correctly and reliably - through verification rules, validation, and monitoring.

Data quality is a management task

Data quality is often left to IT. But without a technical context, this is not enough. Example: If marketing and sales define the term “customer” differently, misunderstandings and wrong decisions arise.

Traditional Approaches	Modern Governance
Focus on IT processes	Focus on business value
Reactive corrections	Proactive quality assurance
High complexity	Automated, simple processes
Technical perspective	Combination of business expertise and IT

Leadership teams that actively embed data lake governance create clarity, trust, and speed in data initiatives.

Data Scientists entlasten

Wenn ein Data Lake unstrukturiert befüllt wird, verbringen Data Scientists bis zu 80 % ihrer Zeit mit Datenbereinigung, anstatt Modelle zu entwickeln oder Innovationen voranzutreiben.

Mit Data Lake Governance bekommen sie von Anfang an saubere, geprüfte Daten. Das bedeutet:

weniger Zeit für Korrekturen
schnellere Analysen
genauere Entscheidungen
bessere Zusammenarbeit zwischen IT, Fachbereichen und Analyse‑Teams

Relieve data scientists

When a data lake is filled with unstructured data, data scientists spend up to 80% of their time cleaning data instead of developing models or driving innovation.

With data lake governance, they get clean, verified data right from the start. This means:

Less time spent on corrections
Faster analyses
More accurate decisions
Better collaboration between IT, business departments, and analytics teams

No artificial intelligence without data lake governance

Artificial intelligence and machine learning are only as good as the data they work with.

Poor data leads to unreliable predictions.
Clean data enables robust, reproducible models.

Data lake governance ensures structured data pipelines, clear responsibilities, and verified data sets-the foundation for reliable AI applications.

About the author

Mary Hartwell is Global Practice Lead for Data Governance at Syniti, a Capgemini company. With over 25 years of experience in data governance and master data management, she helps international companies sustainably secure their data quality and leverage its business value.

Previously, Mary held senior positions at IBM, United Technologies, Johnson Matthey, and Accenture, where her responsibilities included global programs for data quality, governance, and master data management. She specializes in developing scalable data strategies that strengthen compliance and trust while enabling measurable business results.

Mary is considered an expert in linking technology and business requirements. She works closely with leadership teams to transform data into a true strategic asset-as the foundation for informed decisions, successful AI applications, and sustainable business success.