Almost no company starts out asking about data governance. The question shows up later, and nearly always by the same route: two areas bring different figures for the same indicator to the management meeting, nobody can explain where each number came from, and a session meant for decisions turns into an improvised audit.
This guide explains, in business language, what data governance is, when an organization needs it, which components it has, how it connects to Peruvian regulation and how it is implemented on AWS.
What is data governance?
Data governance is the set of rules, owners and controls that define who can use each piece of data, for what purpose, at what level of quality and for how long it is kept.
It is not a product you install. It is an organizational agreement about how information is handled, supported by technology that makes it enforceable. That is why programs that begin by buying a tool and end without naming owners usually settle into an elegant inventory nobody consults.
The sign that it works is concrete: when two areas look at the same indicator, they see the same number and know who answers for it.
Why it shows up once you have data, not before
Data governance is an answer to a real problem, not a prerequisite. Setting it up before there is data to govern produces documents nobody applies.
The moment arrives when three symptoms appear together:
- Figures that do not reconcile. Sales and finance report different revenue for the same month, and each can defend their number.
- Lost traceability. Nobody knows which system an indicator came from, or what transformations it went through along the way. To understand that part, it helps to read what ETL is.
- Access by trust. Granting access to a database is settled by asking whoever administers it, not by applying a written rule.
That third symptom is usually what accelerates the decision, because it is the one that becomes a finding when an audit arrives.
The five components
A complete data governance program covers five fronts. The ones that only cover the first end up as a tidy catalog with no effect on decisions.
| Component | What it answers | Sign that it is missing |
|---|---|---|
| Catalog and lineage | What data exists and where it comes from | Nobody knows what a field means |
| Quality | How reliable it is and who measures it | Reports get corrected by hand |
| Ownership | Which area answers for each domain | Everyone uses the data, nobody maintains it |
| Classification and access | What is sensitive and who may see it | Access is granted on trust |
| Lifecycle | How long it is kept and when it is deleted | Everything is stored just in case |
Catalog and lineage. The inventory of what data exists, what each field means and whose hands it passed through before reaching the dashboard. It is the foundation for everything else: you cannot govern what is not registered.
Quality. Explicit rules for what counts as acceptable data —completeness, format, valid ranges, freshness— and a periodic measurement that somebody actually looks at. Without this, trust in reports depends on the mood of the day.
Ownership. Each data domain —customers, products, transactions— has an area that answers for it. It is the most organizational component and the one most often postponed, because it demands a conversation about responsibilities that technology cannot settle.
Classification and access control. Distinguishing which information is public, internal, confidential or sensitive, and having that classification actually govern permissions. This is where data governance meets cloud security.
Lifecycle and retention. How long each type of data is kept and when it should be deleted. Storing everything indefinitely feels prudent, but it increases both the exposed surface and the storage cost.
Data governance and Peruvian regulation
For a company operating in Peru, data governance has moved from good practice to something with concrete regulatory backing.
Law No. 29733, the Personal Data Protection Law, has had a new regulation since November 2024, approved by Supreme Decree No. 016-2024-JUS, which took effect on 30 March 2025 and fully replaced the 2013 regulation. The text modernizes the framework for the digital economy: it addresses automated profiling, cookies, location data, and treats biometric data as part of the sensitive data category.
One change matters especially for the governance conversation. In the event of a security incident of certain severity —when it exposes large volumes of data, affects many people, involves sensitive data or causes evident harm to other rights of the data subject— the National Authority for the Protection of Personal Data must be notified within 48 hours of becoming aware of it, with justification required if that window is exceeded. When the incident happens in the digital environment, notification also reaches the National Digital Security Center. Failing to notify within the deadline is classified as a serious infraction.
That deadline is the best proof of why data governance is a board-level topic rather than a technical one: 48 hours is not enough time to find out what personal data the organization held and where it lived. You either know that in advance or you do not.
For financial system companies there is also SBS Resolution No. 504-2021, the Regulation for Information Security and Cybersecurity Management, in force since July 2021, which reaches supervised banks, insurers and pension fund administrators, and brings international standards into the local framework.
How it is implemented on AWS
Technology does not replace organizational decisions, but it makes the difference between a written policy and a policy that enforces itself. On AWS, each component has its counterpart:
| Component | AWS service | What it provides |
|---|---|---|
| Catalog | AWS Glue Data Catalog | Central registry of tables, schemas and partitions |
| Access control | AWS Lake Formation | Table, column and row level permissions |
| Classification | Amazon Macie | Sensitive data discovery in object storage |
| Business context | Amazon SageMaker Catalog | Glossary, business metadata and publishing rules |
The AWS Glue Data Catalog is the central registry: which tables exist, with what structure, and where the underlying files live.
AWS Lake Formation manages permissions over that catalog with fine granularity —table, column and row— and lets you govern through tags: instead of granting permission by permission, you tag resources (for example, department equals finance) and grant permissions on the tag. When a new table arrives carrying that tag, it inherits the existing policy. That is the difference between administering dozens of permissions and administering thousands.
Amazon Macie discovers sensitive data in object storage using machine learning and pattern matching, so classification does not depend on someone remembering to mark it by hand.
Amazon SageMaker Catalog adds the business layer on top: glossaries, metadata with names and descriptions the business area can read, and rules requiring certain metadata before a data asset is published.
One clarification on naming: the governance capabilities AWS offered under the name Amazon DataZone are now integrated into the unified Amazon SageMaker environment, and existing domains can be upgraded to it. The concept is the same; the place where it is administered changed.
Where to start
Programs that make progress start small and with a concrete pain:
- Pick one domain, not the whole company. Customers or sales are usually good first candidates because the pain is already visible.
- Name the owner before buying anything. If no area answers for that domain, the tool has nobody to serve.
- Catalog and classify that domain. What data exists, which parts are sensitive and who should see them.
- Only then automate permissions with the appropriate tags and rules, and measure quality with two or three simple indicators.
If the starting point is further back —data still scattered across systems that do not talk to each other— it is worth sorting out the foundation first: read what a data warehouse is and what a data lake is, and see how they connect with data analytics.
What data governance is not
- It is not a project with an end date. It is a practice you sustain; new data arrives every week.
- It is not only for large companies. A mid-sized organization holding customer personal data carries the same regulatory obligations.
- It is not about restricting access to data. Done well, it does the opposite: when the rules are clear, granting access stops being a negotiation and becomes a routine step.
Frequently asked questions
What is data governance? The rules, owners and controls defining who uses each piece of data, for what, at what quality and for how long it is kept.
When does it start to matter? Once decisions are made with data and you see figures that do not reconcile, lost traceability and access granted on trust.
Where do you start? With one domain where the pain is visible, an owner named, cataloged and classified before automating anything.
Do your numbers already drive decisions, but not always agree?
Let’s talk about your case and we will review the state of your data foundation, which domain is worth governing first and which controls make sense for your industry.
