All local bodies have data. Even if they are small, even if they don't have a specialized technical team, even if they don't have a data office or advanced analytics tools.
The data is there: in the registry, in files, in taxes, in licenses, in contracts, in social assistance, in facilities, in activities, in incidents, in documents, in accounting records or in citizen service systems. But having data does not necessarily mean being able to use it well.
A piece of data may exist, but be duplicated. It may be stored in a system, but no one knows exactly what it means. It may be available, but not reliable enough. It may be well collected, but not have a clear owner. It may be useful for a service, but difficult to share with another area or administration.
That's why, when we talk about data in the local world, it's not enough to ask if we have data. We have to ask if the data is well governed, well managed and of sufficient quality.
They are three different pieces, but they are part of the same gear.
Three ideas that should be differentiated
When talking about data, concepts are often mixed up. Data governance, data management, data quality, interoperability, analytics, open data, artificial intelligence...
To begin, it is useful to distinguish three basic ideas:
- Governing data means deciding the rules of the game. As described in the previous post «What does it mean to govern data in a city council?«, answers questions such as: who decides, who is responsible, what data can be used for, who can access it and what guarantees must be applied
- Managing data means applying these rules on a daily basis. It answers questions such as: where it is stored, how it is updated, how it is documented, how it is shared, how it is preserved and how it is integrated with other systems.
- Ensuring data quality means ensuring that the data is good enough for the intended use. It answers questions such as: is it correct, complete, up-to-date, consistent, unique, understandable, and useful for decision-making?
None of these three pieces works well on their own.
Governing data: applying criteria and responsibility
Governing data does not mean creating a lot of documentation or complicating municipal work. It means establishing minimum criteria so that important city council data has responsibilities, clear uses and guarantees.
For example, in a small municipality, a formal data governance structure may not be necessary. But it is necessary to know who knows the census data best, who can validate economic data, who is responsible for urban planning data, or who can decide whether information can be published on the portal. transparència.
It is also necessary to know which data is particularly sensitive, which can be shared with other administrations, which is critical for municipal management and which can generate risk if not well protected. Governing data is, therefore, a matter of public responsibility. It is deciding how we want to treat municipal information so that it is useful, secure, reliable and respectful of the rights of citizens.
Managing data: making rules work on a day-to-day basis
Data management is the most operational part. It is the set of practices that allow data to be kept organized, updated, accessible and reusable.
In a small town hall, managing data can mean very specific things:
- Be clear in which system each piece of information is recorded.
- prevent the same data from being maintained differently in several files.
- minimally document what each data set contains.
- establish who can modify certain data.
- periodically review critical data.
- ensure that data can be exported or shared when needed.
- retain the information for the appropriate time.
- avoid relying on a single person or a single provider to understand the data.
Managing data is not just about “running programs.” It is about ensuring that municipal information can be used well throughout its entire life cycle: from when it is collected until it is archived, reused, published, or no longer needed.
Ensuring quality: reliable data for reliable services
Data quality is what makes a piece of data useful for a specific purpose.
A piece of data may be of good quality for one purpose and insufficient for another. For example, an address may be used to roughly identify an area of the municipality, but it may not be good enough to make a formal notification. A list of facilities may be used for an internal report, but it may not be complete enough to publish on a citizen services portal.
Therefore, quality does not mean absolute perfection. It means suitability for use.
In daily municipal life, the lack of data quality can have very practical consequences:
- Notifications not arriving.
- Duplicate people in different systems.
- Files difficult to locate.
- Contradictory reports.
- Errors in registration, taxes or subsidies.
- Difficulties in knowing which services are in greatest demand.
- Waste of time reviewing information manually.
- Distrust in indicators and dashboards.
Improving data quality doesn't always require big tools. It often starts with simple criteria: mandatory fields, consistent formats, basic validations, regular reviews, and clear responsibilities.
A simple example: data on municipal facilities
Let's imagine a small town hall that wants to know better what municipal facilities it has, what use they have and what maintenance needs they have.
If there is no data governance, perhaps no one knows who is responsible for keeping this information up to date. Culture has one list, sports has another, intervention has economic information, maintenance knows the condition of the buildings and the secretariat has administrative documentation.
If there is no data management, each list may have different names, addresses written differently, duplicate equipment, or outdated data.
If there is no quality data, the conclusions drawn may be unreliable: we will not know which equipment is most used, which is most expensive, which needs investment or which could be shared with other services.
However, if the three pieces are worked together, the result changes.
- Governing means deciding who is responsible for the information in the facilities and for what purposes it is to be used.
- Managing means having a common file for the facilities, with minimum fields and shared criteria.
- Ensuring quality means checking that the information is complete, up-to-date and consistent.
With this, the city council can plan better, better justify investments and better inform citizens.
Before talking about artificial intelligence, we need to talk about reliable data
There is more and more talk about artificial intelligence in public administration. And it is positive to explore how it can help improve services, automate tasks, detect needs or support decision-making.
But artificial intelligence needs reliable, well-described, well-protected and well-governed data.
If the initial data is incomplete, erroneous, outdated or biased, analytics or artificial intelligence tools can generate unhelpful or even unfair results.
Therefore, city councils should not start by asking themselves which AI tool they need. They should start by asking themselves what data they have, what is important, what level of quality it has and with what guarantees it can be used.
The best way to prepare for artificial intelligence is to start better governing and managing data today.
Small municipalities can also start
A small city council doesn't need to start with a big data governance plan. It can start with a very practical and gradual approach.
For example:
- choose two or three priority areas, such as the register, records, facilities, social services, incidents or economic data;
- identify which people know this data best;
- detect the main quality problems;
- agree on minimum entry and update criteria;
- document in a simple way what each data set contains;
- check for duplicate or contradictory data;
- assess what data could help build useful indicators;
- identify which data is sensitive and requires more protection.
These first steps do not require large resources. They require a willingness to organize, shared criteria and methodological support.
The key is not to try to govern all the data at once. You need to start with the data that creates the most problems or that can provide the most value.
The role of the Smart Local Government Network
Small municipalities do not have to face this challenge alone. The local world needs shared responses, because many of us have similar needs but very different capacities.
The Smart Local Government Network wants to provide value precisely at this point: helping to convert concepts that may seem abstract—data governance, data management, quality, interoperability or artificial intelligence—into models, tools and practical cases adapted to local reality.
Through its working groups, the Network can help establish common models so that local entities do not have to start from scratch. This includes
- shared criteria for inventorying data,
- describe data sets,
- define responsibilities,
- prioritize critical data,
- establish quality levels
- identify useful use cases for municipalities.
It also contributes to defining common or reusable technological solutions, designed to serve the local world as a whole and avoid duplication, unnecessary dependencies or isolated projects.
Another key area is the description of professional profiles and functions associated with the data. In many small municipalities there will not be a person dedicated exclusively to these tasks, but it will be necessary to understand which functions need to be covered: who validates data, who updates it, who protects it, who documents it, who interprets it and who uses it to make decisions.
Likewise, the Network promotes shared use cases that help to see specific applications: preparing data and building common indicators, facilitating open data sets, helping with decision-making or preparing services based on artificial intelligence with guarantees.
A shared task between AOC, provincial councils, county councils and local entities
The data and AI platform and the Smart Local Government Network should help make data governance, management and quality available to all local authorities.
This requires cooperation between administrations: city councils, provincial councils, county councils, Consorci AOC and other actors that support the local world.
The value of this cooperation is clear: sharing knowledge, generating common criteria, taking advantage of economies of scale, promoting shared services and ensuring that municipalities with fewer resources can also advance.
Data-driven transformation cannot be just for large municipalities. It must be a common capability of the entire local public system.
Three pieces, one goal: better public service
Governing, managing and ensuring data quality are not separate tasks or reserved for specialists.
They are three pieces of the same gear that allow municipal information to be more useful, more reliable, more secure and more oriented towards public service.
- Governing means assuming responsibility and judgment.
- Managing means making data organized and available.
- Guaranteeing quality means ensuring that they are suitable for the use we want to make of them.
When these three pieces work together, city councils can reduce errors, improve procedures, plan better, better protect citizens and make more informed decisions.
To start, you don't need to have everything resolved. You need to take a first step: identify the most important data, understand how it's being used, and start taking better care of it.
This is the path for data to stop being an invisible problem and become a real tool for improving local public services.
