Data Governance Consulting

Q: What is Data Governance?

Data Governance is the end-to-end management of an organisation’s data in order to maximise its value. It combines people, processes and technology to ensure quality, security, traceability and responsible use of data, so decisions rest on reliable information.

Q: How are data quality and traceability assured?

They are assured through clear policies, standardised processes and organisation and monitoring tools such as catalogues, glossaries and dashboards that make it possible to control data quality and traceability.

Q: Is Data Governance the same as AI Governance?

No. Data Governance manages the quality, security and traceability of data, while AI Governance regulates the responsible use of AI models, and relies on solid Data Governance as its base.

Q: Does my company need Data Governance?

It is strongly recommended. As your company’s data grows in volume and complexity, a Data Governance strategy helps make the information you work with reliable when decisions are made, while also supporting regulatory compliance and data security.

We turn data volume into strategic assets through an operational Data Governance framework. We define policies and implement the technical architecture that guarantees the quality, traceability and security needed for high-impact decision-making and measurable ROI.

Our Data Governance service is one of the most important stages of business-oriented data consulting, because it delivers reliable, accessible and secure information aligned with your company’s goals.

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What happens when a company does not govern its data?

When a company does not govern its data, decisions are based on inconsistent information. That can lead to chaotic data processes and to mistaken decisions with no sound basis, harming both reputation and ROI.

Without a data governance process, the first steps of professional advanced analytics can look more agile, but data quality and format can vary. The result is contradictory, inaccurate insights and low scalability.

Without clear policies in data processes, there is also a greater risk of failing to meet mandatory privacy and data protection regulations.

Data Governance versus Data Management

Data Governance

Oversight

Focus: Strategic and regulatory

Purpose: Define policies, standards and roles for correct, secure use of data. Set the regulatory framework for data (how it is used, protected and managed)

Typical tasks: Defining policies, quality standards, regulatory compliance and alignment with business strategy

End goal: Ensure data is reliable, consistent, secure and compliant with regulations

Data Management

Execution

Focus: Operational and technical

Purpose: Run the day-to-day operations of the data lifecycle and apply data governance guidelines

Typical tasks: Capture, storage, integration, transformation and distribution of data

End goal: Make sure data is available, clean and up to date for use

Obtain the benefits of Data Governance

A Data Governance framework is essential to guarantee that your data is of sufficient quality to achieve your organisational goals. The key benefits include:

Efficiency and Costs

Improved efficiency: making the company more efficient by ensuring it has access to high value-added data and a system based on capturing, handling and monitoring it.
Better operational coordination: interdepartmental work is improved through working on the same structure, using the same procedures and rules of the game.
Simplified decision-making: information based on top quality data, which are constantly processed and curated to generate value, facilitates more accurate decision-making and reduces the time spent analysing decisions.

Trust and Culture

Increasing the value of data: Data Governance improves the quality of data and the information produced is, therefore, better.
Organisation-wide information: information and the data it is based on refers to the whole company and can be used by the entire organisation. It is no longer departmentalised, but global.
Greater transparency: Data Governance involves the entire company and also reveals everything about it in the form of data.

Security and Compliance

Better security: data and its use are fully controlled, with a record of when data is entered, consulted and used, and for what purpose.
Minimisation of risk: ensuring that data is of the highest quality and the business is better monitored reduces risk, with alerts that are tailored to the needs of the organisation and which are triggered earlier.

Why LISDATA in Data Governance

At LISDATA we offer an end-to-end approach to data governance consulting and development:

Strategic support and execution: we do not only define the data governance strategy. We also implement the processes, tools and controls needed so data becomes a reliable, useful asset for your company.
Focus on people and data culture: we work to encourage a data culture, train users and make sure the whole organisation adopts responsible, efficient data practices, with personalised support.
Experience in data and AI: our experience with data goes beyond visualisation. We connect business intelligence with AI models, including machine learning, NLP or computer vision.
Closeness and knowledge of the Spanish market: we have a deep knowledge of the Spanish business ecosystem, which lets us adapt our solutions to the specific needs and challenges of each organisation.
Experience across sectors: such as tourism, hospitality, industry, transport, education, health, insurance and the public sector, among others.
Data analytics outsourcing: for companies that prefer to focus on their core and delegate analytics, we offer data analytics outsourcing with continuous expert support.
Up to date with regulation: our track record is combined with strong experience in regulatory compliance, including GDPR and the AI Act, so the solutions stay aligned with current rules. LISDATA holds ISO 27001, ISO 9001 and the Spanish National Security Scheme (ENS) certification.

What makes us different as a Data Governance consulting and development firm?

Our Data Governance consulting and development method: from strategy to real operations.

LISDATA is a consultancy specialised in agile, technical data governance, focused on artificial intelligence solutions that deliver value and real results. Our way of working is a set of ordered phases, end to end, from an initial diagnosis and roadmap through to final implementation and continuous improvement.

Maturity diagnosis (AS-IS)

We run a technical audit of your data sources, looking at connection systems, extraction protocols and regulatory or technical bottlenecks.

We then assess the types of support and the internal capabilities needed to use the information, and how each department will use it.

Governance model design (TO-BE)

We define the processes, tools and architectures for managing data, and what information each department needs to collect.

All of this is gathered in a priorities plan, which guides the development of applications, dashboards, reports and reporting, among other things. We also put in place a method for keeping a history of the data, and we make recommendations to adjust and optimise processes.

Roadmap and prioritisation

We build a development priorities plan based on use cases, taking usability, integration and data security into account.

We also define potential applications, development timelines and a cost estimate, plus how the model scales to the rest of the company, so the data model can grow with the organisation.

Organisational and technical implementation

We set up the organisational structure and the tools needed to govern the data. We define roles and owners such as Chief Data Officer, Data Owners and Data Stewards, and we implement the policies, processes and technology that data governance requires.

Operation, adoption and continuous improvement

Once the Data Governance strategy is in place, we run it and, at the same time, support internal adoption through training.

For continuous improvement, we review processes and adjust them when technology or regulation changes, so the data management service stays active and able to adapt.

Data Governance committee

As a standalone service, the Data Governance committee can be engaged without taking on a full Data Governance project. LISDATA helps define roles and responsibilities, set a meeting cadence and organise the procedures for escalating and resolving decisions about data management.

Our Data Governance services are modular and can be adapted to each organisation’s maturity. It is possible to start with one phase, such as an architecture diagnosis or the Data Governance committee, and widen the scope over time according to the company’s priorities, needs and capacity.

The LISDATA Governance framework

LIS has its own data governance framework and structure, inspired by DAMA and the UNE 0077/78 standard. Our Data Governance team is also certified in Data Management Fundamentals by DAMA.

The framework includes defined roles such as:

Chief Data Officer: sets the vision and the rules for data.
Data Owner: decides how data is used in their area.
Data Steward: looks after the quality and the meaning of the data day to day.

Our operating model can be:

Centralised: the Data Governance department takes on the workload in full.
Hybrid or hub and spoke: Data Governance sits with one person who keeps data governance to the company’s standards, combined with decentralised working groups.
Decentralised: everything runs through committees, and there is no single Data Governance owner.

Data governance policies and processes cover every stage of the data lifecycle, from capture and analysis through to storage and even destruction.

Data Governance, regulatory compliance and AI governance

Data Governance is about managing information. It focuses on storing data, structuring it, transforming it and using it so that quality, traceability, privacy and security are assured.

A very important part of data governance is compliance with the laws and regulations in force, such as the GDPR and the Spanish LOPDGDD.

AI governance sits inside the transformation and use of data. AI governance aims for a responsible use of artificial intelligence models, supervising the whole lifecycle of the algorithms and making sure they are developed, deployed and managed in an ethical, responsible way, in line with rules such as the AI Act.

As an artificial intelligence consultancy, at LISDATA we join data governance with a responsible use of AI, aligned with each company’s values.

What does the Regulatory Compliance service cover?

Within the Data Governance service, at LIS we treat regulatory compliance in a practical way, adapting it to each organisation’s information and processes.

The work can include:

Policies and procedures to regulate how data is processed and used.
Classification of information by criticality and sensitivity.
Risk management linked to the use and processing of data.
Legal bases and traceability, documenting why and under what conditions each piece of data may be used.
Incident and breach management, defining response procedures.
Records and audits so compliance can be evidenced in internal reviews or external requests.

Catalogues, glossaries and lineage

These pillars make enterprise data management understandable, findable and trustworthy for the whole organisation.
Data governance dashboards

Defining rules is not enough. Compliance and quality have to be monitored so issues and opportunities can be spotted.
Integration with cloud and analytics platforms

Data governance does not sit on its own. It connects with the cloud environments and the analytics, BI or AI platforms the company already uses, so duplicates and silos are avoided.
Market tools

We adapt the project to each client’s technology context, working with leading market tools such as Anjana and Microsoft Purview, or building tailored solutions.

Tangible results for our clients

Many of the companies that committed to a Data Governance strategy turned their data into business value. In this use case, traceability was improved in the consumer goods sector.

The challenge

Fragmented information and critical inconsistencies in data lineage. The company was working with duplicates and incomplete records, which undermined operational reliability and regulatory compliance.

Our solution

We implemented an end-to-end Data Governance ecosystem, defining a new framework of policies, roles (owners and stewards) and standardisation processes.

Technical delivery

Critical sources (Navision ERP and SQL Server) were integrated through a unified data architecture, with advanced visualisation in Power BI.

Results and KPIs

100% of master data homogenised.
Information silos removed, and errors in strategic decision-making removed with them.
Data quality metrics (DQ) set up for proactive monitoring of the data.

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What is Data Governance?

Data Governance is the end-to-end management of an organisation’s data in order to maximise its value. It combines people, processes and technology to ensure quality, security, traceability and responsible use of data, so decisions rest on reliable information.

How are data quality and traceability assured?

They are assured through clear policies, standardised processes and organisation and monitoring tools such as catalogues, glossaries and dashboards that make it possible to control data quality and traceability.

Is Data Governance the same as AI Governance?

No. Data Governance manages the quality, security and traceability of data, while AI Governance regulates the responsible use of AI models, and relies on solid Data Governance as its base.

Does my company need Data Governance?

It is strongly recommended. As your company’s data grows in volume and complexity, a Data Governance strategy helps make the information you work with reliable when decisions are made, while also supporting regulatory compliance and data security.

Does LIS's Data Governance service include regulatory compliance?

Yes. Regulatory compliance is part of the Data Governance service and is covered through deliverables such as internal rules, data classification, risk management, incident procedures, documentation of the legal bases for use, and records of activities and audits.

Which standards does LIS follow to support regulatory compliance in its projects?

LIS combines recognised standards with its own Data Governance framework. The team holds DAMA certification, and the method follows UNE 0077:2023 and UNE 0078:2023. The service also covers GDPR, LOPDGDD and the AI Act.

Which kinds of company is this service especially important for?

Data Governance is especially relevant for public-sector organisations and for companies that handle personal or sensitive data. It also matters in sectors with specific traceability requirements, such as healthcare, food and pharma.

Can Data Governance help me meet regulations that are specific to my sector?

Yes. Data Governance can be adapted to the regulatory requirements of each sector. For example, LIS helped a company recover traceability between laboratory and production systems through a maturity diagnosis, a prioritised roadmap and the definition of internal roles.