estrategias

 On 19 November, the European Commission presented the Data Union Strategy, a roadmap that seeks to consolidate a robust, secure and competitive European data ecosystem. This strategy is built around three key pillars: expanding access to quality data for artificial intelligence and innovation, simplifying the existing regulatory framework, and protecting European digital sovereignty. In this post, we will explain each of these pillars in detail, as well as the implementation timeline of the plan planned for the next two years.

Pillar 1: Expanding access to quality data for AI and innovation

The first pillar of the strategy focuses on ensuring that companies, researchers and public administrations have access to high-quality data that allows the development of innovative applications, especially in the field of artificial intelligence. To this end, the Commission proposes a number of interconnected initiatives ranging from the creation of infrastructure to the development of standards and technical enablers. A series of actions are established as part of this pillar: the expansion of common European data spaces, the development of data labs, the promotion of the Cloud and AI Development Act, the expansion of strategic data assets and the development of facilitators to implement these measures.

1.1 Extension of the Common European Data Spaces (ECSs)

Common European Data Spaces are one of the central elements of this strategy:

• Planned investment: 100 million euros for its deployment.

• Priority sectors: health, mobility, energy, (legal) public administration and environment.

• Interoperability: SIMPL is committed  to interoperability between data spaces with the support of the European Data Spaces Support Center (DSSC).

• Key Applications:

  • European Health Data Space (EHDS): Special mention for its role as a bridge between health data systems and the development of AI.

  • New Defence Data Space: for the development of state-of-the-art systems, coordinated by the European Defence Agency.

1.2 Data Labs: the new ecosystem for connecting data and AI development

The strategy proposes to use Data Labs as points of connection between the development of artificial intelligence and European data.

These labs employ data pooling, a process of combining and sharing public and restricted data from multiple sources in a centralized repository or shared environment. All this facilitates access and use of information. Specifically, the services offered by Data Labs are:

• Makes it easy to access data.

• Technical infrastructure and tools.

• Data pooling.

• Data filtering and labeling 

• Regulatory guidance and training.

• Bridging the gap between data spaces and AI ecosystems.

Implementation plan:

• First phase: the first Data Labs will be established within the framework of AI Factories (AI gigafactories), offering data services to connect AI development with European data spaces.

• Sectoral Data Labs: will be established independently in other areas to cover specific needs, for example, in the energy sector.

• Self-sustaining model: It is envisaged that the Data Labs model  can be deployed commercially, making it a self-sustaining ecosystem that connects data and AI.

1.3 Cloud and AI Development Act: boosting the sovereign cloud

To promote cloud technology, the Commission will propose this new regulation in the first quarter of 2026. There is currently an open public consultation in which you can participate here.

1.4 Strategic data assets: public sector, scientific, cultural and linguistic resources

On the one hand, in 2026 it will be proposed to expand the list of high-value data  in English or HVDS to include legal, judicial and administrative data, among others. And on the other hand, the Commission will map existing bases and finance new digital infrastructure.

1.5 Horizontal enablers: synthetic data, data pooling, and standards

The European Commission will develop guidelines and standards on synthetic data and advanced R+D in techniques for its generation will be funded through Horizon Europe.

Another issue that the EU wants to promote is data pooling, as we explained above. Sharing data from early stages of the production cycle can generate collective benefits, but barriers persist due to legal uncertainty and fear of violating competition rules. Its purpose? Make data pooling a reliable and legally secure option to accelerate progress in critical sectors.

Finally, in terms of standardisation, the European standardisation organisations (CEN/CENELEC) will be asked to develop new technical standards in two key areas: data quality and labelling. These standards will make it possible to establish common criteria on how data should be to ensure its reliability and how it should be labelled to facilitate its identification and use in different contexts.

Pillar 2: Regulatory simplification

The second pillar addresses one of the challenges most highlighted by companies and organisations: the complexity of the European regulatory framework on data. The strategy proposes a series of measures aimed at simplifying and consolidating existing legislation.

2.1 Derogations and regulatory consolidation: towards a more coherent framework

The aim is to eliminate regulations whose functions are already covered by more recent legislation, thus avoiding duplication and contradictions. Firstly, the Free Flow of Non-Personal Data Regulation (FFoNPD) will be repealed, as its functions are now covered by the Data Act. However, the prohibition of unjustified data localisation, a fundamental principle for the Digital Single Market, will be explicitly preserved.

Similarly, the Data Governance Act  (European Data Governance Regulation or DGA) will be eliminated as a stand-alone rule, migrating its essential provisions to the Data Act. This move simplifies the regulatory framework and also eases the administrative burden: obligations for data intermediaries will become lighter and more voluntary.

As for the public sector, the strategy proposes an important consolidation. The rules on public data sharing, currently dispersed between the DGA and the Open Data Directive, will be merged into a single chapter within the Data Act. This unification will facilitate both the application and the understanding of the legal framework by public administrations.

2.2 Cookie reform: balancing protection and usability

Another relevant detail is the regulation of cookies, which will undergo a significant modernization, being integrated into the framework of the General Data Protection Regulation (GDPR). The reform seeks a balance: on the one hand, low-risk uses that currently generate legal uncertainty will be legalized; on the other,  consent banners will be simplified  through "one-click" systems. The goal is clear: to reduce the so-called "user fatigue" in the face of the repetitive requests for consent that we all know when browsing the Internet.

2.3 Adjustments to the GDPR to facilitate AI development

The General Data Protection Regulation will also be subject to a targeted reform, specifically designed to release data responsibly for the benefit of the development of artificial intelligence. This surgical intervention addresses three specific aspects:

1. It clarifies when legitimate interest for AI model training may apply.

2. It defines more precisely the distinction between anonymised and pseudonymised data, especially in relation to the risk of re-identification.

3. It harmonises data protection impact assessments, facilitating their consistent application across the Union.

2. 4 Implementation and Support for the Data Act

The recently approved Data Act will be subject to adjustments to improve its application. On the one hand, the scope of business-to-government ( B2G) data sharing is refined, strictly limiting it to emergency situations. On the other hand, the umbrella of protection is extended: the favourable conditions currently enjoyed by small and medium-sized enterprises (SMEs) will also be extended to medium-sized companies or small mid-caps, those with between 250 and 749 employees.

To facilitate the practical implementation of the standard, a model contractual clause for data exchange has already been published , thus providing a template that organizations can use directly. In addition, two additional guides will be published during the first quarter of 2026: one on the concept of "reasonable compensation" in data exchanges, and another aimed at clarifying the key definitions of the Data Act that may generate interpretative doubts.

Aware that SMEs may struggle to navigate this new legal framework, a Legal Helpdesk  will be set up in the fourth quarter of 2025. This helpdesk will provide direct advice on the implementation of the Data Act, giving priority precisely to small and medium-sized enterprises that lack specialised legal departments.

2.5 Evolving governance: towards a more coordinated ecosystem

The governance architecture of the European data ecosystem is also undergoing significant changes. The European Data Innovation Board (EDIB) evolves from a primarily advisory body to a forum for more technical and strategic discussions, bringing together both Member States and industry representatives. To this end, its articles will be modified with two objectives: to allow the inclusion of the competent authorities in the debates on Data Act, and to provide greater flexibility to the European Commission in the composition and operation of the body.

In addition, two additional mechanisms of feedback and anticipation are articulated. The Apply AI Alliance will channel  sectoral feedback, collecting the specific experiences and needs of each industry. For its part, the AI Observatory will act as a trend radar, identifying emerging developments in the field of artificial intelligence and translating them into public policy recommendations. In this way, a virtuous circle is closed where politics is constantly nourished by the reality of the field.

Pillar 3: Protecting European data sovereignty

The third pillar focuses on ensuring that European data is treated fairly and securely, both inside and outside the Union's borders. The intention is that data will only be shared with countries with the same regulatory vision.

3.1 Specific measures to protect European data

• Publication of guides to assess the fair treatment of EU data abroad (Q2 2026):

• Publication of the Unfair Practices Toolbox  (Q2 2026):

  • Unjustified location.

  • Exclusion.

  • Weak safeguards.

  • The data leak.

• Taking measures to protect sensitive non-personal data.

All these measures are planned to be implemented from the last quarter of 2025 and throughout 2026 in a progressive deployment that will allow a gradual and coordinated adoption of the different measures, as established in the Data Union Strategy.

In short, the Data Union Strategy represents a comprehensive effort to consolidate European leadership in the data economy. To this end, data pooling and data spaces in the Member States will  be promoted, Data Labs and AI gigafactories will be committed to and regulatory simplification will be encouraged.

Did you know that less than two out of ten European companies use artificial intelligence (AI) in their operations? This data, corresponding to 2024, reveals the margin for improvement in the adoption of this technology. To reverse this situation and take advantage of the transformative potential of AI, the European Union has designed a comprehensive strategic framework that combines investment in computing infrastructure, access to quality data and specific measures for key sectors such as health, mobility or energy.

In this article we explain the main European strategies in this area, with a special focus on the Apply AI Strategy or the AI Continent Action Plan , adopted this year in October and April respectively. In addition, we will tell you how these initiatives complement other European strategies to create a comprehensive innovation ecosystem.

Context: Action plan and strategic sectors

On the one hand, the AI Continent Action Plan establishes five strategic pillars:

1. Computing infrastructures: scaling computing capacity through AI Factories, AI Gigafactories and the Cloud and AI Act, specifically:
   • AI factories: infrastructures to train and improve artificial intelligence models will be promoted. This strategic axis has a budget of 10,000 million euros and is expected to lead to at least 13 AI factories by 2026.
   • Gigafactorie AI: the infrastructures needed to train and develop complex AI models will also be taken into account, quadrupling the capacity of AI factories. In this case, 20,000 million euros are invested for the development of 5 gigafactories.
   • Cloud and AI Act: Work is being done on a regulatory framework to boost research into highly sustainable infrastructure, encourage investments and triple the capacity of EU data centres over the next five to seven years.
2. Access to quality data: facilitate access to robust and well-organized datasets through the so-called Data Labs in AI Factories.
3. Talent and skills: strengthening AI skills across the population, specifically:
   • Create international collaboration agreements.
   • To offer scholarships in AI for the best students, researchers and professionals in the sector.
   • Promote skills in these technologies through a specific academy.
   • Test a specific degree in generative AI.
   • Support training updating through the European Digital Innovation Hub.
4. Development and adoption of algorithms: promoting the use of artificial intelligence in strategic sectors.
5. Regulatory framework: Facilitate compliance with the AI Regulation in a simple and innovative way and provide free and adaptable tools for companies.

On the other hand, the recently presented, in October 2025, Apply AI Strategy seeks to boost the competitiveness of strategic sectors and strengthen the EU's technological sovereignty, driving AI adoption and innovation across Europe, particularly among small and medium-sized enterprises. How? The strategy promotes an "AI first" policy, which encourages organizations to consider artificial intelligence as a potential solution whenever they make strategic or policy decisions, carefully evaluating both the benefits and risks of the technology. In addition, it encourages a European procurement approach, i.e. organisations, particularly public administrations, prioritise solutions developed in Europe. Moreover, special importance is given to open source AI solutions, because they offer greater transparency and adaptability, less dependence on external providers and are aligned with the European values of openness and shared innovation.

The Apply AI Strategy is structured in three main sections:

Flagship sectoral initiatives

The strategy identifies 11 priority areas where AI can have the greatest impact and where Europe has competitive strengths:

• Healthcare and pharmaceuticals: AI-powered advanced European screening centres will be established to accelerate the introduction of innovative prevention and diagnostic tools, with a particular focus on cardiovascular diseases and cancer.
• Robotics: Adoption will be driven for the adoption of European robotics connecting developers and user industries, driving AI-powered robotics solutions.
• Manufacturing, engineering and construction: the development of cutting-edge AI models adapted to industry will be supported, facilitating the creation of digital twins and optimisation of production processes.
• Defence, security and space: the development of AI-enabled European situational awareness and control capabilities will be accelerated, as well as highly secure computing infrastructure for defence and space AI models.
• Mobility, transport and automotive: the "Autonomous Drive Ambition Cities" initiative will be launched to accelerate the deployment of autonomous vehicles in European cities.
• Electronic communications: a European AI platform for telecommunications will be created that will allow operators, suppliers and user industries to collaborate on the development of open source technological elements.
• Energy: the development of AI models will be supported to improve the forecasting, optimization and balance of the energy system.
• Climate and environment: An open-source AI model of the Earth system and related applications will be deployed to enable better weather forecasting, Earth monitoring, and what-if scenarios.
• Agri-food: the creation of an agri-food AI platform will be promoted to facilitate the adoption of agricultural tools enabled by this technology.
• Cultural and creative sectors, and media: the development of micro-studios specialising in AI-enhanced virtual production and pan-European platforms using multilingual AI technologies will be incentivised.
• Public sector: A dedicated AI toolkit for public administrations will be built with a shared repository of good practices, open source and reusable, and the adoption of scalable generative AI solutions will be accelerated.

Cross-cutting support measures

For the adoption of artificial intelligence to be effective, the strategy addresses challenges common to all sectors, specifically:

• Opportunities for European SMEs: The more than 250 European Digital Innovation Hubs have been transformed into AI Centres of Expertise. These centres act as privileged access points to the European AI innovation ecosystem, connecting companies with AI Factories, data labs and testing facilities.
• AI-ready workforce: Access to practical AI literacy training, tailored to sectors and professional profiles, will be provided through the AI Skills Academy.
• Supporting the development of advanced AI: The Frontier AI Initiative seeks to accelerate progress on cutting-edge AI capabilities in Europe. Through this project, competitions will be created to develop advanced open-source artificial intelligence models, which will be available to public administrations, the scientific community and the European business sector.
• Trust in the European market: Disclosure will be strengthened to ensure compliance with the European Union's AI Regulation, providing guidance on the classification of high-risk systems and on the interaction of the Regulation with other sectoral legislation.

New governance system

In this context, it is particularly important to ensure proper coordination of the strategy. Therefore, the following is proposed:

• Apply AI Alliance: The existing AI Alliance becomes the premier coordination forum that brings together AI vendors, industry leaders, academia, and the public sector. Sector-specific groups will allow the implementation of the strategy to be discussed and monitored.
• AI Observatory: An AI Observatory will be established to provide robust indicators assessing its impact on currently listed and future sectors, monitor developments and trends.

Complementary strategies: science and data as the main axes

The Apply AI Strategy does not act in isolation, but is complemented by two other fundamental strategies: the AI in Science Strategy and the Data Union Strategy.

AI in Science Strategy

Presented together with the Apply AI Strategy, this strategy supports and incentivises the development and use of artificial intelligence by the European scientific community. Its central element is RAISE (Resource for AI Science in Europe), which was presented in November at the AI in Science Summit and will bring together strategic resources: funding, computing capacity, data and talent. RAISE will operate on two pillars: Science for AI (basic research to advance fundamental capabilities) and AI in Science (use of artificial intelligence for progress in different scientific disciplines).

Data Union Strategy

This strategy will focus on ensuring the availability of high-quality, large-scale datasets, essential for training AI models. A key element will be the Data Labs associated with the AI Factories, which will bring together and federate data from different sectors, linking with the  corresponding European Common Data Spaces, making them available to developers under the appropriate conditions.

In short, through significant investments in infrastructure, access to quality data, talent development and a regulatory framework that promotes responsible innovation, the European Union is creating the necessary conditions for companies, public administrations and citizens to take advantage of the full transformative potential of artificial intelligence. The success of these strategies will depend on collaboration between European institutions, national governments, businesses, researchers and developers.

Today, transparency, innovation and economic development are key to the progress of public institutions. In this context, the Cabildo of Tenerife has undertaken an ambitious open data project that goes beyond the mere publication of information. The aim of this strategy is to ffacilitate access to information, encourage the reuse of data and generate social and economic value for the island.

Through its open data portal, the Cabildo not only promotes transparency and accountability, but also drives innovation in areas as diverse as tourism, transport and the rural environment.

Scope and objectives of the project

The open data portal of the Cabildo de Tenerife publishes datasets of the entire public sector of the island, including all the local councils of the island. In particular, this includes municipalities with less than 20,000 inhabitants, as established in Law 8/2015 on Island Councils. The main objectives of this project are:

• Strengthen the internal and external culture of data use.
• Increase transparency and accountability through data.
• Generate wealth in society through the reuse of information.

In this website you can find open datasets on tourism, transport, culture and leisure and rural environment, among others. In order to offer this information the Cabildo of Tenerife benefits from the collaboration of various bodies such as:

• Transportes Interurbanos de Tenerife (TITSA)
• Tenerife Island Water Board (CIATF)
• Metropolitan of Tenerife
• SINPROMI (Insular Society for the Promotion of People with Disabilities)
• ITER (Technological Institute of Renewable Energies)
• IASS (Insular Institute for Social and Socio-sanitary Care)
• Agrocabildo

Most downloaded and significant datasets

Some of the portal's most downloaded and significant datasets include:

• Network of weather stations, with updates every 10 minutes.
• Influx of recreational areas, such as Punta Teno and Barranco de Masca, with information on the number of vehicles and people, and the itineraries of trails or routes on the island.
• Indicators of economic development and tourist occupancy, including the number of tourists accommodated by category and area.
• Prices of fruit and vegetable products in Mercatenerife.
• Public transport Origin Destination Matrix, which shows the relationships between the places of origin and destination of journeys made on the island.

The Cabildo's open data project is clearly oriented towards compliance with the Technical Interoperability Standard (NTI) for the Reuse of Information Resources and the DCAT-AP model, areas in which it continues to make progress.

Use cases and applications to incentivise reuse

Beyond publishing open datasets, the Cabildo de Tenerife actively promotes their use through the development of use cases and applications. Examples of this work include:

• Development of a urban development plan in the municipality of Santiago del Teide through the reuse of various datasets.
• Project on meteorological data forecasting.
• Tourist places and traffic scorecards. Specifically:
  
  • On tourism: tourist accommodation places by municipality and type, and the occupancy rate by type of accommodation from 1978 to 2023.
  • About traffic: dashboard from Power BI showing the average daily traffic intensity at a station on the island for the years 2021, 2022 and 2023.

On the other hand, in order to promote the reuse of the data on its portal, the Cabildo of Tenerife organises various activities, such as the I Ideas Reuse Competition, in which 25 proposals were received. This competition will be followed by a second edition that will reward the development of applications.

In addition, there are talks and webinars, such as the one organised in collaboration with the Chair of Big Data, Open Data and Blockchain of the University of La Laguna on how to use open data from Tenerife to drive innovation , which you can see again here.

Next steps AI and community building

In order to measure the impact of open data, the Cabildo de Tenerife uses tools such as Google Analytics which allows the analysis of user interaction with the available data. The next big step, as reported by the organisation, will be to implement a virtual assistant with generative AI that will allow:

• Analysing data with natural language.
• Discover trends and correlations.
• Bringing information closer to any citizen.

In parallel, the Cabildo of Tenerife will also continue working on new avenues of collaboration with the island's municipalities and other entities, with the aim of expanding the amount and variety of open data available to citizens.

From datos.gob.es, we encourage development and research professionals, students and citizens to explore, reuse and create value with Tenerife's data.

Visit datos.tenerife.es

In an increasingly data-driven world, all organisations, both private companies and public bodies, are looking to leverage their information to make better decisions, improve the efficiency of their processes and meet their strategic objectives. However, creating an effective data strategy is a challenge that should not be underestimated.

Often, organisations in all sectors fall into common mistakes that can compromise the success of their strategies from the outset. From ignoring the importance of data governance to not aligning strategic objectives with the real needs of the institution, these failures can result in inefficiencies, non-compliance with regulations and even loss of trust by citizens, employees or users.

In this article, we will explore the most common mistakes in creating a data strategy, with the aim of helping both public and private entities to avoid them. Our goal is to provide you with the tools to build a solid foundation to maximise the value of data for the benefit of your mission and objectives.

Figure 1. Tips for designing a data governance strategy. Source: own elaboration.

The following are some of the most common mistakes in developing a data strategy, justifying their impact and the extent to which they can affect an organisation:

Lack of linkage to organisational objectives and failure to identify key areas

For data strategy to be effective in any type of organisation, it is essential that it is aligned with its strategic objectives. These objectives include key areas such as revenue growth, service improvement, cost optimisation and customer/citizen experience. In addition, prioritising initiatives is essential to identify the areas of the organisation that will benefit most from the data strategy. This approach not only allows maximising the return on data investment, but also ensures that initiatives are clearly connected to desired outcomes, reducing potential gaps between data efforts and strategic objectives.

Failure to define clear short- and medium-term objectives

Defining specific and achievable goals in the early stages of a data strategy is very important to set a clear direction and demonstrate its value from the outset. This boosts the motivation of the teams involved and builds trust between leaders and stakeholders. Prioritising short-term objectives, such as implementing a dashboard of key indicators or improving the quality of a specific set of critical data, delivers tangible results quickly and justifies the investment in the data strategy. These initial achievements not only consolidate management support, but also strengthen the commitment of the teams.

Similarly, medium-term objectives are essential to build on initial progress and prepare the ground for more ambitious projects. For example, the automation of reporting processes or the implementation of predictive models for key areas can be intermediate goals that demonstrate the positive impact of the strategy on the organisation. These achievements allow us to measure progress, evaluate the success of the strategy and ensure that it is aligned with the organisation's strategic priorities.

Setting a combination of short- and medium-term goals ensures that the data strategy remains relevant over time and continues to generate value. This approach helps the organisation to move forward in a structured way, strengthening its position both vis-à-vis its competitors and in fulfilling its mission in the case of public bodies.

Failure to conduct a maturity assessment beforehand to define the strategy as narrowly as possible.

Before designing a data strategy, it is crucial to conduct a pre-assessment to understand the current state of the organisation in terms of data and to realistically and effectively scope it. This step not only prevents efforts from being dispersed, but also ensures that the strategy is aligned with the real needs of the organisation, thus maximising its impact. Without prior assessment, it is easy to fall into the error of taking on initiatives that are too broad or poorly connected to strategic priorities .

Therefore, conducting this pre-assessment is not only a technical exercise, but a strategic tool to ensure that resources and efforts are well targeted from the outset. With a clear diagnosis, the data strategy becomes a solid roadmap, capable of generating tangible results from the earliest stages. It should be recalled that the UNE 0080:2023, which focuses on the assessment of the maturity of data governance and management, provides a structured framework for this initial assessment. This standard allows for an objective analysis of the organisation's processes, technologies and capabilities around data..

Failure to carry out data governance initiatives

The definition of a sound strategy is fundamental to the success of data governance initiatives. It is essential to have an area or unit responsible for data governance, such as a data office or a centre of excellence, where clear guidelines are established and the necessary actions are coordinated to achieve the committed strategic objectives. These initiatives must be aligned with the organisation's priorities, ensuring that the data is secure, usable for its intended purpose and compliant with applicable laws and regulations.

A robust data governance framework is key to ensuring consistency and quality of data, strengthening confidence in reporting and analysis that generates both internal and external value. In addition, an appropriate approach reduces risks such as non-compliance, promoting effective use of data and protecting the organisation's reputation.

It is therefore important to design these initiatives with a holistic approach, prioritising collaboration between different areas and aligning them with the overall data strategy. For more information on how to structure an effective data governance system, see this series of articles: From data strategy to data governance system - Part 1.

Focusing exclusively on technology

Many organisations have the mistaken view that acquiring sophisticated tools and platforms will be the ultimate solution to their data problems. However, technology is only one part of the ecosystem. Without the right processes, governance framework and, of course, people, even the best technology will fail. This is problematic because it can lead to huge investments with no clear return, as well as frustration among teams when they do not get the expected results.

Failure to involve all stakeholders and define roles and responsibilities

A sound data strategy needs to bring together all relevant actors, whether in a public administration or in a private company. Each area, department or unit has a unique vision of how data can be useful to achieve objectives, improve services or make more informed decisions. Therefore, involving all stakeholders from the outset not only enriches the strategy, but also ensures that they are aligned with the real needs of the organisation.

Likewise, defining clear roles and responsibilities is key to avoid confusion and duplication. Knowing who is responsible for the data, who manages it and who uses it ensures a more efficient workflow and fosters collaboration between teams. In both the public and private spheres, this approach helps to maximise the impact of the data strategy, ensuring that efforts are coordinated and focused towards a common goal.

Failure to establish clear metrics of success

Establishing key performance indicators (KPIs) is essential to assess whether initiatives are generating value. KPIs help demonstrate the results of the data strategy, reinforcing leadership support and encouraging willingness to invest in the future. By measuring the impact of actions, organisations can guarantee the sustainability and continuous development of their strategy, ensuring that it is aligned with strategic objectives and delivers tangible benefits.

Failure to place data quality at the centre

A sound data strategy must be built on a foundation of reliable and high quality data. Ignoring this aspect can lead to wrong decisions, inefficient processes and loss of trust in data by teams. Data quality is not just a technical aspect, but a strategic enabler: it ensures that the information used is complete, consistent, valid and timely.

Integrating data quality from the outset involves defining clear metrics, establishing validation and cleansing processes, and assigning responsibilities for their maintenance. Furthermore, by placing data quality at the heart of the strategy, organisations can unlock the true potential of data, ensuring that it accurately supports business objectives and reinforces user confidence. Without quality, the strategy loses momentum and becomes a wasted opportunity.

Failure to manage cultural change and resistance to change

The transition to a data-driven organisation requires not only tools and processes, but also a clear focus on change management to engage employees. Promoting an open mind towards new practices is key to ensuring the adoption and success of the strategy. By prioritising communication, training and team engagement, organisations can facilitate this cultural change, ensuring that all levels work in alignment with strategic objectives and maximising the impact of the data strategy.

Not planning for scalability

It is critical for organisations to consider how their data strategy can scale as the volume of information grows. Designing a strategy ready to handle this growth ensures that systems can support the increase in data without the need for future restructuring, optimising resources and avoiding additional costs. By planning for scalability, organisations can ensure long-term sustainable operational efficiency and maximise the value of their data as their needs evolve.

Lack of continuous updating and review of the strategy

Data and organisational needs are constantly evolving, so it is important to regularly review and adapt the strategy to keep it relevant and effective. A flexible and up-to-date data strategy allows you to respond nimbly to new opportunities and challenges, ensuring that you continue to deliver value as market or organisational priorities change. This proactive approach ensures that the strategy remains aligned with strategic objectives and reinforces its long-term positive impact.

In conclusion, it is important to highlight that the success of a data strategy lies in its ability to align with the strategic objectives of the organisation, setting clear goals and encouraging the participation of all areas involved. A good data governance system, accompanied by metrics to measure its impact, is the basis for ensuring that the strategy generates value and is sustainable over time.

In addition, addressing issues such as data quality, cultural change and scalability from the outset is essential to maximise its effectiveness. Focusing exclusively on technology or neglecting these elements can limit results and jeopardise the organisation's ability to adapt to new opportunities and challenges. Finally, continuously reviewing and updating the strategy ensures its relevance and reinforces its positive impact.

To learn more about how to structure an effective data strategy and its connection with a solid data governance system, we recommend exploring the articles published in datos.gob.es: From Data Strategy to Data Governance System - Part 1 and Part 2.. These resources complement the concepts presented in this article and offer practical insights for implementation in any type of organisation.

Content elaborated by Dr. Fernando Gualo, Professor at UCLM and Data Governance and Quality Consultant. The content and the point of view reflected in this publication are the sole responsibility of its author.

Local public bodies, such as county councils and municipalities, play a crucial role in opening their data to the public. Making data available to citizens not only builds trust in institutions, but also drives innovation, facilitates citizen participation and promotes informed decision-making. Through open data, these entities can contribute to a more efficient, collaborative and accountable management that is more responsive to the needs of society.

To find out about their activities and the most popular datasets, a survey was carried out last May in which more than 65 representatives of local authorities took part. Some of the conclusions drawn are summarised below.

Strategies to boost open data

Some municipalities have designed specific open data strategies tailored to their needs. This is the case of the city councils of Barakaldo, Pinto, Sant Feliu de Llobregat and Valencia, among others. 

Other municipalities include the promotion of open data within broader strategic plans, such as Granada and its Strategic Plan for Innovation and SmartCity. In its strategic line number eight, the Granada City Council includes actions related to data governance and its data platform, focusing on the application of Open Data principles in the City Council and the public availability of municipal databases.

In addition, some city councils are going a step further, such as Alcobendas City Council, which is updating its open data strategy with a Data Governance strategy, focused on three axes (people, processes and technology), with which they seek to promote the data culture. Vitoria Gasteiz, for its part, is working on the constitution of a data office and the necessary infrastructure to support it, which will allow them to design a more general data strategy.

This is a path where the Provincial Councils have a lot to say, helping local councils to make progress in making data of interest available to citizens. One example is the Cordoba Provincial Council, which has launched a Public Sector Information Re-use Plan to promote the opening of data in a homogeneous way through its provincial smart platform and automated uploading.  The provincial councils also promote open data through training, giving courses to public employees in the province. This has been done by the Salamanca and Cáceres Provincial Councils.

Provincial Council

Publishing open data is not enough if it is not updated in a continuous and timely manner. As a result, all respondents agree that updating their datasets is one of the most time and resource consuming tasks. Keeping open data up to date allows citizens, businesses and other stakeholders to access relevant and timely information, facilitating planning, research and the development of new solutions in a changing environment.

To this end, many respondents confirm that they carry out regular audits as well as inventories of databases - as, for example, the municipality of Quart de Poblet. These inventories are also being carried out in order to identify high-value data. This is the case of Valencia City Council, which is cataloguing current data and classifying them according to whether or not they belong to the so-called high-value categories.

In order to ensure that the data provided are up to date, many organisations are also promoting automation processes, such as the Diputación de Salamanca. By implementing automated systems, public bodies can ensure that data are continuously updated, reducing human error and optimising resources. In addition, it ensures that the information is the same on all of the organisation's portals.

In addition to continuous updating and improvement audits, the use of geospatial data enables local authorities to better understand their environment and make informed decisions. Publishing this type of data makes it possible to visualise the distribution of services, infrastructures, resources and problems in a territory, facilitating urban planning, environmental management and mobility, among other key aspects. In this sense, Spatial Data Infrastructures (SDI) continue to be developed at local level, such as the SDI of the Provincial Council of Guipuzcoa, or Geoportals such as that of the Provincial Council of Cordoba.

Another priority, indicated by Sant Feliu Town Council, is the incorporation of the gender perspective in data publications, whenever and wherever possible, with a double objective: to adequately highlight the differences and inequalities in the situation of women and, above all, to help define corrective public policies.

On the other hand, among the challenges, agencies highlight the technical difficulties in standardising and normalising data at the corporate level, often because information is isolated in silos. For this reason, they see the need to establish coordination mechanisms between areas and structures of data governance.

In addition, the organisations surveyed consider it necessary, in the first place, to promote the data culture within the organisation, by increasing human and technical resources and training.

Most popular datasets

The ultimate goal of open data portals is the re-use of data. In this sense, some of the mechanisms used by local authorities to monitor the use of their data are:

• View statistics on the most downloaded datasets. In this sense, the creation of interactive dashboards is being promoted, such as this one with access to all the datasets of the portals of local councils that use the AOC (Administració Oberta de Catalunya) consortium solution.
• Consultation of feedback and implementation of a rating system.
• Use of software observability platforms , based on artificial intelligence (AI) and automation, to monitor, analyse and optimise the user experience.
• Implementation of tools that generate popularity indexes.
• Creation of forms so that re-users can report on the products and services they develop using the portal's data, such as this example from the Cabildo de Tenerife.

Of all the datasets, the categories highlighted by the local authorities participating in the survey are the following:

Access the accessible version here

In short, local bodies' open data initiatives represent an invaluable opportunity to strengthen open data. Challenges remain, but the commitment to open data is a reality.

The European Parliament's tenth parliamentary term started on July, a new institutional cycle that will run from 2024-2029. The President of the European Commission, Ursula von der Leyen, was elected for a second term, after presenting to the European Parliament her Political Guidelines for the next European Commission 2024-2029.

These guidelines set out the priorities that will guide European policies in the coming years. Among the general objectives, we find that efforts will be invested in:

1. Facilitating business and strengthening the single market.
2. Decarbonise and reduce energy prices.
3. Make research and innovation the engines of the economy.
4. Boost productivity through the diffusion of digital technology.
5. Invest massively in sustainable competitiveness.
6. Closing the skills and manpower gap.

In this article, we will explain point 4, which focuses on combating the insufficient diffusion of digital technologies. Ignorance of the technological possibilities available to citizens limits the capacity to develop new services and business models that are competitive on a global level.

Boosting productivity with the spread of digital technology

The previous mandate was marked by the approval of new regulations aimed at fostering a fair and competitive digital economy through a digital single market, where technology is placed at the service of people. Now is the time to focus on the implementation and enforcement of adopted digital laws.

One of the most recently approved regulations is the Artificial Intelligence (AI) Regulation, a reference framework for the development of any AI system. In this standard, the focus was on ensuring the safety and reliability of artificial intelligence, avoiding bias through various measures including robust data governance.

Now that this framework is in place, it is time to push forward the use of this technology for innovation. To this end, the following aspects will be promoted in this new cycle:

• Artificial intelligence factories. These are open ecosystems that provide an infrastructure for artificial intelligence supercomputing services. In this way, large technological capabilities are made available to start-up companies and research communities.
• Strategy for the use of artificial intelligence. It seeks to boost industrial uses in a variety of sectors, including the provision of public services in areas such as healthcare. Industry and civil society will be involved in the development of this strategy.
• European Research Council on Artificial Intelligence. This body will help pool EU resources, facilitating access to them.

But for these measures to be developed, it is first necessary to ensure access to quality data. This data not only supports the training of AI systems and the development of cutting-edge technology products and services, but also helps informed decision-making and the development of more accurate political and economic strategies. As the document itself states " Access to data is not only a major driver for competitiveness, accounting for almost 4% of EU GDP, but also essential for productivity and societal innovations, from personalised medicine to energy savings”.

To improve access to data for European companies and improve their competitiveness vis-à-vis major global technology players, the European Union is committed to "improving open access to data", while ensuring the strictest data protection.

The European data revolution

"Europe needs a data revolution. This is how blunt the President is about the current situation. Therefore, one of the measures that will be worked on is a new EU Data Strategy. This strategy will build on existing standards. It is expected to build on the existing strategy, whose action lines include the promotion of information exchange through the creation of a single data market where data can flow between countries and economic sectors in the EU.

In this framework, the legislative progress we saw in the last legislature will continue to be very much in evidence:

• Directive (EU) 2019/1024 on open data and re-use of public sector information, which establishes the legal framework for the re-use of public sector information, made available to the public as open data, including the promotion of high-value data.
• Regulation (EU) 2022/868 on European Data Governance (EDG), which regulates the secure and voluntary exchange of data sets held by public bodies over which third party rights concur, as well as data brokering services and the altruistic transfer of data.
• Regulation (EU) 2023/2854 on harmonised rules for fair access to and use of data (Data Act), which promotes harmonised rules on fair access and use of data in the framework of the European Strategy.

The aim is to ensure a "simplified, clear and coherent legal framework for businesses and administrations to share data seamlessly and at scale, while respecting high privacy and security standards".

In addition to stepping up investment in cutting-edge technologies, such as supercomputing, the internet of things and quantum computing, the EU plans to continue promoting access to quality data to help create a sustainable and solvent technological ecosystem capable of competing with large global companies. In this space we will keep you informed of the measures taken to this end.

The Artificial Intelligence Strategy 2024 is the comprehensive plan that establishes a framework to accelerate the development and expansion of artificial intelligence (AI) in Spain. This strategy was approved, at the proposal of the Ministry for Digital Transformation and the Civil Service, by the Council of Ministers on 14 May 2024 and comes to reinforce and accelerate the National Artificial Intelligence Strategy (ENIA), which began to be deployed in 2020.

The dizzying evolution of the technologies associated with Artificial Intelligence in recent years justifies this reinforcement. For example, according to the AI Index Report for 2024 by Stanford University AI investment has increased nine-fold since 2022. The cost of training models has risen dramatically, but in return AI is driving progress in science, medicine and overall labour productivity in general. For reasons such as these, the aim is to maximise the impact of AI on the economy and to build on the positive elements of ongoing work.

The new strategy is built around three main axes, which will be developed through eight lines of action. These axes are:

• Strengthen the key levers for AI development. This axis focuses on boosting investment in supercomputing, building sustainable storage capacity, developing models and data to form a public AI infrastructure, and fostering AI talent .
• Facilitate the expansion of AI in the public and private sector, fostering innovation and cybersecurity. This axis aims to incorporate AI into government and business processes, with a special emphasis on SMEs, and to develop a robust cybersecurity framework .
• Promote transparent, ethical and humanistic AI. This axis focuses on ensuring that the development and use of AI in Spain is responsible and respectful of human rights, equality, privacy and non-discrimination.

The following infographic summarises the main points of this strategy:

Go to click to enlarge the infographic

Spain's Artificial Intelligence Strategy 2024 is a very ambitious document that seeks to position our country as a leader in Artificial Intelligence, expanding the use of robust and responsible AI throughout the economy and in public administration. This will help to ensure that multiple areas such as culture or the city design can benefit from these developments.

Openness and access to quality data are also critical to the success of this strategy, as it is part of the raw material needed to train and evaluate AI models that are also inclusive and socially just  so that they benefit society as a whole. Closely related to open data, the strategy dedicates specific levers to the promotion of AI in the public sector and the development of foundational and specialised corpora and language models . This also includes the development of common services based on AI models and the implementation of a data governance model to ensure the security, quality, interoperability and reuse of the data managed by the General State Administration (AGE, in Spanish acronyms).

The foundational models (Large Language Models or LLMs) are large-scale models that will be trained on large corpora of data in Spanish and co-official languages, thus ensuring their applicability in a wide variety of linguistic and cultural contexts. Smaller, specialised models (Small Language Models or SLMs) will be developed with the aim of addressing specific needs within particular sectors with a lower demand for computational resources.

Common data governance of the AGE

Open data governance will play a crucial role in the realisation of the stated objectives, e.g. to achieve an efficient development of specialised language models. With the aim of encouraging the creation of these models and facilitating the development of applications for the public sphere, the strategy foresees a uniform governance model for data, including the documentary corpus of the General State Administration, ensuring the standards of security, quality, interoperability and reusability of all data.

This initiative includes the creation of a unified data space to exploit sector-specific datasets to solve specific use cases for each agency. Data governance will ensure anonymisation and privacy of information and compliance with applicable regulations throughout the data lifecycle.

A data-driven organisational structure will be developed, with the Directorate-General for Data as the backbone. In addition, the AGE Data Platform, the generation of departmental metadata catalogues, the map of data exchanges and the promotion of interoperability will be promoted. The aim is to facilitate the deployment of higher quality and more useful AI initiatives.

Developing foundational and specialised corpora and language models

Within lever number three, the document recognises that the fundamental basis for training language models is the quantity and quality of available data, as well as the licenses that enable the possibility to use them.

The strategy places special emphasis on the creation of representative and diversified language corpora, including Spanish and co-official languages such as Catalan, Basque, Galician and Valencian. These corpora should not only be extensive, but also reflect the variety and cultural richness of the languages, which will allow for the development of more accurate models adapted to local needs.

To achieve this, collaboration with academic and research institutions as well as industry is envisaged to collect, clean and tag large volumes of textual data. In addition, policies will be implemented to facilitate access to this data through open licences that promote re-use and sharing.

The creation of foundational models focuses on developing artificial intelligence algorithms, trained on the basis of these linguistic corpora that reflect the culture and traditions of our languages. These models will be created in the framework of the ALIA project, extending the work started with the pioneering MarIA, and will be designed to be adaptable to a variety of natural language processing tasks. Priority will also be given, wherever possible, to making these models publicly accessible, allowing their use in both the public and private sectors to generate the maximum possible economic value.

In short, Spain's National Artificial Intelligence Strategy 2024 is an ambitious plan that seeks to position the country as a European leader in the development and use of responsible AI technologies, as well as to ensure that these technological advances are made in a sustainable manner, benefiting society as a whole. The use of open data and public sector data governance also contributes to this strategy, providing fundamental foundations for the development of advanced, ethical and efficient AI models that will improve public services and drive economic growth drive economic growth. And, in short, Spain's competitiveness in a global scenario in which all countries are making a major effort to boost AI and reap these benefits.

Content prepared by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization. The contents and points of view reflected in this publication are the sole responsibility of its author. 

Two of the European Union's most relevant data regulations will soon articulate the legal contours that will delineate the development of the data economy in the coming years. The Data Governance Act (DGA) has been fully applicable since September 24, 2023, while the wording of the Data Act (DA) was approved on November 27. 

They are not the only ones, as the legal framework already includes other important rules that regulate interconnected matters, thus revealing the proactive approach of the European Union in establishing rules of the game in line with the needs of European citizens and businesses. These guidelines provide the necessary legal security environment to achieve the ultimate goal of promoting a European Digital Single Market.  

 In the case of the DGA and the DA, the negotiations for their approval have shown that their objectives were shared by the stakeholders concerned. For both, data is a central element for digital transformation, and they share an interest in eliminating or reducing the barriers and obstacles to its sharing. They thus assume that data-driven innovation will bring enormous benefits to citizens and the economy. Therefore, creating legal frameworks that facilitate such processes is a common goal for companies, institutions and citizens. 

The contributions from the academic, business and associative worlds have been abundant and enriching, both for the drafting phase of the standards and for what will be their implementation and development in practice. One of the most reiterated questions is the concern about how the different standards of this 'digital regulatory package' will interact. Particularly important is the interaction with the General Data Protection Regulation, which is why DGA and DA have established general guidelines on the pre-eminence of said regulation in case of conflict. In this regard, the increase in regulation does not prevent specific situations from arising in practice around key concepts in the field of personal data, such as consent, purposes of processing, anonymization, or portability. 

Another of the issues highlighted has to do with the search for synergies between this regulation and current or future data business models. The recognized overall goal is to boost the development of data spaces and the data economy as a whole. This goal will be closer to the extent that the 'regulatory burden' does not reduce the incentives for companies to invest in collecting and managing data; that it does not weaken the competitive position of European companies (by adequately protecting trade secrets, intellectual property rights and confidentiality); and that there is an appropriate balance between general and business interests. 

The case of the Data Governance Act  

In the case of the DGA, the provisions related to data brokering services ––one of the central parts of the regulation–– occupied a significant part of the previous analyses carried out. For example, the question was raised as to what extent SMEs and start-ups could compete with large technology companies in the provision of these services; or whether, by requiring the structural separation required of data brokering service providers (through a separate legal entity), there could be problems related to other functionalities of the same companies.    

Along the same lines, the question arises as to whether a more decentralized data economy requires new intermediaries, or whether under the new legal formulation, they can successfully compete in data markets through alternative, non-vertically integrated business models. 

Considerations on the deployment of the Data Act  

With regard to the DA, the final wording of the regulation clarified its scope, the definition of concepts and the categorization of data, as suggested by the industry. The specific sectoral application to be developed subsequently will further define those concepts and interpretations that provide the desirable legal certainty.  

This legal certainty has also been argued in relation to trade secrets, intellectual property rights and confidentiality; an aspect that the Regulation seeks to address with safeguards aimed at preventing misuse and fraud. 

Other aspects that attracted attention were compensation for making data available; dispute resolution procedures; provisions on unfair contract terms (aimed at compensating for imbalances in bargaining power); making data available in case of exceptional need; and, finally, provisions on switching from one data processing service provider to another.  

 A positive starting point  

The starting point, in any case, is positive. The data economy in the European Union is taking hold on the basis of the European Data Strategy and the regulatory package that develops it. There are also practical examples of the potential of the industrial ecosystems that are being deployed around the Common European Data Spaces in sectors such as tourism, mobility and logistics, and agri-food, among others. In addition, initiatives that bring together public and private interests in this area are making significant progress in the deployment of technical and governance foundations, strengthening the competitive position of European companies, and achieving the ultimate goal of a single data market in the European Union. 

Click here for an extended version of this note. 

In the first part of this article, the concept of data strategy was introduced as the organisation's effort to put the necessary data at the service of its business strategy. In this second part, we will explore some aspects related to the materialisation of such a strategy as part of the design or maintenance - if it already exists - of a data governance system.

For the materialisation of the data strategy, a development environment will have to be addressed, as described in a founding act that includes some aspects such as the identification of the main people responsible for the implementation, the expected results, the available resources and the timeframe established to achieve the objectives. In addition, it will contain a portfolio of data governance programmes including individual projects or specific related projects to address the achievement of the strategic objectives of the data.

It is important to mention that the implementation of a data strategy has an impact on the development and maintenance of the different components of a data governance system:

• Manager
• Organisational structures
• Principles, policies and frameworks
• For Information
• Culture, ethics and behaviour
• People, skills and competences
• Services, infrastructures and applications.

In this sense, it can be said that each of the projects included in the data government's programme aims to contribute to developing or maintaining one or more of these components.

It should be noted that the final design of this data governance system is achieved in an iterative and incremental manner over time, depending on the constraints and possibilities of the organisation and its current operating context. Consequently, the prioritisation, selection and sequencing of projects within the data governance programme to implement the strategic objectives of data also has an iterative and incremental nature[1].

The three biggest data risks commonly encountered in organisations are:

• Not knowing who has to take responsibility for implementing the data strategy,
• Not having adequate knowledge of data in quantity and quality, and
• Failure to exercise adequate control over the data, e.g. by at least complying with current legislation.

Therefore, as far as possible, projects should be approached in the following way:

• Firstly, to address those projects related to the identification, selection or maintenance of organisational structures ("strategic alignment" type objective), also known as governance framework.
• Next, undertake projects related to the knowledge of the business processes and the data used (a "strategic alignment" type objective aimed at the description of data through the corresponding metadata, including data lifecycle metadata).
• And finally, proceed to the definition of policies and derived controls for different policy areas (which may be of the "strategic alignment", "risk optimisation" or "value for money" type). 

The artefact-based approach and the process approach

In approaching the definition of these data governance programmes, some organisations with a project understanding more oriented to the generation and deployment of technological products follow an artefact-based approach. That is, they approach the projects that are part of the data governance programme as the achievement of certain artefacts. Thus, it is possible to find organisations whose first concern when implementing data governance is to acquire and install a specific tool that supports, for example, a glossary of terms, a data dictionary, or a data lake. Moreover, as for various reasons some companies do not adequately differentiate between data governance and data management, this approach is often sufficient. However, the artefact approach introduces the risk of "the tool without the instruction manual the artefact approach introduces the risk of "the tool without the instruction manual": the tool is purchased - probably after a proof of concept by the vendor - and deployed according to the needs of the organisation, but what it is used for and when it is used is unknown, leaving the artefact often as an isolated resource. This, unless the organisation promotes a profound change, may end up being a waste of resources in the long run as the use of the artefacts generated is abandoned.

A better alternative, as has been widely demonstrated in the software development sector, is the execution of the data governance programme with a process approach. This process approach allows not only to develop the necessary artefacts, but also to model the way the organisation works with respect to some area of performance, and contextualises the rationale and use of the artefacts within the process, specifying who should use the artefact, for what, when, what should be obtained by using the artefact, etc.

This process approach is an ideal instrument to capture and model the knowledge that the organisation already has regarding the tasks covered by the process, and to make this knowledge the reference for new operations to be carried out in the future. In addition, the definition of the process also allows for the particularisation of chains of responsibility and accountability and the establishment of communication plans, so that each worker knows what to do, what artefacts to use, who to ask for or receive resources from to carry out their work, who to communicate their results to, or who to escalate potential problems to.

This way of working provides some advantages, such as predictable behaviour of the organisation with respect to the process; the possibility to use these processes as building blocks for the execution of data projects; the option to easily replace a human resource; or the possibility to efficiently measure the performance of a process. But undoubtedly, one of the greatest advantages of this process approach is that it allows organisations to adopt the good practices contained in any of the process reference models for data governance, data management and quality management that exist in the current panorama, such as the UNE 0077 specifications (for data governance), UNE 0078 (for data management) and UNE 0079 (for data quality management).

This adoption enables the possibility of using frameworks for process assessment and improvement, such as the one described in UNE 0080, which includes the Alarcos Data Maturity Model, in which the concept of organisational maturity of data governance, data management and data quality management is introduced as an indicator of the organisation's potential to face the achievement of strategic objectives with certain guarantees of success. In fact, it is common for many organisations adopting the process approach to pre-include specific data objectives ("strategic alignment" type objectives) aimed at preparing the organisation - by increasing the level of maturity - to better support the execution of the data governance programme. These "preparatory" objectives are mainly manifested in the implementation of data governance, data management and data quality management processes to close the gap between the current initial state of maturity (AS_IS) and the required final state of maturity of the organisation (TO_BE).

If the process approach is chosen, the different projects contained in the data programme will generate controlled and targeted increments in each of the components of the data governance system, which will enable the transformation of the organisation to meet the organisation's strategic business objectives.

Ultimately, the implementation of a data strategy manifests itself in the development or maintenance of a data governance system, which ensures that the organisation's data is put at the service of strategic business objectives. The instrument to achieve this objective is the data governance programme, which should ideally be implemented through a process approach in order to benefit from all the advantages it brings.

More and more organisations are deciding to govern their data to ensure that it is relevant, adequate and sufficient for its intended uses, i.e. that it has a certain organisational value.

Although the scenarios are often very diverse, a close look at needs and intentions reveals that many of these organisations had already started to govern their data some time ago but did not know it. Perhaps the only thing they are doing as a result of this decision is to state it explicitly. This is often the case when they become aware of the need to contextualise and justify such initiatives, for example, to address a particular organisational change - such as a long-awaited digital transformation - or to address a particular technological challenge such as the implementation of a data lake to better support data analytics projects.

A business strategy may be to reduce the costs required to produce a certain product, to define new lines of business, to better understand customer behaviour patterns, or to develop policies that address specific societal problems. To implement a business strategy you need data, but not just any data, but data that is relevant and useful for the objectives included in the business strategy. In other words, data that can be used as a basis for contributing to the achievement of these objectives. Therefore, it can be said that when an organisation recognises that it needs to govern its data, it is really expressing its need to put certain data at the service of the business strategy. And this is the real mission of data governance.

Having the right data for the business strategy requires a data strategy. It is a necessary condition that the data strategy is derived from and aligned with a business strategy. For this reason, it is possible to affirm that the projects that are being developed (especially those that seek to develop some technological artefact), or those that are to be developed in the organisation, need a justification determined by a data strategy[1] and, therefore, will be part of data governance.

Strategic objectives of the data

A data strategy is composed of a set of strategic data objectives, which may be one or a necessary combination of the following four generic types:

• Benefits realisation: ensuring that all data producers have the appropriate mechanisms in place to produce the data sources that support the business strategy, and that data consumers have the necessary data to be able to perform the tasks required to achieve the strategic objectives of the business. Examples of such objectives could be:
  
  • the definition of the organisation's reporting processes;
  • the identification of the most relevant data architecture to service all data needs in a timely manner;
  • the creation of data service layers;
  • the acquisition of data from third party sources to meet certain data demands; or
  • the implementation of information technologies supporting data provisioning and consumption
• Strategic alignment: the objective is to align the data with basic principles or behavioural guidelines that the organisation has defined, should have defined, or will define as part of the strategy. This alignment seeks to homogenise the way of working with the organisation's data. Examples of this type of objective include:
  
  • establish organisational structures to support chains of responsibility and accountability;
  • homogenise, reconcile and unify the description of data in different types of metadata repositories;
  • define and implement the organisation's best practices with respect to data governance, data management and data quality management[2];
  • readapt or enrich (what in DAMA terminology is known as operationalising data governance) the organisation's data procedures in order to align them with the good practices implemented by the organisation in the different processes;
  • or define data policies in any of the areas of data management[3] and ensure compliance with them, including security, master data management, historical data management, etc.
• Resource optimisation: this consists of establishing guidelines to ensure that the generation, use and exploitation of data makes the most appropriate and efficient use of the organisation's resources. Examples of such targets could include:
  
  • the decrease of data storage and processing costs to much more efficient and effective storage systems, such as migrations of data storage and processing layers to the cloud[4];
  • improving response times of certain applications by removing historical data; improving data quality;
  • improving the skills and knowledge of the different actors involved in the exploitation and use of data;
  • the redesign of business plans to make them more efficient; or
  • the redefinition of roles to simplify the allocation and delegation of responsibilities.
• Risk optimisation: the fundamental objective is to analyse the possible risks related to data that may undermine the achievement of the different business objectives of the organisation, or even jeopardise its viability as an entity, and to develop the appropriate data processing mechanisms. Some examples of this type of target would be:
  
  • the definition or implementation of security and data protection mechanisms;
  • the establishment of the necessary ethical parameters; or
  • securing sufficiently qualified human resources to cope with functional turnover.

A close reading of the proposed examples might lead one to think that some of these strategic data objectives could be understood as being of different types simultaneously. For example, ensuring the quality of data to be used in certain business processes may seek, in some way, to ensure that the data is not only used ('benefit realisation' and 'risk optimisation'), but also helps to ensure that the organisation has a serious and responsible brand image with data ('strategic alignment') that avoids having to perform frequent data cleansing actions, with the consequent waste of resources ('value for money' and 'risk optimisation').

Typically, the process of selecting one or more strategic data objectives should not only take into account the context of the organisation and the scope of these objectives in functional, geographic or dataterms, but also consider the dependency between the objectives and the way in which they should be sequenced. It may be common for the same strategic objective to cover data used in different departments or even to apply to different data. For example, the strategic objective of the types "benefit realisation" and "risk optimisation", called "ensuring the level of access to personal data repositories", would cover personal data that can be used in the commercial and operational departments.

Taking into account typical data governance responsibilities (evaluate, manage, monitor), the use of the SMART (specific, measurable, achievable, realistic, time-bound) technique is recommended for the selection of strategic objectives. Thus, these strategic objectives should:

• be specific,
• the level of achievement can be measured and monitored,
• that are achievable and realistic within the context of the strategy and the company, and finally,
• that their achievement is limited in time.

Once the strategic data objectives have been identified, and the backing and financial support of the organisation's management is in place, their implementation must be addressed, taking into account the dimensions discussed above (context, functional aspects and dependencies between objectives), by defining a specific data governance programme. It is interesting to note that behind the concept of "programme" is the idea of "a set of interrelated projects contributing to a specific objective".

In short, a data strategy is the way in which an organisation puts the necessary data at the service of the organisation's business strategy. This data strategy is composed of a series of strategic objectives that can be of one of the four types outlined above or a combination of them. Finally, the implementation of this data strategy will be done through the design and execution of a data governance programme, aspects that we will address in a future post.

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