estrategias

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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Open data is the highest level of data sharing, as it is freely available and accessible to all. Properly processed and with full respect for the protection of personal data, it can help citizens, businesses and the public sector to make better decisions. 

Open data, together with other data, play a key role in the creation of data spaces, as referred to in the European Data Strategy. As stated in the document, the implementation of common and interoperable data spaces in strategic sectors is set up with the aim of "overcoming technical and legal barriers to data sharing between organisations, combining the necessary tools and infrastructures and addressing trust issues", for example through common standards developed for the space. 

In view of its relevance, the European Data Portal Academy has organised a series of webinars on data spaces. The first of these was held on 12 May in an online format and can be viewed here. In it, the new developments and progress being made regarding data spaces were mentioned, developments that in Spain are being carried out by the Data Office. 

The following training material is now available on data.europa academy:

• The recording of the session;
• The slide deck presented during the webinar.

Just over a year ago - and after an extensive process of analysis, research and public consultation - the UK government announced its new national data strategy created in response to the increasingly important role of data in all aspects of our society. The strategy builds on other related initiatives such as the industrial strategy, the research and development roadmap, the artificial intelligence industry review and the framework agreement between the government and the artificial intelligence industry. As part of this process, five key opportunities have been identified where data could play a truly transformative role in the country: stimulating productivity and trade; supporting the creation of new businesses and jobs; increasing the speed, efficiency and scope of scientific knowledge; improving public policy and services; and contributing to the creation of a fairer society for all.

Such a data strategy is called to be part of the national digital strategy (still under development) and it is built as a whole on four structural pillars. These pillars correspond to the outstanding challenges identified during the analysis phase:

1. The adoption of the basic fundamentals of data, understood as the identification of the data that are most suitable for a given purpose, and the fact that these are always stored in standard formats and in systems that allow their location, access, interoperability and reuse.
2. The development of data capabilities, including a wide range of skills - both technical and managerial - ranging from the most generic and basic to more specific and advanced ones.
3. Improvements in data access and availability, creating an environment that facilitates the flow and reuse of data across all public and private sectors while meeting the necessary security and privacy guarantees.
4. Responsible use of data, securely, ethically and in compliance with all applicable legislation, but also allowing for its exploitation for research, development and innovation.

Based on these pillars, 5 priority action areas are developed as a central part of the strategy, the objective of which will be to respond to the identified barriers that could hinder the new opportunities existing in the data sector:

1. Unlock the value of data in the economy, recognizing the general value of data as a new and thriving economic asset, but at the same time studying each individual case in search of the necessary balance between individual rights and common benefit.
2. Maintain a data policy that is simultaneously pro-growth and pro-trust, so that it does not pose a barrier to entry for new innovators and can keep pace with the growth of new technologies. A regime through which companies manage data more transparently and individuals can make more informed decisions about their data.
3. Transformation in the use of data by government, with the aim of improving the efficiency of public services. This would require the leadership of the Chief Data Officer to revitalize and transform the way in which data is collected, managed, used and shared across government.
4. Ensure the security and reliability of the infrastructures that serve the data, protecting the data from potential cyber-attacks or any other possible threat of disruption or interruption in service - both when in storage and when in use or in transit.
5. Encourage international data flows by creating an environment of trust that ensures the security, privacy and confidentiality of data at all times; removing unnecessary obstacles to data sharing between countries; developing standards that facilitate interoperability; and encouraging international adoption of the same standards and values that apply at home when working with data.

One year after the publication of this strategy, the UK government is launching a new consultation for its reform - this time with the aim of implementing more flexible data governance to accelerate innovation, remove unnecessary barriers to data use and foster economic growth, while protecting the interests of the public. This new consultation comes loaded with multiple reform proposals, some of them quite striking for questioning several of the most relevant aspects within the currently existing baseline data protection framework in an attempt to make it more flexible for businesses. Some of the issues that have been discussed are:

• The obligation to have data protection officers in companies.
• The need to carry out preliminary assessments of the possible impact of their activity on the privacy of individuals.
• The obligation to have records of all personal data processing activities carried out in companies.
• The definition of what exactly constitutes a "data breach" and the threshold above which it would be considered a significant impact breach.
• Establishing a number of legitimate uses of data that would not require consent.

The specific proposals and considerations of the reform can be analyzed in detail through the extensive document published for the consultation and also through the analysis of the estimated impact. However, although the official consultation period has already ended, we will still have to wait a few months to hear the reactions and comments of companies, organizations and citizens to these proposals - once the government concludes its analysis of the responses and its conclusions on this new consultation.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation.

The contents and views expressed in this publication are the sole responsibility of the author.