13 posts found
Common mistakes in the development of a data strategy
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 chal…
Open data in local authorities: priorities and highlighted datasets
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. Thro…
Data reuse and data governance in the new AI 2024 strategy
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 Ci…
European data regulation faces the challenge of a harmonized application that will boost data sharing
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…
From data strategy to data governance system (part 1)
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…
From data strategy to data governance system (part 2)
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…
When to use each programming language in data science?
Python, R, SQL, JavaScript, C++, HTML... Nowadays we can find a multitude of programming languages that allow us to develop software programmes, applications, web pages, etc. Each one has unique characteristics that differentiate it from the rest and make it more appropriate for certain tasks. But h…
10 Popular natural language processing libraries
The advance of supercomputing and data analytics in fields as diverse as social networks or customer service is encouraging a part of artificial intelligence (AI) to focus on developing algorithms capable of processing and generating natural language.
To be able to carry out this task in the current…
10 Popular Data Analytics and Machine Learning Libraries
Programming libraries refer to the sets of code files that have been created to develop software in a simple way . Thanks to them, developers can avoid code duplication and minimize errors with greater agility and lower cost. There are many bookstores, focused on different activities. A few weeks ag…
11 libraries for creating data visualisations
Programming libraries are sets of code files that are used to develop software. Their purpose is to facilitate programming by providing common functionalities that have already been solved by other programmers.
Libraries are an essential component for developers to be able to program in a simple way…
