Abstract

This article analyzes the use of publicly-owned massive data (Open Data) for communicative and business purposes by organizations. The objectives are to identify the communicative strategies that are being developed in this regard, the regulatory framework in which they take place and their impact on the generation of value. To this end, two significant cases of Open Data use have been investigated: the National Library of Spain and the Open Data Portal of the Junta de Castilla y León. It has been observed that the richness provided by the use of data is in relation to the type of filtering carried out, although it is found that elements such as personalization and interaction with users predominate for the generation of value.

 

Introduction

Spain is one of the countries that is spearheading the institutional advance towards the use of Open Data as a system of participation and economic profitability of the same, as reflected in the report of the European Data Portal, Analytical Report 13 (2019), which places Spain, along with Ireland and France, as a benchmark in the use of good Open Data practices. The parameters analyzed in the study are the political framework, the portal, the quality and their impact.

The position adopted by Spain in this regard is important taking into account, as Smolan and Erwitt (2012) point out, that we are facing the era with more capacity for stored data. All of these data are being integrated into the daily social routine so that, as indicated in Mosco (2014: 4), they involve «economic (who pays for it?), Political (who controls it?), social (how is privacy managed?), environmental (what is the impact on the ground and energy use?) and cultural (what values ​​do they imply?) ».

In such reuse of Open Data, it is necessary to bear in mind that these are publicly owned data, the use of which must be framed within a regulatory framework that complies with the principles of equality and privacy. Therefore, there must be a balance between privacy and use, taking into account the economic impact produced from the reuse of said data.

In addition, Burrows and Savage (2014), explain that users voluntarily contribute data and by accepting a series of premises interposed a priori. This fact reflects that one of the parties accepts a series of conditions in exchange for services; so that this interrelation should not take place in only one direction, but there is a direct reciprocity in the satisfaction of interests.

In the use of big data, as indicated by Mayer Schönberger and Cukier (2013: 17) “the change of scale has led to a change of state. The quantitative change has led to a qualitative change. ” This qualitative change is based on the analysis of social behavior combined with predictive models.

For Kitchin (2014: 262), the raw data capture must take place around entire populations of systems, and the classification of the same must reach the maximum detail through common fields that allow flexibility and scalability in use.

These connections require the storage, processing and distribution of the data, as well as the applications and services for individuals and companies that are involved in what is known as Cloud computing (Mosco, 2014: 17). Therefore, the management of massive data in the Cloud is the beginning of this whole new universe, allowing for personalized responses based on the characteristics of the data.

This translates into a vital interest of organizations in being part of this structure, and thus be able to form relationships with users to anticipate needs. Keep in mind, as Chakravorti and Chaturvedi (2017: 16) indicate that the four fundamental variables of digital development are the support conditions, the institutional environment, the

change and innovation and demand conditions.
Obviously, in this environment the door opens to the automation of anticipation

(Lyon, 2014: 6), prediction, micro-segmentation and prescription. Context in which it is necessary to propose a legal framework that establishes a protocol defined in the uses.

A key factor is progress in data processing, allowing it to be managed in a scalable and flexible way. Keep in mind that, as Siegel (2013) points out, prediction feeds on the most powerful natural resource: information. For this, statistics are used together with data mining algorithms. The goal is to identify patterns and build models to get to predict.

As Bertolucci (2013) points out, the key word is datafication, while the heart of many business structures is the infrastructure around information.