Promoting Social and Solidarity Economy through Big Data

Promoting Social and Solidarity Economy through Big Data

David De la Antonia López
DOI: 10.4018/978-1-5225-0097-1.ch012
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The aim of this chapter is to describe how to implement a strategy of Big Data to boost Social and Solidarity Economy (SSE). Because of reduction of prices in ICT systems, computing technology has changed and new techniques for distributed computing are the mainstream. With the evolution, it is now possible to manage immense volumes of data that previously could have only been handled by supercomputers at great expense. Through better analysis of the large volumes of data that are becoming available, there is the potential for making faster advances and improving the profitability of many enterprises. Thus, large companies can invest more money into these tools and consequently have more opportunities in obtaining good results. This new situation will widen the gap between large and small organizations, mainly those organizations of modest economic capacity as those that belong to SSE. Therefore, in this research we have made a complete development of software, techniques and tools for implementing a Big Data in SSE. It will help them to narrow the gap with large organizations.
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In the last decade, as a result of economic crisis, have emerged multiplicity of expressions of Social and Solidarity Economy (SSE) acting beyond the formal economy linking action inside and outside the traditional markets. It can be stated that in all sectors of economic activity: health, social housing, agriculture, industry, public transport, etc., the SSE has become a benchmark of sustainable development policies and of common interest. Furthermore, this kind of companies has credited ability to organize efficiently to large numbers of people with problems of social integration and maintenance in the labor market. Companies and organizations of SSE have become an effective response to unemployment, a means to access the labor market and a good factor for job and wealth creation, as well as promoting creative projects linked to the world work. All this makes that companies and organizations of SSE be essential parts for the construction of society and emerge as a different way to build wealth from an economic activity that responds the assessment of the person above capital.

Regarding the type of organizations and companies of SSE, it is necessary to include those that produce goods and services and that have social objectives and often environmental objectives, and are guided by principles and practices of cooperation, solidarity, ethics and democratic self-management (TFSSE, 2014). They include, for example, cooperatives, mutual associations, NGOs engaged in income generating activities, women’s self-help groups, community forestry and other organizations, associations of informal sector workers, social enterprise and fair trade organizations (Utting, 2013).

On the other hand, Kawano (2013) considers the following five points as the most significant of SSE's concepts:

  • 1.

    Social Solidarity Economy is an alternative that allows ordinary people to play an active role in shaping their economic lives.

  • 2.

    Social Solidarity Economy is an ethical and values-based approach to economic development that prioritizes the welfare of people over profits and blind growth.

  • 3.

    Self-management and collective ownership in the workplace and in the community is central to the solidarity economy. There are many different expressions of self-management and collective ownership including: cooperatives, community-owned enterprises and the commons.

  • 4.

    The solidarity economy has a focus on the empowerment of women and other marginalized groups, as well as engaging in anti-poverty and social inclusion work.

  • 5.

    In the SSE there is great potential to build alliances and mutually supportive collaborations.

The Catholic Church has a long tradition worried by the Social Economy. It is known that began with the Encyclical “Rerum Novarum” of Leo XIII promulgated in 1891. More recently, the Pope John Paul II, in his Encyclical “Laborem Exercens” introduces the distinction between progress and development. The Pope affirms that the real development cannot limit to the multiplication of the goods and services, but it must contribute to the fullness of the human being. In 2013 the Pope Francis, in speech in the CELAM, pleads for a more human economy and appeals to the generosity and to the contribution according to the possibility of every person.

With regard to Big Data, the managing and analyzing data has always offered the greatest benefits and challenges for all organizations of all sizes and across all industries. Businesses have long struggled with finding a pragmatic approach for capturing information about customers, products, and services (Hurwitz et al. 2013). This year 2015 will be identified as the year that companies and organizations assume that every decision must be derived from data analysis. Companies will leave instinctive decision-making, since will be recorded and analyzed data of all movements. Data analysis will be no longer only the language of IT professionals, but throughout the business world, especially in the area of more advanced knowledge. Fast and accurate data collection will become an asset for organizations to meet their goals. Only with better data and more viewpoints organizations will be able to succeed and survive (Dufour, 2015).

Key Terms in this Chapter

CELAM (Consejo Episcopal Latinoamericano): Organization that groups the bishops of the Catholic Church of Latin America and the Caribbean.

Database: It is an organized collection of data structured on a computer system. The data are typically organized to model aspects of reality in a way that supports processes requiring information.

Distributed System: Multiple computers, communicating through a network, used to solve a common computational problem. The problem is divided into multiple tasks, each of which is solved by one or more computers working in parallel.

NoSQL (Not Only SQL): A set of technologies that provide a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. One major difference is that SQL is not used as the primary query language. These database management systems are also designed for distributed data stores.

In-Memory Database: Whereas in other databases the information is structured, managed processed in disks, in In-memory data bases is memory.

Social Solidarity Economy: An economy different from the market economy, where profit is not a goal. In Social and Solidarity Economy people associate in informal or formal groups and have common goals, these goals are neither centered in profit nor in individualistic needs.

Non-Relational Database: A database that does not store data in tables (rows and columns).

Hadoop: An open source software framework for processing huge datasets on certain kinds of problems on a distributed system. Its development was inspired by Google’s MapReduce and Google File System. It was originally developed at Yahoo! and is now managed as a project of the Apache Software Foundation.

Networks: A set of social units and direct or indirect relations, with a common goal.

Real-Time: Form of processing in which a computer system accepts and updates data at the same time.

Metadata: Structured data that describes resources. This data explains, locates or makes it easy to recover, use and manage the described resources.

Web Based Information Systems: Information systems based on the web technology.

RFID (Radio Frequency Identification): It is the wireless use of electromagnetic fields to transfer data, for the purposes of automatically identifying and tracking tags attached to objects. The tags contain electronically stored information. Some tags are powered by electromagnetic induction from magnetic fields produced near the reader.

NGO: A non-governmental organization (NGO) is an organization that is neither a part of a government nor a conventional for-profit business.

SQL: Originally an acronym for structured query language, SQL is a computer language designed for managing data in relational databases. This technique includes the ability to insert, query, update, and delete data, as well as manage data schema (database structures) and control access to data in the database.

Data Mining: A set of techniques to extract patterns from large datasets by combining methods from statistics and machine learning with database management.

Extract, Transform, and Load (ETL).: Software tools used to extract data from outside sources, transform them to fit operational needs, and load them into a database or data warehouse.

Semi-Structured Data: Data that do not conform to fixed fields but contain tags and other markers to separate data elements. Contrast with structured data and unstructured data.

Visualization: Technologies used for creating images, diagrams, or animations to communicate a message that are often used to synthesize the results of big data analyses.

Data Warehouse: Specialized database optimized for reporting, often used for storing large amounts of structured data. Data is uploaded using ETL (extract, transform, and load) tools from operational data stores, and reports are often generated using business intelligence tools.

Data Marts: A subset of a data warehouse extracted and designed to focus on a specific set of business information.

Open Source: A movement in the software industry that makes programs available along with the source code used to create them so that others can inspect and modify how programs work. Changes to source code are shared with the community at large.

Structured Data: Data that reside in fixed fields. Examples of structured data include relational databases or data in spreadsheets. Contrast with semi-structured data and unstructured data.

Relational Database: A database made up of a collection of tables (relations) that are stored in rows and columns. Relational database management systems (RDBMS) store a type of structured data. SQL is the most widely used language for managing relational databases.

Unstructured Data: Data that do not reside in fixed fields. Examples include free-form text (e.g., books, articles, body of e-mail messages), untagged audio, image and video data. Contrast with structured data and semi-structured data.

Community: A formal or informal association of people that share the same goals and visions of a certain dimension of their lives.

Columnar Database: Whereas relational database stores data in rows, columnar data base stores data in columns.

Business Intelligence (BI): A type of application software designed to report, analyze, and present data. BI tools are often used to read data that have been previously stored in a data warehouse or data mart.

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