The TEAL model represents a break from classical internal organizational models of entities, with a special emphasis on the people who are part of them. What benefits could its implementation have in the social sector?

One area where we can apply innovation in the social sector is in the internal organizational model of entities and organizations. Frédéric Laloux (Belgium, 1973) published the book Reinventing Organizations in 2014, where he presents the TEAL model, a new perspective on business organization that breaks with classical models and aims to put people at the center of organizations. Below, we will explore what the TEAL model is and what benefits it can bring to the social sector.

Organizational Paradigms: Models Prior to TEAL

To explain what the TEAL model entails, we need to understand where we start. Laloux traces the evolution of organizational models and identifies different types according to their characteristics and advancements, each associated with a different color. From oldest to most recent, there are five:

a. Red. This stage is often associated with environments where strong direction and control are necessary to maintain order, and where power dynamics are clear and manifest. It can be observed in authoritarian regimes, religious sects, or highly competitive sports teams.

b. Amber. In this case, roles are well defined and stable, although they allow some movement on the scale. The organization is structured around a hierarchical pyramid, and control is exercised from top to bottom. The perspective is long-term with stable processes that usually repeat past patterns. Examples include the military, the Catholic Church, and governmental organizations.

c. Orange. This approach introduces innovation for the first time, incorporates accountability, and is based on merits. The main objective is to overcome competition and achieve profits and growth. Management is done through objectives that require control and prediction. Banks and many companies follow this organizational model.

d. Green. Culture and empowerment are at the center of this model, with the aim of boosting employee motivation. The organization’s purpose is articulated around interest groups, not just shareholders. A current example is non-profit organizations.

e. Teal. The last type, and the most recent, replaces the hierarchical pyramid with self-management. Organizations function as “living” entities oriented towards fulfilling their purpose. Thus, they are based on three principles that we will examine below: self-management, wholeness, and evolutionary purpose. Some organizations around the world are already beginning to implement this model.

The TEAL Model: Self-Management, Wholeness, and Evolutionary Purpose

As we have mentioned, the TEAL model is based on three key pillars that serve as a guide for organizations wishing to adopt this system:

a. Self-Management. This trait represents a break with the hierarchy that characterizes most organizations. The main objective is to replace traditional methods based on vertical communication and hierarchical structures with autonomous work groups. This does not mean that organigrams with differentiated positions disappear, but when working, decisions are made without hierarchies. In this way, everyone feels more involved in the goals and strategies. Through regular or spontaneous meetings, different decisions made by each work group are shared. These groups are also flexible and adapt to the needs of projects and contexts. Transparency, conflict management, and a sense of belonging are some characteristics of this self-management.

b. Wholeness. According to Laloux, companies require their workers to hide feelings of doubt or vulnerability and instead always show determination and strength through a “professional mask.” This approach questions the classic barrier between personal and professional life with the aim of creating spaces where people can express themselves fully and thus develop their full potential. It is key at this point to ensure that workers feel useful and have opportunities for fulfillment.

c. Evolutionary Purpose. The third pillar of the TEAL model is based on conceiving the organization as an organism that has its own life and sense of direction. Therefore, instead of trying to predict and control the future, the focus is on listening to its purpose within society. The goal is for the organization’s mission, values, and vision not to be based on the leaders’ interests, but to be aspects shared by all members. Additionally, this perspective also seeks to ensure that entities do not focus on rivalry with other organizations but place their purpose at the center.

The TEAL Model and the Social Sector

The TEAL model is particularly interesting and innovative in the social sector, given its community-oriented nature. However, there is not just one way to apply the model, as it will also depend on the uniqueness of each entity. Some of the benefits we can find are:

a. Increased Autonomy. Self-management allows decisions to be made more efficiently and quickly, as the people working on each project have more autonomy in their area of responsibility. This way, the organization is more flexible to changes and situations that require a quick response. This is useful in the field of social services and direct user care.

b. Motivation and Engagement. Sharing a vision and working towards the same purpose fosters commitment from all members of the organization, regardless of their area, and increases motivation and a sense of belonging.

c. Greater Initiative. Fostering a comfortable and participatory space helps reduce the fear of failure and increases self-confidence. Thus, workers show greater initiative in proposing ideas and making decisions.

d. Fostering Creativity. In relation to the previous point, the greater initiative and involvement of professionals encourages a more fluid exchange of ideas. Consequently, it creates an optimal space for the emergence of creativity.

e. Responsibility. Flattening the organizational chart allows everyone to make decisions. Thus, this autonomy increases the sense of responsibility in responding to the agreed-upon agreements.

In conclusion, the application of the TEAL model can generate numerous advantages in the social sector thanks to its emphasis on self-management, wholeness, and evolutionary purpose. On one hand, autonomy and flexibility in decision-making improve efficiency and adaptability in providing social services. On the other hand, creating a comfortable and participatory space fosters motivation, initiative, and creativity among workers, while increasing their sense of responsibility. All these characteristics make the TEAL model particularly interesting for social sector organizations looking to innovate and improve their internal structure to offer better services to the community.

References

Amurrio Gónzalez de Langarica, Í. (2019). Nuevos modelos organizativos de los recursos humanos en la empresa. Addi. https://addi.ehu.es/handle/10810/37110

Clos, I. (2020, 10 desembre). Organizaciones Teal: 50 prácticas revolucionarias que cambiarán tu futuro. Sociedad de la Innovación. https://www.sociedaddelainnovacion.es/50-practicas-organizativas-revolucionarias-cambiaran-futuro-organizacion-basado-modelo-teal/

De Hijas, P. M. (2023, 19 agost). Laloux, las organizaciones TEAL. Emergentes. https://emergentes.org/laloux-las-organizaciones-teal/

Great Place to Work. (2023, 25 abril). Organizaciones TEAL ¿Qué son? ¿Qué ventajas tienen?. Great Place To Work Spain. https://greatplacetowork.es/organizaciones-teal/

Organizaciones TEAL: ¿cómo reinventan la gestión y cuáles son sus ventajas? (s. f.). Iberdrola. https://www.iberdrola.com/talento/organizaciones-teal

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Ortiz de Zárate Hors, María Eugenia (2019). Modelos organizativos evolutivos – Teal para startup social: estudios de caso. Tesis (Master), E.T.S.I. Industriales (UPM). https://oa.upm.es/65570/

Pablo, S. A. J. (2022, 10 març). Implementación del modelo Teal evolutivo para el desarrollo de la felicidad organizacional. https://repository.unimilitar.edu.co/handle/10654/43633

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In recent years, multiple sectors have embraced big data technology and predictive models to manage large amounts of data, draw conclusions, and make predictions. How can this technology be applied to social services?

Every day, the world generates approximately 328.77 million terabytes of data, according to a Market Splash analysis from 2024. In recent years, the growth rate has increased exponentially. From 2010 to 2023, for example, the amount of data generated has increased in volume by 60 times, although only 10% of these data are unique, as the rest are duplicated information on the network.

In this context, most sectors are incorporating intelligent data analysis services to manage data, draw conclusions, and make predictions. Social services are no exception. In recent years, the sector has embraced big data technology to collect information in the socio-health field and thus be able to act preventively, personally, and efficiently. Next, we will explore what big data and predictive models are, what opportunities they present for social services, and what innovative initiatives are already being carried out.

What is big data?

Broadly speaking, we could define big data as a set of data that, due to its large volume, cannot be processed or managed with traditional integration databases. More specifically, we find that big data is based on the paradigm of the 3 V’s:

• Volume. A large amount of data is worked with, requiring new storage techniques and new approaches.
• Variety. The data comes from various sources and formats, such as messages, videos, images, or emoticons. Therefore, tools that can decipher all types of content are needed.
• Velocity. The large volume of data requires that it be processed and analyzed quickly to prevent the results from becoming outdated once deciphered.

According to the report Big Data and its impact on the public sector, coordinated by Chema Maroto, four more characteristics can currently be added:

• Variability. This trait refers to data that constantly changes its meaning, something particularly applicable in the case of language, as words do not have static meanings and require context to be understood. This point is one of the main challenges of big data and demands the design of processing programs capable of understanding each context to decode the precise meaning of each word.
• Veracity. The results obtained when processing large amounts of data are only useful if the data being worked with is accurate and truthful. Therefore, it is crucial to keep in mind that sometimes data can be presented in a disordered manner, as well as to know how to distinguish errors and excess noise, that is, all those false or irrelevant information. To carry out this task, it is necessary to accompany the algorithms with professional human supervision.
• Visualization. Another important challenge of big data is the ability to visually represent large amounts of data obtained. Traditional data representation methods are no longer sufficient; instead, it is necessary to find simple and accessible ways to visualize information.
• Value. Big data provides added value in the different areas where it is applied. For example, in the case of social services, it can mean economic and time savings in user care.

What are predictive models?

One of the applications of intelligent data analysis is the creation of predictive models. In the case of social services, these models can help professionals make better decisions.

A predictive model is a type of mathematical model that allows making predictions in the future based on past data. Through big data technology and machine learning, it is possible to infer how a variable will behave in the future based on other known variables.

To implement a predictive model, data must first be prepared to minimize noise and analyze it. Next, the predictive model to be used and the problem to be solved through it are selected. Then, the model is trained and validated to reach its implementation, which will be accompanied by monitoring to identify possible errors and improvements. Ensuring the quality of the data entered into the system is essential, as the reliability of the results obtained from the model will depend on it.

Big data predictive models and social services

Big data technology, in combination with other tools such as artificial intelligence and machine learning, allows analyzing social services system data to look for patterns (repetitions, anomalies, or correlations) that are usually too complex to detect manually. Along with other data sources, this can allow us, for example, to anticipate the emergence and evolution of population needs. In this sense, predictive models can help advance social services in four important areas: prevention, personalization, planning, and efficiency.

According to Big Data: What are the Implications for Public Sector Policy in Society 5.0 Era? (Fajar Rahmanto et al.), the combination of big data and artificial intelligence allows obtaining information that can become a very good starting point in decision-making and improving automation systems.

However, it is essential to define good security policies when dealing with personal and confidential information in the socio-health field, as the data being worked with is very sensitive and intimate.

Initiatives

Currently, the social sector is carrying out numerous initiatives that use big data to make predictions and act preventively. Below, we summarize some initiatives that work in this direction:

• PACT Project. In Castilla y León, the PACT project uses an algorithm developed from big data and machine learning techniques capable of predicting the risk of social exclusion of social services users. Through ten main risk factors, the predictive model can determine which of the people requesting the minimum guaranteed income will continue to need this benefit for 60 months or more. Through a mobile application, professionals have access to these forecasts and can make better decisions.
• Predictive and descriptive tool. In Catalonia, the Association for Welfare and Development (ABD), in collaboration with Momentum Analytics, has developed a predictive tool based on big data and artificial intelligence that allows describing the socio-demographic and economic characteristics of the families in the territory with the aim of helping municipal social services better understand current real needs, predict future ones, dimension the service portfolio, and plan resources.
• One View. In the United Kingdom, the One View system analyzes the set of data related to services for adults, children, and homeless people to identify existing risk situations that could lead to homelessness in the near future. The analytical model includes income, benefits, and school data, obtaining a general and complete view of citizens and households. The model has a preventive approach and can detect future risk situations 6 to 9 months before they occur.
• Crisis Text Line. This American initiative uses machine learning and artificial intelligence to predict high-risk suicide or self-harm situations. The system is based on the analysis of 30 million messages that users have sent through a prevention hotline and is capable of identifying which cases are more urgent. Thus, 94% of high-risk people receive an intervention in less than 5 minutes.
• L’Allegheny Family Screening Tool (AFST). Also in the United States, this big data system provides social services professionals with an objective assessment of child neglect risk situations through more than a hundred parameters. When a case is received, the algorithm calculates the risk index to guide professional action.

In conclusion, big data and predictive models in social services represent an interesting tool to improve efficiency, personalization, and prevention in population care. The ability to process and analyze large amounts of data quickly and accurately, as well as generate predictive models, allows professionals to anticipate needs, identify risk situations, and make decisions based on solid evidence. Initiatives such as the PACT project in Castilla y León, the predictive tool of ABD in Catalonia, and the One View system in the United Kingdom demonstrate the positive impact of these technologies in the socio-health field. However, it is essential to implement robust security policies to protect the privacy of confidential data and ensure its veracity. Ultimately, big data and predictive models are key to a future where social services are more proactive and effective in their mission of supporting communities and individuals.

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