Data Science for social work practice
Data Science is a tool for discovering or gaining knowledge from structured and unstructured data by exploring and quantitatively analysing the available data. The knowledge can further be used to make evidence-based decisions. A vast amount of psychological, social, and organizational data is getting produced every day as a by-product of human activity in the digital world. This data is popularly known as ‘Big Data’ and this can be used to understand and solve society's most difficult problems. The advancement in the field of data science and technology has made it possible to manage, and analyse this data for the greater good.
Data Science in Social Sector
Many sectors especially the business sector have benefitted themselves by advancing themselves along with the digital revolution and using the data for understanding trends and consumer behaviours and needs, whereas the social sector is still lagging behind by not integrating itself fully with the effectiveness of big data and data science.
In social work practice, data science can be used not only for making important decisions but also for innovating communication for example graphs, charts, and other visualizations can be used to talk about several issues and the related causes and effects. These innovative communication skills can be further used for raising awareness and collecting funds for improving different social conditions.
Data Science Approaches
Prediction using data models, especially machine learning models, can greatly support the epicycles of analysis. The machine learning algorithm uses the data to find the patterns within it and comes up with rules and solutions for different problem specifications. It helps in understanding the correctness of the hypothesis. Also, machine learning models teach themselves to improve their performances with experience, which can effectively make faster and better decisions for new data if we already have a good model for the problem.
The availability of a large amount of data can be used to understand the needs and necessities of people in different regions of the world. Government policies can be made using the data-driven solutions provided by the machine learning models, which makes the policies effective as they are made using strong mathematical and logical evidence. The digitalisation of the world is producing a large amount of data every day, and performing sentiment analysis can reduce the cost and effort of surveys to make any important decisions and policies.
Related Work
For example, databank.worldbank.org contains various time series data on a variety of social topics, and analysing these data can be helpful in understanding different problems of different regions of the world. I remember, an interesting project I worked on, which was about the analysing life expectancy and understanding the dependency of the life expectancy of a country on different factors.
The Chicago Police Department’s work on creating a ‘heat-list’ of young people at risk of becoming involved in gun violence, which is a big example of utilizing data science advancement for solving social issues. According to the publication, “the heat list utilises an algorithm to assign a risk level to a person based on criminal past.” “The risk that you will be involved in gun violence increases with your score. High-scoring users get a “custom notification.” This entails a home visit from the police, a social worker, and a prominent local citizen, like a preacher or sports coach. They then provide a reference to social services in case the client wants assistance in making a turn for the better in his or her life.
Obstacles
There are concerns such as data security and data ownership which need to be taken care of while working with Data. Also, the Curation of Big Data is tedious as big data is highly unstructured and it requires a good amount of time and effort to create an effective dataset from it. In addition to this, reliance on digital data can overlook big problems as there are a lot of people in the world who face major problems, but do not leave a significant digital footprint, especially older people and people with low levels of income and education.
Conclusion
Big Data and Data Science have the ability to transform the social sector by changing the way government agencies and non-profit organizations make decisions. Additionally, a successful social sector can quicken the pace at which new social processes and issues are discovered, leading to creative and novel solutions. As a result, the industry will become more accountable, transparent, and productive.
References
- Cariceo, O., Nair, M. and Lytton, J., 2018. Data science for social work practice. Methodological Innovations, 11(3), p.2059799118814392.
- Coulton, C.J., Goerge, R., Putnam-Hornstein, E. and de Haan, B., 2015. Harnessing big data for social good: A grand challenge for social work. Cleveland: American Academy of Social Work and Social Welfare, pp.1–20
- https://onlinemasters.ohio.edu/blog/how-is-big-data-helping-social-workers/
- https://www.weirfoulds.com/the-perils-of-prediction-lessons-for-regulators-in-the-age-of-big-data
