Data science for social work management: Tools and skills from art and science Oscar E. Cariceo, MSW, NSWM Chile Chapter Murali D. Nair, PhD, Clinical Professor, USC Suzanne Dworak-Peck School of Social Work Network of Social Work Management National Conference. “The Business of Social Work: Mission, Morals, Morale and Money” Fordham University, New York. June 15, 2017

Data science for social work management Data Science is a research tool that allows the exploration and quantitative analysis of a wide range of available data. Both structured and unstructured, in order to develop understanding, extract knowledge, and formulate actionable results.

Keys elements for social work management Decision making actions. Data science is “highly iterative and non- linear process, better reflected by a series of epicycles” (Peng & Matsui, 2016, p.4). Information is learned in each step. Then, it is possible to refine, refresh the action that was just performed or proceed to the next step.

Data science for social work management Connection among fields Knowledge and applications Data exploration Data analysis.

Technical steps for data science Finding data Acquiring those data Cleaning and transforming them Understanding general relationships in data Delivering value from data.

Data science for social work management Time sensitive and scientific approach. Prescriptive interventions. .

Keys elements for social work management Knowledge Art of communication Intuition to decide about critical social conditions

Processes for social work management and data science . Match expectations Data availability Develop expectations Collect data.

Machine learning techniques for data science Its goal is to “teach” computers through “examples”. Predict the future. “Train” a data set Crisis Text Line project, emails.

Machine learning techniques for social research Example, epidemiology. how machine learning techniques can improve social interventions on complex social issues?

Big data and evidence-based macro practice Information is produced by individuals. Huge amount of personal and collective data. Opportunity to address complex social problems.

Big data and evidence-based macro practice Social media and the usage of the Internet. Improving decision-making Reach permanent social change.

Ethical dilemmas and social work challenges on data science Privacy Access Open Data movement Closed to shared

Conclusions Data an asset for human services agencies. Real-time can resources. Crowdsourcing.