Chapter 1 – Introduction to Data Analysis and Management
Learning Objectives
- To understand advantages and limitations of data-driven approaches to management and policy
- To understand how data and models relate to organizational decision making
- To reflect on the importance of data to produce knowledge about policy and management
In this first chapter of the Data Section of the book, we start by reflecting on the importance of developing data science skills in pulic management and policy. In general, attention to data science is part of a more general trend on data-driven decision and policy making. In this sense, although most content relates to technical aspects, we are also interested in the social and organizational aspects associated with managing the avalanche of government data and using it for decision and policy making. The technical part involves using analytic tools and Information Technologies (IT) such as big data, open data, analytics, decision models, statistics, social media, or simulation models. The social part involves an understanding of the problem as well as being able to work with diverse teams, understand stakeholders and their goals, understanding social processes involved in making choices, communicating results, etc. In most (if not all) cases, different stakeholders will have different perspectives on what is desirable, and what are best ways of reaching that desirable state. Finding a policy alternative, in this way, goes beyond finding an acceptable technical solution, but also involves working with policy makers and other stakeholders in developing the solution, communicating findings in a way that resonates with them and with results that they can use.
Dealing with both technical and social components of data science require the development of certain traits that include Dialogue, Creativity, Team Work, Curiosity, Attention to Detail, and Critical Thinking. Curiosity, Attention to Detail and Critical Thinking are directly related to modeling work and working with data. These traits will be much evident as you work with larger datasets like the ones included in the tutorials. Dialogue and Teamwork are both needed to complete the task and communicate findings to multiple stakeholders with varying levels of information needs and technical skills.
Attribution
By Luis F. Luna-Reyes and Erika G. Martin, and licensed under CC BY-NC-SA 4.0.
