Systems Thinking and Deep Data Literacy
Many roles in public and nonprofit organizations require staff to become sophisticated consumers, analysts and presenters of data. But before data can be used, it must first be specified and collected—and that is increasingly done via information systems.
This upstream area is rife with complex problems. Data may seem hidden from stakeholders as if it were in a black box. Communication between non-technological staff and information system developers is often fraught. Data and information systems are frequently an arena of contention among different stakeholder groups including executive leadership, front-line workers and their supervisors, measurers of performance, evaluators, and funders. And information system projects are inherently risky.
This course will provide students with a deep level of literacy about upstream data so that they can be more effective stakeholders in information systems. The course teaches practical techniques for querying databases and for understanding the implications of the data architecture that underlies an information system.
The majority of the course focuses on developing a practical understanding of relational databases. Students will learn the essential elements of Structured Query Language (SQL) and practice writing basic scripts. They will learn how to read a data model and how to reverse engineer one from an existing database. They will also learn to consider the practical implications of definitions and taxonomies embedded in databases.
The course also addresses challenges that organizations face with procuring information systems. Students will become familiar with the tiered structure of information systems; the impact of data architecture on labor and financial cost; stages of information system projects; and factors that contribute to success or failure.
The course includes readings from the systems thinking traditions which are helpful for understanding the diverse ways of construing the boundaries and nature of the organizational environment; for understanding the virtues, limitations and pitfalls of common approaches to information system development; and for designing more effective, holistic and evolvable information systems.