Selling The American People: Data, Technology, And The Calculated Transformation Of Advertising

This dissertation tells the history of a future imagined by advertisers as they interpreted and constructed the affordances of digital information technologies. It looks at how related efforts to predict and influence consumer habits and to package and sell audience attention helped orchestrate the marriage of behavioral science and big-data analytics that defines digital marketing today. My research shows how advertising and commercial media industries rebuilt their information infrastructures around electronic data processing, networked computing, and elaborate forms of quantitative analysis, beginning in the 1950s. Advertisers, agencies, and media companies accommodated their activities to increasingly calculated ways of thinking about consumers and audiences, and to more statistical and computational forms of judgement. Responding to existing priorities and challenges, and to perceived opportunities to move closer to underlying ambitions, a variety of actors envisioned the future of marketing and media through a set of possibilities that became central to the commercialization of digital communications. People involved in the television business today use the term “advanced advertising” to describe a set of abilities at the heart of internet and mobile marketing: programmability (automation), addressability (personalization), shoppability (interactive commerce), and accountability (measurement and analytics). In contrast to the perception that these are unique elements of a “new” digital media environment that emerged in the mid-1990s, I find that these themes appear conspicuously in designs for using and shaping information technologies over the course of the past six decades. I use these potential abilities as entry points for analyzing a broader shift in advertising and commercial media that began well before the popular arrival of the internet. Across the second half of the twentieth century, the advertising industry, a major cultural and economic institution, was reconstructed around the goal of expanding its abilities to account for and calculate more of social and personal life. This transformation sits at an intersection where the processing of data, the processing of commerce, and the processing of culture collide

Situating Data

Taking up the challenges of the datafication of culture, as well as of the scholarship of cultural inquiry itself, this collection contributes to the critical debate about data and algorithms. How can we understand the quality and significance of current socio-technical transformations that result from datafication and algorithmization? How can we explore the changing conditions and contours for living within such new and changing frameworks? How can, or should we, think and act within, but also in response to these conditions? This collection brings together various perspectives on the datafication and algorithmization of culture from debates and disciplines within the field of cultural inquiry, specifically (new) media studies, game studies, urban studies, screen studies, and gender and postcolonial studies. It proposes conceptual and methodological directions for exploring where, when, and how data and algorithms (re)shape cultural practices, create (in)justice, and (co)produce knowledge

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse