Authors: Zachary Kaiser and Gabriel Schaffzin
“Growth” is endemic to our economic system. Economies of scale, long-held as critical means to efficiency and profitability, have taken on new meanings: whereas they once relied on the skills of the mechanical engineer to realize wide-scaling manufacturing capabilities, our cultural shift towards the individual means a focus placed on customization of both product research and design. In the age of networked information and networked publics—built on the “solutions” of large, multinational companies like Cisco and EMC, as well as small, connected product and service startups— this paradoxical emphasis on broad ranging customization is resolved through the automated tools at our disposal today: platforms, data, and algorithms, to name a few. Throughout the decades, however, scalability and growth has always been perceived as essential by investors and the public alike.
As commercial practitioners, we are often tasked with the design and development of projects meant to reach a large audience—at the time of release or further down the line. For instance, a website for a large NGO must scale in terms of both content and reach, accommodating a broad swath of information types for a global audience. Increasingly, as the integration of social streams and other “open” sources of content becomes valued by clients, the access of publicly available APIs requires an accommodation of the parameters set by those sources—often multi-billion dollar corporations.
To develop designs and technologies that scale, we build on or develop our own platforms. These platforms include those with which we are familiar and interact every day, such as YouTube (and its API), Twitter, and various content management systems such as SiteCore or WordPress. The way in which we are able to design for scale today is enabled by our ability to capture a tremendous (and often overwhelming) amount of data. “Big Data” has become common parlance. We use the data that we capture to make inferences about the users for whom we design, giving us the ability to scale solutions across geographies, demographics, and markets. Algorithms are pervasive in today’s experience of designing at scale, especially as the time and cognition required to process the volume of information with which we interact increases. Once the sources of our data and content are identified, in order to present that information back to our end-user in a means unique to our project, we must process it. Infusing this data with value requires moving it through algorithms—ones that aggregate, analyze, modify, and more.