Our society associates big data with digital technology and computers as the technological identifiers of this field. It is true that we use digital technology to compile, manage, store, manipulate, and analyze large datasets in a fast, efficient manner. However throughout human civilization, big data has been collected and various tools were created to facilitate the same functionalities. From tracking a harvest’s inventory to allowing multiple users to upload their own content’s data, big data is a multifaceted area in which humans have utilized innovation to work with these large volumes before modern technology came onto the scene.
As I compiled research for this design, there were two visualizations that inspired my timeline’s overall question: the Ishango Bone and IBM 5100. These historical images unsurface similar principles (i.e. user accessibility, process documentation, technological innovation, etc.) towards data collection and highlight that big data collection was needed prior to computers as well as practiced throughout time.
For example, anthropologists theorize the Ishango Bone was created to track inventory of trading activity and farming harvests to forecast future survival for its associated community. While these earlier communities aren’t dealing with terabytes of information, this logistical task was a huge undertaking that would require large amounts of data to be collected, then analyzed by the people involved. It is extraordinary to see how this task was achieved and documented for such a large operation.
As for the IBM 5100, the age of computers was instrumental in processing and manipulating data for analysis, but the portable computer opened up the physical and societal accessibility of these purposes. A user no longer needed to travel for the digital accessibility of records or use other materials to track their data—the computer was now their available sidekick to perform all those tasks without the hassle of travel or timing.
To construct this chronological visualization, I used TimelineJS, an “open-source tool that enables anyone to build visually rich, interactive timelines.” This tool is accessible to beginners via their easy, fill-in-the-blank Google spreadsheet and it is also customizable through JSON for more experienced users.
For my research, I primarily relied on Google’s search engine to read, review, and locate the research used for this project. I entered several keywords from the big data field (i.e. data center, big data, supercomputer, etc.) into the search engine and its results delivered all the articles in my References section.
Since I was a new user to TimelineJS, I primarily built my visualization using their Google spreadsheet template and following their step-by-step guide to fill in the necessary cells with my written content.
Then I experimented with their customizable, visual features, such as changing an event’s background image, using multimedia (i.e. video, gif, etc.) as visuals for events, and using different colors for the written content’s typography.
Once the visualization was built, my peer reviewed the timeline via a mini-user experience study and provided feedback on how I can improve the timeline’s visual and contextual experience. Finally I updated the timeline with my peer’s feedback to build and release the final version on this live WordPress site.
Most individuals underestimate the innovativeness and brilliant functionalities of past data collection tools. Mistakenly some may think big data wasn’t easily achieved nor the data analysis process was facilitated without the advent of digital technology; this brief history illustrates how these assumptions can be untrue and how one needs to step back to appreciate the design of previous machines. These machines served a human need, whether on the analytical, operational, or managerial side of big data, and their existence had a greater impact that may not be recognized at first. Also these innovations have paved the way for our current technologies and their weaknesses opened the inventive space for better, efficient creations to expand our big data thinking into what it is today.