Working on a political campaign back in the day, I consistently found myself assigned to the phone banks. I would manually dial constituents who had voted in a specific way in the past, identify myself as a volunteer for the candidate that matched their preferences, and ask if we would be able to count on their vote in the upcoming election. If they responded in the affirmative, I would annotate their support on a card. If they were undecided, I would make a note to snail-mail them materials detailing my candidate’s position on the various issues that were the focus of that election cycle. If they responded negatively, I would record on their card that we had lost their support.
It was a very labor-intensive process whose freshly-collected data was instantly perishable, given that a voter’s opinion could shift without the candidate’s team ever becoming aware of it. And while manual phone polls only provide a snap-shot of how a voter might cast their ballot at that moment in time, election cycles can sometimes last up to two years. That’s why for most campaigns, constant contact with constituents is required for the candidate to understand if his or her perspective is in line with the people that he or she hope to represent.
Yes We Can (Use Big Data)
Every day, big data companies working for businesses of all sorts gather user information from suppliers across the web. These data suppliers are websites consumers visit to conduct personal business, connect on social media, read or comment on news articles, or simply watch video clips or movies.
Political parties can make use of the very same techniques that big data companies use to collect vital information and perform their own analytics to better predict outcomes in each election. Like marketers, political strategists should use big data to gather better insights about their constituents in a geographic area or among certain demographics. These insights allow them to more reliably predict how voters will respond, which in turn allows strategists to more precisely allocate limited resources towards regions or groups where the race is closest.
Big Iron: The Right Candidate for the Job
National political parties should leverage the power and capability of the mainframe to collect and analyze data from around the web. Doing this will help them to better gauge who their constituents really are and therefore predict with greater accuracy which way voters are leaning and how to influence their decisions every election.
Here’s an example of how this would work. Voters leave their sentiment as a digital footprint throughout the web as they log into websites to contribute content, and their IP address can be used to identify their general geographic location. Let’s be clear: no identifying information can be gathered from an IP address, which is merely an internet protocol that identifies one device on a network from another. However, knowing their general geographic location may be enough to categorize a constituent into political districts.
This is where the mainframe comes in. The mainframe can crunch sentiment analysis with data modeling using data from a broad spectrum of websites and return predictive behavior to more accurately provide insights into election outcomes. This isn’t rocket science; this is already happening today in other industries such as the insurance industry. And since the mainframe can process billions of instructions per second, it’s perfectly suited for collecting data across the net and processing it in time for the mid-term elections this November.
A Tool for Modern Governing
Politics is the science of human behavior – understanding it and organizing our society in a way that’s optimal for our well-being on the basis of that behavior. With that in mind, it’s clear that processing and analyzing massive heaps of data to help parties win elections is just one way the mainframe can be useful in politics. Like businesses who use big data to reduce risk, increase customer satisfaction and maximize efficiency, government agencies have not just an opportunity, but a duty to leverage the unprecedented technologies available to them to help serve the citizens to whom they answer. Regardless of your political affiliation, let’s agree that with its unparalleled speed and wide breadth of tools ideal for finding insights in big data, our vote should be with the mainframe.
Jennifer Nelson is senior director of software engineering and head of the Austin Center of Excellence at Rocket Software. After serving in the U.S. military, Jennifer attended the University of Texas while moonlighting as a Db2 database administrator at an IT company in Austin.