BlogThe Election, Big Data and What It Teaches Us About Communications Management

The Election, Big Data and What It Teaches Us About Communications Management

November 10, 2016, Communications Lifecycle Management

The votes are in. We have a new president-elect. However, many have been shocked by the outcome.  After all, almost all major polling organizations had Clinton set to win going into November 8th. And if you listened to any number of the major media outlets, they had Clinton picked to win as well. In fact, the only person saying Trump was going to win was … well, the Trump campaign.

So what happened? And was Trump’s optimism based on more than hopeful wishes, or did he actually see what nobody else could?

This 2016 election marks one of the most sophisticated campaign runs in history. Leveraging big data analytics, candidates are now able to use massive amounts of data, complex models and advanced algorithms to determine the best way to identify opportunities that offer the most appeal to the electorate.

From the beginning, the Clinton campaign hit the ground running – with her own in-house data team using big data. After all, the Democrats had successfully adopted the practice in the Obama 2008 campaign, and they had a head start on how to leverage the massive amounts of available information.

On the flip side, Trump rejected the need for big data initially. However, after winning the nomination, he hired British firm Cambridge Analytica to help drive his data, digital and technology team.

According to Wired.com, Matt Oczkowski, director of product for the president-elect’s data team at Cambridge Analytica, knew weeks ago that Trump had a solid shot at the presidency. He’s quoted as saying “This is not something that political intuition would tell you, but our models predicted most of these states correctly.”

So what did Trump’s team do differently than the major polling/analyst models like Nate Silver’s FiveThirtyEight model, The New York Times’ Upshot model and the Clinton campaign’s own public projections?

According to Oczkowski, “We came to realize the way folks were polling in terms of their samples and who they consider likely voters, it’s probably been incorrect.” They noticed this trend when the first early votes started pouring in. The Trump team then completely re-worked their models, interpreting the data differently than everybody else.

As a result, they could see what others didn’t. Trump’s team realized that the legacy way of interpreting the masses of data was flawed. It was based on a historic way of looking at voting patterns and behaviors. So while everybody else in the world was using these older models, Trump’s team flipped the models, upending nearly every traditional poll across the US and abroad.

So what does this have to do with Communications Management?

Much like we saw in the differences between modeling behavior data for this election, communications data has the potential to be interpreted any number of ways. Anyone can pop raw data into a spreadsheet and start to try making sense of it.  Some even have access to the same sophisticated tools used during the election. However, the real difference can be broken out into a few things:

  1. Understanding how to securely navigate access to the right set of data
  2. Applying the right interactive tools to enable guided intuitive exploration and analysis
  3. Using the right inter-related presentation models that drive the right actionable insights

If there’s a flaw in any one of those things, you’re likely to be trapped in a world where you think you’re correct, but most certainly are not.

Calero believes so strongly in the idea of using the right data, tools and models, that we’ve spent the past years honing the analytic capabilities natively embedded within our Communications Lifecycle Management applications. Once you can connect the data and the tools, the real trick is then creating the integrated data models and interactive guided dashboards to synthesize that data into a view that provides a correct and consistent interpretation of the data. With over thirty years of working with communications data, we’ve been able to highly accelerate the development of various models to help guide the decision making process, while not confining the exploration of data.

 Looking for ways to improve analytics within your TEM solution?  Download 7 Questions to Ask Your TEM Vendor about Analytics.

This election has been an interesting one to say the least. Inclusive of what we saw with the use of big data analytics, there was a lot we all learned about ourselves and the people that serve our country. Whether you’re a Clinton or Trump supporter no longer matters. Now we move forward together as one United States.