Data has transformed politics. When leveraged strategically, it has the power to fundamentally change how campaigns, PACs, and public advocacy groups operate. The core challenge is that there is so much data available—both quantitative and qualitative—that it is sometimes difficult to sift through. So, where does one begin?
While we can all acknowledge how data mining is useful in politics, it’s also safe to say that it is equally useless when you’re unsure how to interpret your findings. This article provides an overview of data mining and will show you how to create an actionable, straightforward data mining strategy.
At Aristotle, our team has been the foundational support for election campaigns and public affairs groups for nearly four decades. We are committed to being the leaders in political consulting, campaign management software, and political data. Our team of experts will make sure you have the tools, information, and resources to run a successful campaign. If you have any questions, don’t hesitate to contact us to learn more about Campaign Manager, the only three-in-one campaign management software.
What Is Data Mining?
According to TechTarget, data mining is “the process of sorting through large data sets to identify patterns and relationships.” In practice, data mining involves extracting, searching, and analyzing data to discover valuable patterns in trends and behavior. It exists at the intersection of statistics, machine learning, computer science, and database management.
In some ways, the term “data mining” is a bit of a misnomer. The raw data is already there before the data mining process starts. Data mining is not about extracting any particular piece of information (raw data). Instead, data mining is about constructing and discovering patterns and relationships that make the raw data meaningful and actionable.
Critical Data for Modern Elections, Campaigns, and Public Affairs
Political data is all the information—statistical or otherwise—related to campaigns, elections, political fundraising, or public policy. Some of the most notable examples of political data include:
- Census results;
- Demographic information;
- Voter registration statistics;
- Election results;
- Polling results;
- Political contribution history;
- Consumer information; and
- Academic research.
Data is foundational to the success of elections and public affairs. Unfortunately, campaigns and political advocacy without a system to collect and utilize raw data are likely to fall behind the competition. Luckily, a little preparation goes a long way for those ready to work and make the most of their campaign.
Data, Data Everywhere
There is far more data accessible and available now than ever before. As a reference point, researchers estimate that an average of 2.5 quintillion bytes of data is created every day. It is a truly staggering amount of information! Yet, frustratingly, most of it is not particularly useful to anybody—let alone to a political campaign, PAC, or public policy organization.
Here is the challenge: Candidates, campaigns, and other political groups need to know how to manage and use data. It is easy to lose the forest for the trees. Getting stuck working through all the individual data points could mean that big-picture patterns get lost in the mix. Data mining can make a difference. It is a systematic process to analyze and utilize data, and you can do it, too. Let’s get started.
5 Aspects of Data Mining
The core objective of data mining is to take a large dataset and extract patterns or relationships that help predict future behavior. Political campaigns, PACs, and political advocacy groups must use the proper approach when mining or analyzing large datasets. Here are five critical aspects of data mining that you need to know:
#1 Classification
One of the initial steps of organizing a database is classifying the data. Data classification helps ensure that data mining produces reliable and actionable analysis. For example, a political contribution database assessment may want to organize donors based on the political party they most frequently donate to.
#2 Clustering
In data mining, “clustering” refers to developing structures and groups within the dataset. Clustering ensures that data that is “similar” in some critical way is grouped together.
#3 Regression
Regression is a core part of data mining. As described by Investopedia, regression is a statistical method used to determine the strength of the relationship between certain variables. Regression analysis of political data is one of the most effective ways to find valuable patterns.
#4 Anomaly Detection
Not all data is perfect. For example, there may be severe outliers in a dataset. Alternatively, a dataset may have flaws that require closer inspection before a proper regression can be run. By detecting anomalies, data mining helps to make it easier to work with a dataset and put raw information into the appropriate context.
#5 Summarization
Finally, data mining requires reliable summarization. This final step provides an efficient, actionable representation of the dataset with relevant patterns/relationships discovered through the analysis. You may create or receive this information as a comprehensive report.
Beware of Data Fishing
Well before the term “data mining” ever entered the lexicon, statisticians and academics used the term “data fishing” as a pejorative to refer to the flawed practice of digging through large datasets to manipulate data and draw ineffable conclusions. The problem with “data fishing” is that there will always be “patterns” when a dataset is too large and unrefined. Many times, with data fishing, the conclusions drawn are mere coincidences.
In response to this flawed practice, data mining developed as a systematic and reliable approach to analyzing and organizing large datasets for patterns/relationships without being fooled by randomness. Still, the risk remains. A data mining strategy that is not well-honed risks producing results that appear significant or groundbreaking but are sadly random and irrelevant.
Data Analytics and Machine Learning
Data analytics and machine learning are closely related to data mining. Broadly speaking, data analytics is how we analyze and interpret raw data; machine learning is an advanced technology through which computers effectively “make decisions” based on harvested data.
Machine learning and related tools—such as algorithms and other statistical methods—can help you turn raw data and patterns into actionable insights. Often, machine learning can help you gain a new understanding or automate decision-making in cases where humans are unable to.
How to Use Data
Before the 2020 election, Reuters published a widely-circulated story that emphasized how fervently the most successful political campaigns and advocacy groups are using political data. A modern political data operation involves several different steps. Here are the big four you need to know:
#1 Collection
Campaigns need to collect and organize relevant data. Of course, this does not mean starting from scratch. Quite the contrary, Aristotle has national databases available. You can easily combine your existing data with our data.
#2 Cleaning
“Cleaning” data refers to sifting through your data to find information that is no longer relevant. For example, you could very well collect outdated information—like old voter addresses or a disconnected phone number. It is your job to remove data like this from your collection before you begin searching for insights. Or, simply work with a trusted partner to take care of your data cleaning. For example, at Aristotle, our MatchIT platform quickly updates lists that are outdated or missing critical data points.
#3 Mapping
Mapping layered data presents information in a visual, spatial format. As a visual aid, mapping helps contrast multiple datasets. Some specific things you can do with mapping include:
- Identify regional hotspots where you have the most support or underperforming areas where additional resources are necessary
- See the stronghold locations of your competitors and identify underserved regions as potential areas for outreach
- Examine the demographics of your base, identify other cities/towns with a similar makeup, and target your marketing accordingly
Work with the Leaders in Political Data
Aristotle is widely regarded as a leader in political data. As an award-winning data provider, we connect clients with the raw data and data analysis (data mining) tools they need to achieve big goals. Our core data includes a National Voter File with 235+ million registered voters, a National Consumer File with 250+ million consumers, and a regularly refreshed New Mover File that documents the 1+ million monthly moves in the United States.
We work closely with a wide range of clients; our political data customers include:
- Campaigns;
- Lawmakers;
- Political consulting firms;
- Private and public pollsters;
- Political Action Committees (PACs);
- Super PACs;
- Public affairs associations;
- Grassroots advocacy groups;
- Data analytics consultants;
- Nonprofit organizations;
- Trade associations;
- Academic organizations; and
- Media companies.
It’s no secret that political data can change the game. To learn more about our campaign management services, give us a call or message us directly online to schedule your free demo. We can’t wait to help you reach your campaign goals!
