Streamlining Your Reporting Workflow: From Data Collection to Visual Output

Are you drowning in data but starving for useful reports?

If you work for any organization that offers election technology solutions, chances are you feel this way every single day. Data is coming at you from every angle, and transforming it into actionable insights can seem like an impossible task.

That’s because…

When you rely on outdated reporting techniques, it takes forever to go from raw data all the way to something visually useful. Not only is it frustrating, but it’s also a huge waste of time that could be spent doing actual analysis.

Fortunately, there are better ways to streamline your reporting workflow.

With the right combination of best practices and helpful tools like automatic forecasting, you can shave hours off your data to visualize the pipeline.

Let’s walk through each step of the reporting process so you can learn how to maximize your workflow efficiency.

Reporting Workflow Breakdown

1. Why Reporting Workflows Are Important
2. Data Collection Stage
3. Data To Insights
4. Creating Useful Visual Outputs
5. Tools To Help You Speed Things Up

Why Reporting Workflows Are Important

Ok, let’s get one thing out of the way…

If you want to consistently make smart, data-driven decisions, you need an efficient reporting workflow. There’s no way around it.

Think of it like this — when all of your election related data starts coming in, it needs to be collected, cleaned, analyzed, and presented. If your reporting workflow is broken, all of those steps fall to the wayside.

Even worse…

Recent analysis shows that less than ten hours per week are dedicated to producing or working on a single visualization. When researchers broke this down even further, most respondents said they only spent two hours a week or less interacting with their visualizations.

If you’re spending more than two hours a week just collecting and preparing your data, you have a bottleneck somewhere. Streamlining your workflow doesn’t just help you save time — it allows you to spend more time analyzing your data.

With an efficient workflow, you can:

• Reduce errors
• Save time
• Improve accuracy

Simple enough, right? Let’s dive deeper into each step of the reporting process.

Data Collection Stage

The collection stage is the foundation of every reporting workflow.

When data is first coming in, it’s usually messy. Not only are you receiving info from various sources, but you’re probably dealing with multiple file formats and timestamps.

But don’t worry, here’s how to tackle it…

Standardize Incoming Data

This step should actually come before data collection, but we’ll get to that.

All your data should come from standardized sources in a standardized format. For example, election technology solutions will often have to collect data from voter registration systems, polling devices, and administrative databases. Ideally, all of that data will be formatted the same way before you collect it.

Automate Data Collection

Did someone say manual data entry?

As dope as Siri and Alexa are, I don’t use voice assistants to enter my data. Luckily, there are technologies that can automatically collect data from your various sources and put it all in one place.

Consider this…

The U.S. Election Assistance Commission revealed over 158 million votes were cast in the 2024 general election. Data is coming at you from every direction, and if you’re stuck entering it manually, you’ll never keep up.

Validate Your Data

Performing validation checks during the data collection process is something a lot of people forget to do.

By forcing your data through checks as it’s collected, you’ll save yourself so many headaches down the line. Trust me.

Data To Insights

Congrats. You’ve collected your data. What’s next?

Turning raw data into usable insights is where the magic happens. But before you pull out your crystal ball, there are some important steps you should follow.

Clean Your Data

Always clean your data before you do any analysis. Period.

Also always make sure your data is formatted correctly, check for duplicates, and deal with any missing values.

Yes, this step is annoying. I know. But cleaning your data ensures you’ll have quality outputs later on.

Apply Analysis Methods

Now it’s time to start diving into your data.

I know you’re excited. Just remember to apply your analysis methods consistently throughout your datasets. For instance, if you’re calculating voter turnout percentages, use the same calculation for every dataset you’re working with.

You should also document your analysis process. Note what calculations you used, what you’re assuming, and where your data is coming from.

Creating Useful Visual Outputs

Data visualization is the cherry on top of the data analytics process.

You can analyze data all day long, but if you’re not able to present that information back to your team in an easy-to-understand way, you’re wasting your time. Here are a few tips to visualize better:

Pick the appropriate visual format

Pie charts are overused. Seriously.

There are so many other visuals you can use to present your data. Use line graphs to show trends. Use bar charts to compare things. Use maps if you’re working with geographical data.

Formatting should complement your data, not annoy everyone who views it.

Keep it simple

Easier said than done, right?

Unless you’re trying to win a data viz award, your visuals shouldn’t be complicated. Make sure each individual chart you’re creating focuses on one core idea.

If you find yourself wanting to showcase multiple insights at once, split your data into multiple visuals.

Accessibility is key

Here’s something you may not consider when working on data visualization…

According to research, 77% of businesses found that data visualization helped make complex data more accessible to non-business users.

Make sure everyone who views your visuals can understand them. Use colorblind-friendly color schemes, include descriptive labels, and don’t make your text too small.

Tools To Help You Speed Things Up

Alright, let’s wrap this up by talking about some useful tools.

Data Collection Tools

Technology exists that allows you to automatically collect data from your various sources. Your data will be standardized and validated as it enters your system.

Analysis & Visualization Tools

This one depends on your preference. There are plenty of quality spreadsheet options out there that can allow you to analyze your data. You can even automate some processes in Excel if you’ve got the chops.

If you’re going down the route of specialized analysis tools, you’ll want to find something that supports automation and can handle the amount of data you’re working with.

For visual outputs, you should consider the tool’s ease-of-use, customizability, and ability to share your visuals with others.

Integration

Here’s the secret sauce that makes all of these tools helpful…

When you build a workflow that integrates each of these pieces together, data flows through your process without anyone having to lift a finger.

Suddenly your data is being collected, analyzed, and visualized in a matter of minutes.

That’s how you know you’ve constructed an efficient workflow.

Wrapping It Up

Streamlining your reporting process isn’t about figuring out one big solution. It’s about optimizing each step of your process and then interconnecting them.

To wrap all of this up…

1. Data collection should be standardized and automated
2. Always clean and validate your data before you start analyzing
3. Apply your analysis methods consistently and document your process
4. Visualizations should be simple and accessible
5. Use tools that can integrate with one another

If you focus on improving each stage of your reporting workflow, you’ll be spending less time pulling data punches and more time doing actual analysis.

Don’t try to boil the ocean. Pick one stage of your process that you think needs the most work. Improve it. Then move onto the next stage.

Before you know it, you’ll have a reporting workflow that doesn’t suck.