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Data Visualization vs Analytics: The Mistake Associations Make

Your data needs context to tell its story. Data visualization tools give you a unique visual perspective, revealing key insights and actionable information at a glance. The value of this vantage point is undeniable for associations. 

 

But while the resulting visuals show big-picture trends, they don't necessarily answer specific questions. Nor do they tell you what actions to take now to change individual member behavior at scale.

Let's dive into both tools, why the difference between data visualization vs. analytics matters, and how to (realistically) follow through on actionable insights at scale.

 


How Associations Use Data Visualization 

Data visualization is a handy tool for interacting with complex data. It translates dense datasets into a clear, digestible visual presentation.

 

Associations use data visualization tools to automate the process of monitoring performance and building monthly reports. Non-technical users get the results they need in an eye-catching visual story. 

 

Previously, each department or functional area had to track KPIs manually, which was painfully tedious and time-consuming. Now association folks can access all kinds of metrics, from member engagement to data quality, via an interactive dashboard, graph, chart, or plot. 

 

Let’s say, for instance, you want to give membership, marketing, and other non-technical the ability to see member engagement scores in real-time. You can use tools like Tableau, ZAP Business Intelligence, or Microsoft Power BI to build an easy-to-use, easy-to-interpret dashboard. 

 

Beyond organizing large sets of data, you can also compare data attributes and uncover patterns of behavior and outliers with these visual “data communication” tools.

Data visualization example

 

The downside of many reporting and visualization tools is they don't provide a stream of “actionable insights" based on individual behavior and engagements. Instead, data from various sources are mashed together to reveal big trends. 

Limitations for Behavior Change 

 

Traditional data visualization and business intelligence tools provide cool visuals and reveal overarching patterns and outliers, but they’re not really able to answer specific questions hidden in your data. 

 

These visualization platforms help us answer descriptive questions such as: How many members have a certain level of membership? How much have our membership counts changed in the past two years? Or where do most of our members live? 

 

But data visualization tools don’t tell you a whole lot about the why. They don’t really answer what you can expect next or how you can get specific members to take a preferred action to move towards an organizational goal. 

 

For example, you can’t answer questions like: Does more engagement in virtual courses reduce in-person attendance? If the renewal date falls around a certain time of the year or event, does it increase retention? What about other confounding factors? 

 

Human-Centered Data Analysis 

Instead of simply organizing your data and leaving the pieces of the puzzle up to creative interpretation, new human-centered data analytics approaches give you aggregate visualizations as well as next best steps. These AI-based “smart analytics” tools tell you what action to take now to achieve your desired outcome. 

 

You get actionable insights for every member across your application ecosystem. These personalized insights are how content intelligence and marketing automation tools allow you to personalize your communications and digital products at scale. 

 

It’s about using your data to form predictions and identify concrete actions to change those outcomes. Actions that help to ensure limited time and funds are allocated where they’ll have the greatest impact. 

 

Turning Data into Actionable Insights

Newer, “person-centered” data analysis tools help you solve daily challenges and allocate limited resources more effectively. It may sound spooky or too much work to get it all going, but it’s honestly about: 

 

  1. Determining your desired outcome (e.g. increase renewals, attendance, donations). 
  2. Figuring out where the relevant data “lives" (aka the data source). 
  3. Sending the data through a predictive model or "smart" automation tool.

 

To get better and better results though, it's important to monitor outcomes and refine your approach. Continuous improvement is all about experimentation, ongoing data monitoring, and action.

Don't have the resources or need help getting going? Let's talk.

 

What's the future of data visualization? 

The next frontier of visualization and data analysis is about revealing personalized insight and developing a humanized strategy to progressively shape results. In order to achieve business goals, you need to know how to change behavior on an individual level. 

 

Today, marketing automation tools that combine smart analytics, machine learning, and automation are more accessible (and affordable) than ever before. They make it possible to turn individualized insights into action at scale, regardless of your size or where you are in your digital transformation journey. 

 

Getting Started 

The future of association data analysis is individualized. If you’re ready to take your data visualization and analytics capabilities to new heights, let’s chat. 

 

We partner with membership associations, non-profits, and other mission-driven organizations in making all things digital – from smart analytics and business intelligence to marketing automation and digital experience optimization. 

 

Are you ready to unmask the secrets of your data to change behavior on a member-to-member, user-by-user basis? Get in touch to start growing your impact. 

Emily Shine

Topics: Analytics

Written by Emily Shine

Emily Shine is a content writer and SEO strategist who helps purpose-driven organizations and entrepreneurs build their online presence. When she's not behind the computer, you can catch her in the park, gigging on a stompbox, or playing tennis.