The importance of data guiding decision-making in sales and marketing continues to grow.
However, the true power of data lies not in its complexity, but in its presentation.
Analyzing the data is a valuable skill; presenting complex concepts engagingly and comprehensibly is another beast. The most valuable asset to a business is the person who can do both effectively.
The mantra here is simple - "tell it to me like I'm 5."
Simplicity is the most complex form of understanding; you must be an expert on a topic to succinctly explain a complex idea. That's where data visualizations come into play
An Intro to Effectively Presenting Complex Data
Data visualizations are any chart, graph, or illustration that tries to communicate some data.
Good ones can look like art that belongs in the Louvre; bad ones bring up bone-chilling memories of hideous 3rd-grade PowerPoint slides.
How you present information determines how effectively your message is understood - which is critical to any sales or marketing role.
Sales teams with proper marketing support and beautifully designed content might take it for granted, but any content developer or analyst knows the struggle of synthesizing intense research into a simple, easy-to-understand conclusion.
Here's a quick look into achieving this simplicity, allowing data to help sell your pitch to change mindsets and drive actions.
1. The Unwrapping: Macro Story and Micro Points
Every dataset has a macro story - a broad narrative it aims to convey. The micro points are the finer details that enrich this narrative. Unwrapping complex data begins with understanding and outlining the macro story, then delving into the micro issues in a logical flow that reinforces the broader narrative.
2. The Purge: Killing Useless Metrics
Not all data is valid. It's crucial to sift through the metrics, keeping only those that add value to the narrative and discarding the rest. This purge streamlines the data, making it more focused and easier to understand.
3. Say No to Tables: Let Every Stat Tell a Story
Tables can be daunting and fail to engage the audience. Replace them with visual representations where each statistic tells a story, making the data more relatable and impactful.
4. Know Your Audience: Data Absorption Capacity
Understanding the audience's ability to absorb data is crucial. Tailor the data presentation to match their comprehension levels, ensuring the narrative is clear and the takeaway is unmistakable.
5. Let Data Drive Mindset and Actions
The ultimate goal is to let the data change mindsets and drive actions. Simplicity in data presentation is the catalyst that allows for this transformation.
6. The Visual Ensemble: Choosing the Right Charts and Graphs
Selecting the appropriate visual tools to present data is pivotal. You need to know what we're looking to present - what is the function of the data visualization? What is the main point you're trying to convey? Reference the outline below to ensure you're using the right tool for the job.
Let's look at the 15 Main Functions of Data Analysis:
1. Comparisons
These visualizations are essential for comparing datasets or values to find similarities, differences, or trends.
2. Concepts
Visualizations under this category help illustrate abstract ideas or concepts, making them easier to understand.
3. Data over Time
These visualizations are crucial for tracking changes, trends, or data patterns over a specified period.
4. Distribution
Visualizations help show how data is distributed, which is vital for understanding a dataset's range and common values.
5. Hierarchy
Hierarchy visualizations illustrate the relationships and rankings among different groups or categories.
6. How Things Work
Visualizations in this category help elucidate processes or systems, aiding in understanding how various components interact.
7. Location
Location-based visualizations are crucial for representing data tied to geographical areas or coordinates.
8. Movement or Flow
These visualizations depict the movement or flow of data from one state or place to another, illustrating transitions or dynamics within a dataset.
9. Part-to-a-Whole
Visualizations in this category are vital for showing how individual parts contribute to the whole, aiding in understanding proportions.
10. Patterns
Pattern visualizations help identify recurring or common trends in a dataset, making it easier to analyze and interpret.
11. Processes & Methods
These visualizations help illustrate workflows, methodologies, or procedures, aiding in understanding the sequence of actions or events.
12. Proportions
Proportional visualizations are crucial for understanding the relative size or importance of different parts of a dataset.
13. Range
Range visualizations illustrate the extent of variation in a dataset, helping to understand the spread and limits of the data.
14. Reference Tool
These visualizations serve as tools for reference, helping to categorize or find information efficiently.
15. Relationships
Relationship visualizations are vital for understanding data elements' connections, interactions, or correlations.
Conclusion
Presenting complex data effectively is an art that marries simplicity with understanding. The complex becomes simple: unwrapping the macro story, purging useless metrics, telling a story with every stat, and choosing the right visual tools.
The result is a straightforward, impactful narrative that has the power to change mindsets and drive actions, propelling the marketing and sales strategy to new heights.