If you start your analysis with a clear outcome in mind, you will stay focused and deliver better insights. This helps you avoid wasting time wandering through data.
The six types of analysis outcomes are a framework you can use to guide your work, known as the SPREAD framework. Understanding these outcomes will help you move from a "zombie" type of analyst, one who gets lost in the data, to a "survivor" type who delivers clear business value.
So, you’ve done the hard work of gathering and analyzing your data. You've cleaned it, run your models, and found some interesting patterns.
But what happens next? The real value of your work isn't just in the numbers; it's in how you explain what those numbers mean. This is where many data professionals get stuck.
They have the facts but struggle to turn them into a compelling story that their audience can understand and act on.
The key is to move beyond simply presenting your data and start interpreting it.
We'll show you how to take your data analysis and findings and transform them into a clear, convincing narrative.
You'll learn how to structure your discussion, connect your results to a bigger picture, and answer the crucial question: "So what?"
This type of outcome is about finding anomalies, spikes, or sudden changes in your data. Your goal is to detect something that has shifted or broken. Think of a key metric you track daily. If that metric suddenly drops, your first job is to find the difference and identify the problem.
For example, a dashboard shows your sales dropped by 30% in one day. You’re not trying to find out why yet; you're simply confirming that an abnormal event has occurred. Your report might say, "We’ve identified a significant drop in sales on Tuesday." This is an immediate, high-value insight for leaders who need to know what's happening right now.
The goal of this outcome is to forecast a future event based on past and current data. This is where you leverage machine learning models and statistical techniques. Instead of just reacting to what has happened, you use your data analysis and findings to prepare for what's next.
For instance, you can forecast next quarter’s sales to help the finance team with budgeting. You can also predict which customers are most likely to leave, giving the customer success team a chance to intervene. This moves your work from a historical report to a proactive business tool.
This outcome is all about explaining why something is happening. This analysis goes deeper than just spotting a difference. You use data to find the real reason behind an issue. For example, you find a drop in sales (Spot the Difference). Now you dig into the data to find the root cause.
Your analysis reveals that the drop in sales was directly caused by an out-of-stock product. You don't stop there. You keep asking "why" to uncover the deeper issue. Was it a supplier delay? A system glitch? A sudden demand spike? This type of analysis empowers leaders to fix the core problem instead of just treating a symptom.
An experiment, often called A/B testing, is about seeing which of two or more options performs better. This is a powerful way to make data-driven decisions on a small scale before a full launch. It's a common practice in marketing and product teams.
For example, a company might want to know if a new product name, "Cranium Crunch," is more appealing than the original. They run a small test and find that "Cranium Crunch" gets more clicks. This clear outcome allows them to make a confident decision. Similarly, you could test different email subject lines or ad creatives to see which ones drive more engagement.
This is one of the most common types of analysis and can provide quick insights. It’s where you compare and contrast different data points to see how they perform. This can be used to understand performance at a granular level.
For instance, you might create a bar chart comparing sales performance across three departments: apparel, cosmetics, and pharmacy. This allows a manager to quickly see that the apparel department is outperforming the others. Or you might compare the sales of two different products to see which one is selling more. This type of analysis is all about providing a clear performance overview.
This is the type of analysis that will make you a standout analyst. A discovery outcome aims to find new opportunities to either increase revenue or decrease costs for the business. This analysis is less about answering a specific question and more about exploring data to find something unexpected and valuable.
For example, you might be exploring customer usage patterns and discover that customers who use a specific new feature are twice as likely to renew their subscription. This is a discovery that can lead to a new business strategy, a product change, and a direct increase in revenue.
Choosing the right outcome for your analysis saves you time and keeps your work focused. Here are some examples of when you might use each type:
Spot the Difference: Use this when a dashboard alerts you to an anomaly. For example, if a key metric suddenly drops, or if there's an unexpected spike in website visits. Your goal is to find the problem quickly.
Predict: Use this when a business leader asks "what will happen?" For example, predicting next month's sales to inform inventory levels, or forecasting customer churn to help a marketing team create a retention campaign.
Root Causes: Use this when a business leader asks "why did this happen?" You would use this after spotting a problem to find its source. For example, investigating why a recent marketing campaign had a low click-through rate.
Experiment: Use this when a business leader wants to test a new idea. For example, testing two different versions of a website homepage to see which one leads to more sales.
Assess and Compare: Use this for regular reporting. For example, comparing sales between the first and second quarters to see if there's been growth, or comparing the performance of different regional teams to find a high-performing one.
Discovery: Use this when a leader says, "Let's find a new way to increase revenue." For example, exploring data to find unexpected correlations between product usage and customer lifetime value.
These six outcomes are the core outputs because they answer the fundamental questions that every business needs to ask.
Whether the goal is to increase revenue or decrease costs, all business decisions fall into one of these six categories.
What happened? (Spot the Difference, Assess and Compare)
Why did it happen? (Root Causes)
What will happen next? (Predict)
What if we try this? (Experiment)
What new opportunities exist? (Discovery)
By framing your analysis around these questions, you ensure that your work is always tied to a clear business purpose. You move from simply reporting on data to providing meaningful, actionable insights that drive real-world outcomes.
Being a "zombie" analyst means you get lost in the data. You collect information without a clear purpose, wasting time and producing reports that don't lead to action. You're busy, but not effective.
A "survivor" analyst, on the other hand, is a leader. They know that every analysis starts with a question and has a specific outcome.
By focusing on one of the six SPREAD outcomes, you will:
Stop Wasting Time: You won't wander through the data without a clear purpose.
Deliver Value Faster: You'll produce focused insights that leaders can use to make immediate decisions.
Build Trust: Leaders will see you as a strategic partner, not just a data reporter. This will help them trust your insights and rely on you for future projects.
Advance Your Career: By consistently delivering clear, actionable outcomes, you will be recognized as an essential contributor to the business's success.
Data analysis outcome is only as valuable as your ability to discuss it.
A great discussion connects your data analysis and findings to real-world implications.
The SPREAD framework outlines the six core outcomes of any analysis: Spot the Difference, Predict, Root Causes, Experiment, Assess and Compare, and Discovery.
Start with a clear business question to keep your analysis focused on a single outcome.
To get better at discussing your results, practice telling the story behind the data.
Communication skills are as important as technical skills for a data analyst.
How can I improve my data analysis skills?
Practice with real datasets, take online courses, and get feedback from peers. Focus on developing both your technical skills and your communication abilities.
What are the six types of outcomes for a data analysis?
The six outcomes are Spot the Difference, Predict, Root Causes, Experiment, Assess and Compare, and Discovery.
How do I avoid getting off track during an analysis?
Always start by writing down the specific business question you are trying to solve. This keeps you focused on the intended outcome.
How to learn data analysis?
Start by learning foundational skills and then progress to more advanced tools. The most important thing is to learn how to think clearly, not just how to use a tool.
What should I include in a data analysis report?
A typical report should include an introduction, a summary of your findings, a detailed discussion of your results, and recommendations for next steps.