Chapter 1: Understanding Data's Potential

In the realm of data projects, approximately 87% fail to reach production, while about 85% of AI initiatives do not yield the desired outcomes. These statistics are familiar to many professionals who have experienced similar challenges. The phrase, "This is a very interesting insight, let me get back to you," resonates all too well. So, how can we avoid having our analyses gather dust in the background? How can we ensure that our insights are integrated into everyday operations? What does it truly mean to deliver value through data? Let's explore these questions.

Once you've gathered the data and interpreted it, focus on the following:

1. Big Picture Impact

   Is your analysis centered on a specific issue, or can it be generalized for broader application? For instance, can a basic feature usage chart be enhanced with insights such as retention improvements, revenue contributions, new use cases, or Jobs To Be Done (JTBD)? Does your analysis provide a deeper understanding of the users, product, or business? If it does, how? If it doesn’t, why not? Recognize what is effective to continue leveraging it, identify what's absent to initiate it, and pinpoint barriers to eliminate them.

2. Alignment with Goals

   Consider a scenario where a Product Manager (PM) oversees a productivity feature. Your analysis should demonstrate how this feature has influenced user productivity, highlight timeframes of increased engagement, and identify which user segments are the most active. Ultimately, the PM's goal is to ensure the feature's success, and your objective should be to support that goal.

3. Signals and Levers

   Continuing with the feature example, identify the indicators that suggest a rise or fall in feature usage. This could involve specific user demographics, geographical trends, launch issues, or lack of awareness. These indicators will substantiate your claims regarding the feature's performance. Additionally, understand the factors that drive feature usage, such as notifications, JTBD, or interactions with other applications. Your goal is to identify the relationships and influences—both leading and lagging—that affect performance.

4. Easy to Understand Insights

   Your analysis doesn’t need to delve into complex metrics like RMSE or AUC to explain why one feature is superior to another. Avoid presenting misleading statements that only hold true in specific contexts. For instance, if new users primarily use Edge to download Chrome, this insight doesn’t provide practical value to the PM. Instead, state that evening notifications yield higher open rates without resorting to jargon. Your aim is to communicate insights in a manner that facilitates actionable steps for stakeholders.

5. Clarity of Action

   The ultimate goal of any analysis is to inspire action. The PM needs clarity on which user segments are most pertinent to the feature, requiring you to understand the JTBD of these users and how they align with the feature's value proposition. For example, if a productivity tool is designed to streamline users' tasks, your role is to clarify which high-value cohorts to focus on and which features resonate with users.

In summary, let’s transform our data into compelling narratives rather than mundane figures. Keep making waves in the data landscape! 🌟✨

Learn more about the intricacies of data storytelling, including the essential aspects every analyst should grasp, how data can be misinterpreted, and the challenges data professionals face. Explore the eight stages of the analysis lifecycle that lead to impactful results, discover ten ways to make your data tell a captivating story, and understand the most common types of data analysis.

The first video presents "5 Ways to Improve Your Data Analysis in Under 5 Minutes," offering quick tips for beginners to enhance their analytical skills.

Chapter 2: Skills for Success in Data Analysis

To excel as a data analyst, certain key skills and best practices are essential.

In the second video, "How To Become A GREAT Data Analyst - 5 Skills And Best Practices For Data Analysts," you will learn the crucial competencies necessary for success in the field.