1. Solving business problems with data
Welcome. In this video, we will explore how data-fluent individuals identify business problems using data and can effectively translate these problems into analytical questions.
2. Scenario - declining user engagement
To begin, let's consider a scenario. Katia works for a tech startup that provides innovative software solutions to businesses. While reviewing KPIs or otherwise Key Performance Indicators in her Weekly KPI dashboard, she noticed a sudden drop in user engagement metrics, particularly a decrease in the number of active users and a rise in customer support tickets related to the software's performance. The declining user engagement impacts customer satisfaction and revenue, hence she deep dives further to solve the problem.
3. Scenario - translating the business problem
By combining her analytical skills and her business knowledge of what is driving the KPI, she formulates several analytical questions: What is the trend in active user numbers over the past few weeks? Are there specific features causing an increase in customer support tickets? Is there a correlation between user engagement and software performance? Now Katia has a clearly defined problem and analytical questions that she can investigate further with the use of data and analytics.
4. Data fluency behaviors
From this scenario, we can observe the following behaviors that showcase strong data fluency skills. First, the ability to identify the business problem using clearly defined KPIs. Second, the ability to transform the business problem into specific analytical questions that can be answered using data. Let’s see these elements in more detail.
5. Identifying the business problem
Data-fluent individuals possess a strong understanding of the KPIs that drive their business. They leverage these KPIs to regularly monitor performance and identify business problems or opportunities for improvement. In addition, when required, they have the expertise to define new KPIs that align with the overall business goals. These KPIs act as quantifiable measurements, enabling them to gauge the success of their strategies and initiatives effectively. Along with these KPIs, they also set specific targets to track performance against. Regularly monitoring these targets enables them to take timely actions if performance deviates from the desired outcomes.
6. Forming analytical questions
Besides identifying business problems, the second fundamental skill of data-fluent individuals is the ability to translate business problems into well-formed analytical questions. By formulating well-defined questions, they can focus their data analysis efforts on answering specific business challenges. A good practice when forming analytical questions is to use the SMART methodology. This means making the analytical questions specific, measurable, actionable, relevant, and time-bound. This helps in determining what data is needed for the analysis and in selecting the most appropriate analytical methods and tools to use. Essentially, it ensures that the analytical solution will address the business problem.
7. Leveraging cross-functional expertise
Lastly, it is worth highlighting the importance of collaboration. When data-fluent individuals collaborate with stakeholders from different departments, they gain access to diverse perspectives, domain expertise, and unique data sources. This includes collaboration not only with business teams but also with analytics experts who are familiar with the data landscape and have strong analytical thinking skills. This collaborative process enriches their understanding of the business problem allowing them to ask more insightful and relevant analytical questions.
8. Let's practice!
By mastering these behaviors and practices, data-fluent individuals can drive data-driven decision-making effectively. Now, let's review these concepts with some exercises!