Data analysis and visualization are powerful tools for business analysts who need to elicit, validate, and communicate requirements for complex projects. In this article, you will learn how to use these techniques to support your requirement elicitation process and enhance your stakeholder engagement.
Data analysis and visualization are processes used to collect, process, interpret, and present data. Data analysis can help to explore and understand the current situation and problem domain, while data visualization can be used to define and measure objectives, identify and prioritize requirements, validate assumptions with data and evidence, and communicate findings to stakeholders in a clear manner. Together, these processes can provide valuable insights into a project.
Before beginning the process of analyzing and visualizing data, it is important to plan for what data is needed, how it will be collected, and how it will be used. Questions such as the purpose and scope of the data analysis and visualization, the needs of stakeholders, the sources and types of relevant data available, the methods and tools used to analyze and visualize data, and how results will be documented and reported should all be considered. Furthermore, it is essential to ensure the quality, accuracy, and reliability of the data and results, as well as how they will be shared with stakeholders.
Planning for data analysis and visualization is a crucial step in any data-related project. As a Power BI developer, here's a structured approach to help you plan effectively: Objectives: Start by understanding the business goals and objectives that your data analysis and visualization should support. What insights are you trying to gain, and what questions should the data answer? In my experience Choose the appropriate visualizations (e.g., charts, graphs, tables) that best represent your data and align with your objectives. Consider the audience and what they'll find most informative. Go through the freer resources and understand the chart type required for your viz. Ex: For a time comparison use a line chart instead of a bar.
Data analysis can help to elicit requirements by providing facts, evidence, and insights to inform decision making and problem solving. You can use descriptive analysis to summarize and describe the features of the data, such as frequency, distribution, and standard deviation. Inferential analysis can be used to test hypotheses and draw conclusions from the data, such as correlation and causation. Exploratory analysis allows you to uncover patterns, trends, outliers, and anomalies in the data. This technique can generate ideas and insights for the project, as well as enable clear communication of requirements and findings to stakeholders.
Data visualization can provide visual and interactive ways to explore, understand, and communicate your data and results, thereby helping you to elicit requirements. Tables and charts can be used to display and compare numerical or categorical data, such as the bar, pie, line, scatter, histogram, etc., to show frequency, distribution, proportion, relationship, or change. Maps and geospatial can be used to display and analyze data that have a geographic or spatial dimension, such as location, distance, area, direction, or movement. Dashboards and interactive techniques can be used to display multiple data visualizations in a single interface with filters, sliders, buttons, or menus that allow stakeholders to customize and explore the data according to their needs and preferences.
Data analysis and visualization are not one-time activities, but rather iterative and collaborative processes that require constant evaluation and improvement. To ensure your data analysis and visualization are effective, you should consider if they are aligned with the purpose and scope of your project, relevant to your stakeholders, clear and accurate, engaging and effective in conveying the message you want, as well as adaptable and scalable to accommodate changes and updates. Additionally, check that they follow the best practices and standards of data quality, ethics, and integrity.
Gather Feedback: Collect feedback from end-users, stakeholders, and domain experts. They can provide valuable insights on the relevance, clarity, and effectiveness of your reports. Refine Visualizations: Make sure your visualizations are clear, concise, and align with the data you're presenting. Consider using the most suitable chart types for your data. Simplify Complexity: Simplify complex visuals and data points to make them more understandable. Use tooltips and drill-through features to reveal details gradually. Based on my experience customers are more interested in looking the qualitative visuals where the visual itself can tell the story behind the peak or downfall.