My Six-Point Guide to Succeeding as a Data Visualization Professional in Business Intelligence Projects
In this article, we explore how understanding the business and data of your stakeholders can lead to significant advantages. As someone who has led many projects as a data visualizer, I will share practical examples of how I became a successful data visualization practitioner. The points discussed here provide a more comprehensive version of the key highlights from my previous presentation, "The 5 Basics of Data Visualization Best Practices for Business Applications." Here is a link to my last presentation on the topic for the Analytics TUG.
Note: I would like to clarify that the experiences I will mention were obtained during my past positions as a data analyst/product owner and consultant with former employers.
1. Embrace Active Listening
Listening is a remarkable power in business, extending beyond merely hearing words. It involves actively interpreting the underlying message, asking insightful questions, and fully understanding the requirements. One must delve beneath the surface and unravel the essential questions to grasp the situation, searching for meaningful solutions truly. This immersive listening process played a vital role in my personal journey. It allowed me to perceive the available data, identify missing pieces, and ultimately create a comprehensive and effective solution. Therefore, let's amplify the significance of active listening and harness it as our secret weapon for success. Drawing from my experience as a former subject matter expert, I witnessed how developers could lose their way due to a lack of understanding of the business. It's crucial to avoid falling into the same trap.
2. Understand Scope
Determining data needs is akin to observing an iceberg; data visualization is merely the tip. You need to identify if data collection, modeling, and engineering are required and gauge the extent of these tasks. Consider a scenario where you're tasked with creating dashboards for an organization. Some questions you should ask:
- How many dashboards are required?
- What data is available?
- Is the data cleaned, cataloged, and governed?
- Is there potential for scope creep?
- Who and what are my available resources?
This foresight helped me when I was roped in to build dashboards for a management consulting firm. By scoping out the project, I could set a realistic timeline and deliver efficiently.
3. Set Expectations
Understanding the requirements and identifying what's available and missing allows you to provide a reasonable timeline and delivery expectations. For example, I once developed forecasting tools for a former employer. While the firm expected the tool within a week with accurate data and limited historical precedence, a careful study of the available data and resources showed the project would take at least a couple of months to get started. By setting clear expectations, I managed the stakeholders and ensured the project's smooth completion.
4. Verify Business Questions
There needs to be more than just providing a dashboard that meets requirements. It's crucial to ensure it serves the intended purpose. For instance, when I developed a demographic dashboard for a public sector client, I validated it against their specific business questions like "Which demographic is most impacted by economic downturns?" or "Does age impact their economic recovery?" This step ensures the solution is effectively tailored to the business needs.
5. Be Ready to Iterate
Effective dashboards require ongoing revisions as new inquiries arise and other impacts are discovered. It's essential to recognize when to pivot and make necessary adjustments. In a project a couple of years ago centered on people analytics, we noticed that as the usage of the dashboard increased, so did the demand for additional features and metrics based on new data points. I confirmed this with the stakeholder and promptly made the necessary updates to continue providing enhanced vital insights.
6. Offer Consistent Support
Short-term support is crucial, like providing instructions, creating a data dictionary, and sourcing. But long-term support, including monitoring usage and seeking feedback, is equally essential. I learned this while working with a sizeable former employer. After the initial hand-holding phase, I continually monitored the platform's usage and regularly sought user feedback. This approach helped me proactively identify and fix issues, boosting user satisfaction and engagement.
Navigating a successful data visualization project necessitates a careful blend of various strategic approaches. These six strategies have been crucial in my journey toward playing a pivotal role in successfully implementing these projects. A thorough understanding of the business environment, clearly defining the project scope, setting realistic expectations, continuously ensuring alignment with actual business needs, remaining open to iterations, and providing continuous support have all been vital to my success. If you want to harness your expertise and create impactful data visualization for projects or seek a seamless career transition, these steps could serve as your guiding light.