Reflecting on my recent Ignite client project with PICKL, where we aimed to identify and recommend success metrics and consumer-brand insights, I have been thinking a lot about the various aspects that contributed to our group’s success, getting the highest score, as well as my own personal challenges that I encountered. Approaching the project’s primary objective, our team came into the project with limited Excel and data analysis skills. strived to translate these insights into actionable recommendations, leveraging PICKL’s Software as a Service (SaaS) platform. The scope of the project was not just about data analysis; it was about understanding and integrating complex, multifaceted streams of information.
Despite these self-imposed pressures, I persisted. I explored the use of Google Maps reverse geocoding API to enhance our data's geographical dimension. This, combined with external datasets like the US Census Bureau’s information on median incomes and house values, was meant to provide a richer, more nuanced analysis. However, the integration of these datasets proved more challenging than anticipated. There were moments of frustration, especially when I realized that the datasets didn't correspond as I had expected, leading to a depreciation in the initial value of my data. Initially, I envisioned using sophisticated BI tools and conducting an extensive OLAP analysis. However, I soon realized that setting such high expectations was not just ambitious but somewhat impractical given the project's timeframe and resources.
I grappled with technical issues, from configuring the Google Maps API incorrectly to struggling with Excel’s VBA macros. Each challenge was a learning opportunity, teaching me the importance of adaptability and resilience in data analytics. The realization that I was using an incorrect dataset (AGI stubs instead of median income) was a pivotal moment, highlighting the significance of meticulous data verification in the initial stages of any project. Despite these hurdles, our team's dynamic was a crucial factor in our success. We collaborated effectively, pooling our diverse skills and knowledge. My teammates were understanding of the technical challenges and were instrumental in finding workarounds and solutions. This collaborative spirit was evident in our final presentation, which was not just a display of our findings but a testament to our collective effort and determination.
Looking back, I realize the importance of setting realistic goals and timelines. My perfectionist tendencies, while beneficial in ensuring high-quality work, sometimes led to unnecessary stress and unmet expectations. However, this project has been a profound learning curve. It taught me the value of flexibility, the importance of early and effective integration of diverse data sets, and the need for appropriate technical resources.In future projects, I aim to leverage these learnings, ensuring that my analytical approach is tempered with practicality and adaptability. I also intend to explore cloud-based analytical solutions and enhance my skills in data visualization tools like Power BI or Tableau. These tools, I believe, will enable more efficient handling and processing of qualitative data, contributing significantly to the project outcomes.