This article details Akhil’s experience in leveraging ChatGPT to navigate the complexities of Power BI for data analysis and dashboard creation. Faced with two challenging tasks—collating data from 43 Excel sheets and analyzing 16 months of sales data—the author used ChatGPT to generate DAX formulas, validate results, and streamline processes. The article emphasizes the importance of clean data, precise prompts, and validation techniques. It showcases how AI tools can significantly enhance the capabilities of management consultants by enabling faster mastery of tools and data-driven decision-making.
Power BI might be Microsoft’s most powerful tool for transforming data into insights, but when you combine it with ChatGPT, you’re tapping into a game-changing synergy. As someone with no prior exposure to Power BI, I found myself facing a daunting task: analyzing complex data and building dashboards. While I had experience in IT and software development, Power BI was uncharted territory for me. This article shares my journey through two challenging cases where I leveraged ChatGPT to navigate the complexities of Power BI.
Context and Challenges:
I was confronted with two critical tasks which demanded a high-level of precision and efficiency. The first involved creating a dashboard by collating data from 43 Excel sheets, each containing thousands of rows. The second required analyzing sales data for 16 months, with the objective of extracting key business insights. Initially, I considered turning to the Microsoft community for guidance. However, I realized that their solutions are not tailored to my case, and I would need a back-and-forth communication to clarify my needs which is very time-consuming.
This is where generative AI tools like ChatGPT came into play. I adopted a solution-oriented approach, utilizing ChatGPT to bridge the knowledge gap and achieve the desired outcomes. With ChatGPT, I was able to explain my requirements clearly, using proxy names and specific examples. Since I had a clear vision of the expected outcome, it became easier to validate the results.
Approach:
The first step was to clean and arrange the data in a structured format. This had to be uniform across all sheets. While I ensured consistency of data like Column Headers, Data Format, etc, ChatGPT assisted me in generating the necessary DAX formulae to create measures & new columns required for my analysis.
The process was iterative: I would test the suggestions, validate them against a subset of the data, and refine them as needed. This method saved me a significant amount of time. I began by outlining the expected insights and explaining the data structure to ChatGPT. The AI’s ability to understand the requirements and provide relevant DAX formulae and Power BI features made the process smooth. For example, I needed to calculate growth percentages and compare sales across different time periods. ChatGPT guided me in creating the necessary measures and visuals to achieve these.
Validation:
Since I had the raw data at hand, I could cross-check the AI-generated results with actual figures. This manual validation process was crucial, but not overly burdensome, as the formulae and logic provided by ChatGPT were consistent and reliable across the dataset.
Key Learnings:
This experience reinforced the importance of a strategic approach to data analysis and dashboard creation. Key learnings from this journey include:
- Consistent Data Collection: A cleaned dataset is paramount to perform a hassle-free analysis and generate appropriate insights. Once we have such data in hand, by articulating specific requirements to ChatGPT, we can tailor the outputs to meet objectives effectively.
- Precise Prompts: Knowing exactly what you want to achieve makes it easier to guide the AI and validate the outcomes. The more precise your prompt is, the more customized would be the response from GPT. If you used proxy names, ensure that you map them properly and tweak the formula suggested by GPT
- Validation and Accuracy: Ensuring the accuracy of analytical models through validation techniques is essential. This step not only builds confidence in the results but also helps you understand the gaps in the recommended solutions. This is where my prior experience in IT and Software Development came handy. It became easier to decode the DAX formulae and identify the gaps.
Conclusion:
This experience serves as a powerful example of how leveraging AI tools like ChatGPT can significantly enhance the capabilities of management consultants. In an industry where data-driven decision-making is paramount, the ability to quickly master new tools, streamline complex processes, and deliver actionable insights is invaluable. By combining technological expertise with strategic thinking, consultants can leverage AI tools for competitive advantage.
As the consulting landscape continues to evolve, those who integrate AI into their practice will be better positioned to deliver impactful results, driving value for both clients and their own organizations.
Akhil Chowdary
Akhil Chowdary holds a Master's in Management from ESCP Business School and an MBA in International Business from the Management Development Institute. His experience spans international exposure and cross-cultural understanding, combined with strong analytical, problem-solving, and leadership capabilities. Currently, he is a Management Consultant at Avalon Consulting, where he specializes in market sizing, opportunity assessment, and strategy consulting, with expertise in Excel and Power BI.
Previously, he worked as a Program Manager at Amazon in Madrid, handling end-to-end country-wise planning and logistics strategy for Southern European countries. He also has a background in IT consulting from Capgemini, where he led customer-facing teams and won several accolades for his service.
Email: akhil.chowdary@consultavalon.com
He thrives on challenges, values resilience, and has a passion for photography, cooking, cricket, philosophy, and psychology.
