In the world of data science and machine learning, it’s easy to get caught up in the technical intricacies and the temptation of cutting-edge algorithms. However, to truly make an impact, data teams must ensure our efforts align with the specific needs and goals of the business. In this article, I’d like to explore the importance of bridging the gap between technical solutions and business requirements. Talking specifically about a Natural Language Processing (NLP) model we built for analyzing customer reviews.

I am an engineer and a data leader from India, living in the United States. English being my second language, I am hyper-aware of how I communicate. Upon arriving in the United States, I was struck by the efficiency and clarity of native English speakers. This sense of awareness makes me think about the importance of effective communication. As a data leader, my team and I may have the best tech and data solutions, but communicating them and getting the business partners and clients on board is a critical part of my job. Without their buy-in, I might as well be just building toys in my garage and patting myself on the back!

I believe a bespoke solution for a Customer Data Platform (CDP) is worth considering. While it requires a more significant initial investment, after exploring the market leader in CDPs and going through a month of demos and discussions, I’ve learned that it is worthwhile to build a CDP in-house if you have a modern data stack.

As you build out a company’s tech towards AI, each phase generates varying returns on investment (ROIs), making it a constantly evolving process. One very exciting and tangible phase is when companies turn their data in spreadsheets into automated visual reports. This is Business Intelligence (BI) in its simplest form.