A holistic understanding of the business is key to building high-impact data products that add value.
A collaborative spirit helps you build expertise, trust, and credibility within the business to lead new data initiatives.
As industries continue to adopt new tools, it’s vital to upskill in data visualization and light coding, such as learning Python and SQL, so you can continue adding value well into the future.
It’s important to have a roadmap. Early on, everyone gets really excited about data science and the analytics, but they get caught up in a lot of buzzwords, making it difficult to get out of the proof-of-concept phase. You have to show how what you’re building is tied to a product and how it's going to be delivered to the business to add value. Identify how you are increasing revenue, cost savings, or cashflow. As finance professionals, we get really caught up in the cost of something because we try to make the budget work, but if you look at what you’re building as an investment, instead of as a cost, and showcase what you’re delivering, you can be successful.
Understanding how everything is connected is really important. If you can take the data you receive from the business and learn how it's being used and the system it’s coming from, then you can utilize it to generate reports on any key metric you want. It allows you to go deeper into the data, really understand what it means, and build something that's usable for the business. That's what it's all about. We don't want to build something for it to die on the vine. And that holistic understanding really comes through collaboration. I mean, I think a key strength of finance and a key ingredient to success is the collaboration and building bridges between all the various functions in your organization.
Google Cloud for Data Scientists: Harnessing Cloud Resources for Data Analysis
The Top 8 Business Analyst Skills for 2024
A Guide to Docker Certification: Exploring The Docker Certified Associate (DCA) Exam
Bash & zsh Shell Terminal Basics Cheat Sheet
Functional Programming vs Object-Oriented Programming in Data Analysis
A Comprehensive Introduction to Anomaly Detection