Unlike the NBA, you can't easily pick and choose the top "plays" of 2021 in the data analytics field like you can with basketball games. This list is by no means exhaustive and is just based on my reading of news and trends in the data analytics world. It's easy to talk about big changes to tool and platforms that occurred throughout the year, but I'll try my best to focus on actual job or organizational trends that affect the data analytics profession. The bigger swag will be my predictions for 2022. They might be just a continuation of trends from 2021 or heck...even 2011. Certain things won't change much (like the use of Excel at work) but who uses data and in what forms will constantly change. What happened in 2021? 1. The "Analytics Engineer" job title While a data analyst spends their time, well, analyzing data, the analytics engineer helps prepare data for data analysts. The analytics engineer is similar to a data engineer in many ways, but is a bit closer to the business. It's probably common for an analytics engineer to become a business analyst and vice versa. I think the organization that put this role on the map is dbt (read more here). I used to think an enterprising data or business analyst did the job of an analytics engineer. This may still be true at smaller companies. In most large organizations, I think there is a clear separation now between those who transform data and those who analyze the data. Take a listen to episode #58 with Krishna Naidu of Canva to see how analysts at Canva need to think more like software engineers; further showing why the analytics engineer role is here to stay. A good video on what an analytics engineer does: https://www.youtube.com/watch?v=ixyzF4Dy9Us 2. Democratization of datasets and visualizations I remember working in a role where one of the head business analysts was tired of getting ad-hoc requests to see customer data sliced and diced different ways. This would require the analyst to write SQL queries to answer some C-suite executive's question. While these questions are important for making business decisions, these one-off questions and subsequent SQL writing was not sustainable. The business analyst ended up organizing a 1-hour session for all his business stakeholders to do a crash course on SQL and the data warehouse so the executives would feel empowered to pull the data themselves. Can you guess what happened during that training? Eyes were glazed over, people ended up answering emails on their laptops, and the business analyst was still fielding one-off questions after the training. With the data visualization tools enterprises use today, the C-suite doesn't need to run their own SQL queries anymore. A basic understanding of filtering and sorting means charts can be manipulated to answer the questions you have for your data. Business and data analysts are no longer the bottlenecks for a dataset. Data is permissioned up and down the organization and access is granted as long as you have a business need for the data. I would take a listen to episode #57 with Nadja Jury of Education Perfect to hear how data analysts partner with business stakeholders to deliver the exact data stakeholders need.