Here are some ideas on what will be driving the data analytics and BI conversation in 2022 and beyond: Top Five BI, Data and Analytics Predictions for 2022 | bipp Analytics (see the article for more details on each of the following items.
1) The Data-Driven Company is Dead – Long Live the Culture of Analytics
True benefits of analytics and BI come from a cultural change. Give people access to tools on their terms by embedding dashboards in the intranets or apps they know. Create trust by ensuring everyone uses the same language to represent critical KPIs and clean data. And combine hands-on training with a platform that can scale your business, recognizing the cultural shift required to take enterprise-wide advantage of BI.
2) Data Modeling Layers Bring Self-Service (BI) Power to the People
Self-service business users can make decisions based on the same trusted logic as the same language represents critical KPIs. For example, they can create dashboards, trust their visualizations and easily filter them in real-time. Which means they’re making decisions based on the latest, real-time information.
3) The Ayes (Eyes!) Have IT
In an age of no-code/low-code, self-service tech, we’d better embrace the vision-first world of BI. Also, we need visual SQL tools that let blend data and create charts from disparate sources without going through an ETL pipeline. Using an intuitive, flexible drag-and-drop interface.
4) The Revolution Will be Augmented
The augmented analysis takes critical business metrics and lets the platform explore millions of combinations, determine the highest impact, reveal these as facts, and prioritize them in order of importance. All without needing to understand a query language, such as SQL.
5) The Year of the Data Engineer
Data engineers have to bolt and chain tools together with code as they strive to simplify the data stack. The data engineering skillset is ideal for a business-critical technical problem. Every business must enable teams with the best tooling while maintaining a unified, flexible data layer. Engineers will need to architect and operate data stacks that solve these problems and be responsible for machine learning, analytic reporting, and decision management.