Data Literacy

TDWI research finds that data democratization—empowering a broader spectrum of people with self-service data access, exploration, preparation, and analytics—is a top priority. Self-service business intelligence and analytics are central to transforming daily operational decisions as well as forecasting and higher-level strategies.Today, citizen data scientists are pushing beyond data consumption to perform more advanced data discovery and predictive analytics. Developers are upgrading business applications by embedding visualization and analytics capabilities. Artificial intelligence and...

How to liberalize data access to empower data users.

To compete in today’s dynamic environment, organizations need to modernize their data and analytics environments.This modernization includes implementing new technologies such as scalable cloud platforms and unified approaches. It includes more advanced analytics such as geospatial analytics and machine learning. It also includes new paradigms such as the data fabric and the data mesh.Modernization may include new organizational constructs such as the data office and new teams such as DataOps, MLOps, and data literacy enablement teams.This TDWI Best Practices Report provides new research...

Data is valuable, no doubt, but it is useless if you and your organization do not know how to unlock its value. Organizations need to have a strategic approach to collecting, curating, and consuming the data. A strategy that combines technology with people; after all, it is the individuals who use the databases, data warehouses, and analytical and reporting applications to convert the data into valuable information needed to run the business better (than the competition). In other words, organizations need to create a data culture that is enabled by literacy and self-service. But how do you...

Presented at DAMA Day NYC 2021It is no secret that our data landscape has continuously evolved and will never be the same. As businesses are getting transformed to embrace digital channels as their primary medium of customer and supply-chain interactions, the vast landscape of heterogeneous data sources, data management architectures, and evolving new technologies make it challenging for data architects and analysts to harvest value from their data. While businesses grasp the reality and the need of generating value from our data assets, we still continue to spend valuable resources on data...