Data management and analytics best practices have their roots in the manual processes of data quality, discovery, preparation, modeling, governance, visualization, and explanation. However, as the scale of data and analytics continues to expand, it is not humanly possible to keep up with increasing volume.
To address this challenge, a new category of tools is emerging: AI-enabled data management tools. Analytics and data management vendors are using machine learning and artificial intelligence to automate processes that are tedious and repetitive. The most advanced modern data platforms now utilize algorithms to find links between data sets, automate data preparation, or find breaches in data governance. Business intelligence and analytics platforms now run machine learning algorithms to explore, suggest, and explain new insights relevant to each user. All of this is possible because of the creation and collection of rich metadata to automate formerly manual data processes.
In this session, you will learn how mature organizations use AI-enabled data management and analytics tools to speed analytics delivery, widen project scopes, and drive more innovation. We will also look at strategies used by mature organizations to gain advantages over their less-developed competitors by increasing the productivity of their data engineers, architects, modelers, stewards, analysts, and scientists.
You Will Learn
- How data management and analytics platforms are using machine learning to automate different processes
- How mature organizations view and adopt AI-enabled data management and analytics platforms to increase speed and scale
- Business and technical drivers for the use of AI-enabled data management to speed data ingestion, cataloging, modeling, quality, and analysis
- Cultural and organizational impact for the use of machine learning and automation techniques in data and analytics
- Best practices for the use of AI-enabled data and analytics tools to create business value and impact the bottom line of your organization
- IT leadership; data warehouse leaders; analytics leaders; systems architects, data engineers, and developers