Data Management for Advanced Analytics
Webinar Speaker: Philip Russom, Senior Research Director for Data Management
Date: Monday, July 27, 2020
Time:10:00 a.m. BST
High-value business goals require advanced forms of analytics, which in turn demand use-case-appropriate data management.
We say “analytics” as if it’s a single entity. In reality, it is a collection of techniques, including data mining, text mining, clustering, statistics, graph, artificial intelligence, machine learning, self-service, visualization, and so on. Each of these has its own methods, use cases, analytics tools, and—especially—data requirements. For example, the dimensional modeling required of OLAP differs from the light standardization required of self-service. In machine learning, even learning data, training data, and production data are integrated and managed differently.
Satisfying the diverse requirements of data management for advanced analytics (DM4AA) is the leading driver behind data warehouse modernization, the adoption of self-service analytics (data prep, visualization), the deployment of new data platforms (Hadoop, NoSQL, clouds, lakes), multiplatform hybrid data architectures, and—of course—successful analytics.
In this TDWI webinar about DM4AA, you will learn about:
- Business and technology drivers, trends, key business use cases
- Benefits and barriers, plus how these affect user planning and tool choice
- Market statistics about user practices, perceptions, maturity
- Tool and platform requirements for common DM4AA use cases
- Role of Hadoop, open source, cloud, and other new data platforms or tools
- Impact on cloud and hybrid architectures for analytics systems and data
- Organizational matters such as governance, staffing, sponsorship, ownership
- In-depth look at priority use cases for analytics (both old and new), with a focus on data management requirements (both on premises and in the cloud)
Philip Russom, Ph.D.