Data Management gone Agile using AI/ML
88% of the data goes untouched based on a data study conducted because it’s hard to find which information is valuable and which is best left ignored. Why? Even though the value of Data Management is well understood by enterprises, it’s difficult to master all its requirements around Data Governance, Data Modeling, Data Architecture, Data Quality, Data Integration, Metadata Management, and Master Data Management (MDM). All of these Data Management facets — take time and effort and relying on traditional manual practices doesn’t scale with growth in data.
Data Management provides quality and time-sensitive information to all business units to make decisions. Imagine a world where you are not managing Data, but a platform that takes a paradigm shift with an agile approach to help manage data smarter in days and months vs. years. All using automation and predictive analytics via AI/ML! While many organizations still struggle today to integrate and synchronize data, implementation has become burdensome, and companies often tire of funding these projects since they had to manually develop and manage across people, process, and technology – without automation. This all could change by taking an automate first approach.
As part of this session, Raj Joseph, CEO of DQLabs shall present how DQLabs.ai approaches Data management in an agile way using AI/ML.
Webinar Highlights
- Learn how Agile discipline can be used for data management given it’s at the heart of the digital transformation
- Operationalize data harvesting, cataloging, lineage, mapping, code generation, and impact analysis in a scalable manner.
- How DQLabs provides a simple, but unified experience in bringing all three components together – Data Quality, Data Catalog, and Agile Governance 2.0.
Featured Speaker
