Extract, Transform, and Load (ETL) technologies, managed exclusively by IT, have until recently been the primary tool used to combine data from multiple sources and thus provide the ability to drive important business decision making for organizations. But, with the advent of self-service data preparation, business users and subject matter experts (SMEs) can find those insights on their own. “In some organizations, each data engineer/IT resource is expected to support…

As the available industry literature suggests, Data Quality (DQ) is seriously hindering the success of many Big Data projects. However, it is apparent that without an organized movement in favor of Data Quality for Big Data, nothing will actually happen to change the current scenario. In an interview with the CEO of Informatica, Anil Chakravarthy, reveals how the lack of DQ can inhibit innovation in information technology. The two major…

To view just the slides from this presentation, click HERE>> Original Air Date: June 18, 2019 This Webinar Sponsored by: About the Webinar With changes in software development methodologies, the role of the data modeler has changed significantly. In many organizations, data modelers now find themselves on the outside looking in, relegated to documentation “after the fact” rather than active participation in database design where the true value is added.…

The graph database, very simply, is a database that recognizes the “relationships” between data to be as important as the data itself. A graph database is designed to hold data while not limiting it to a pre-established model. The data in such a database shows how each individual entity is connected with or related to others. A graph database “natively” embraces relationships while other databases compute relationships at the time…

The “deep” in deep learning refers to the number of hidden layers involved in the design. Deep learning is a way of training artificial intelligence (AI) to recognize specific data, such as speech or faces, and to make predictions based on previous experiences. Unlike machine learning, which organizes and sends data through predefined algorithms, deep learning develops and uses basic algorithms to screen the data, and then trains the AI…

In terms of a market perspective, Data Governance has increased in visibility partly because of the increase in security breaches, data security issues, and compliance requirements for various industry regulations. To secure and manage data properly, it helps to manage it at a higher level and know which of your data is sensitive in the first place. Having the foundation of a solid Data Architecture is fundamental, because without it,…