The axiom goes that the difference between high-performing enterprises and lower-performing businesses is that the former maximizes their data and the latter doesn’t. But most businesses probably still live somewhere between those two worlds. That means a lot of companies are still working hard to get to the next level for IT-supported business initiatives including […]
Case Study: Department of the Interior Lays Out Steps for Metadata Implementation
After the U.S. Office of Management and Governance issued the Open Data Policy, federal agencies set to the task of developing Enterprise Data Inventories to support a mandate of government transparency. The Department of the Interior (DOI) took this opportunity to create and implement a Metadata Management framework using an enterprise approach, properly documenting data […]
The Next Level of Data Governance, Machine Learning, and Data Science
The term, machine learning dates back to a 1959 article by Arthur Samuel, in which he posited: “Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.” The hope was that data alone could be used to develop models, rather than relying on fixed rules or theory. […]
Big Data Ecosystem Updates: Machine Learning, Deep Learning, and the Edge
One of the recent stories within the Big Data ecosystem is that Cisco is joining the AI Hardware frame with a new deep learning server powered by eight GPUs. Cisco is promising support within its AI push for Kubeflow, “which is an open source tool that makes TensorFlow compatible with the Kubernetes container orchestration engine,” […]
The Challenges of Data Quality
The Data Quality and Data Management market is going through a paradigm shift where the focus has turned to the business user. Historically, business users have been at the mercy of over-burdened IT departments with limited resources, but IT is not to blame. Even with the simplest query, the answer used to be “It’ll be […]
So You Want to be a Cloud Engineer?
It has been predicted that “cloud engineer” will be among the top ten in-demand IT jobs in 2020. There is currently a great need for cloud engineers, primarily because a significant number of organizations are moving their business processes to the cloud. As more organizations shift to cloud data storage, the demand for cloud engineers […]
ETL vs. Data Preparation
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 […]
Impact of Data Quality on Big Data Management
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, […]
IDERA Webinar: Lean Data Modeling for any Methodology
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 Third Generation of Graph Databases
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 […]