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 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 […]
Data Governance and Data Architecture: There is No Silver Bullet
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 […]
Data Modeling in the Machine Learning Era
Machine learning (ML) is empowering average business users with superior, automated tools to apply their domain knowledge to predictive analytics or customer profiling. The article What is Automated Machine Learning (AutoML)? discusses a prediction that by 2020, augmented analytics capabilities will play a key role and be a “dominant driver” in the growth (and purchase) […]
Developing a Functional Data Governance Framework
Data Governance practices need to mature. Data Governance Trends in 2019 reports that dissatisfaction with the quality of business data continues in 2019, despite a growing understanding of Data Governance’s value. Harvard Business Review reports 92 percent of executives say their Big Data and AI investments are accelerating, and 88 percent talk about a greater […]
Data Quality, Data Stewardship, and the Omnichannel Customer Experience
What organization—from financial services firms to retailers to healthcare providers—survives without customers? Whether they’re called consumers or patients, retaining existing or acquiring new clients is critical. That means not taking customers for granted, but rather treating them as individuals who can enjoy personalized experiences during interaction. An estimated $62 billion is lost by U.S. businesses each […]
Unifying Big Data Workloads
Try querying Big Data sets and computing results through high volumes and variety across multiple independent storage systems – you’ll find a tangled web in the Tower of Babel, where platforms communicate in different languages. Then ask for speedy manipulations with that data set and it seems almost impossible. This describes the challenge faced by […]
Data Governance vs. Master Data Management
“Data Governance is the creation of rules, the execution of those rules, and the adjudication of any violation of the rules,” remarked Frank Cerwin in a recent DATAVERSITY® interview. He likened its structure to the three branches of the government: “The legislative branch makes the laws, the executive branch executes the laws, and the judicial […]
Embracing Data Silos: Semantic Search and Analytics Innovation
Walk around any large organization and hear people groan about finding the right data to do their work. In the typical organization, data sits in multiple places, lost behind technical and functional boundaries. These isolated systems, referred to as “data silos,” have often existed for good purposes and reasons such as helping each business function […]