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Upcoming Webinars

Aug 21 Webinar: The Future of BI isn’t a BI Tool

DATE: August 21, 2019 TIME: 2 PM Eastern / 11 AM Pacific PRICE: Free to all attendees This webinar is sponsored by: About the Webinar Business data has changed radically. Enterprises today use thousands of SaaS applications and business systems that create more data than ever imagined, resulting in a…

Aug 22 DAS Webinar: Data Quality Best Practices

DATE: August 22, 2019 TIME: 2 PM Eastern / 11 AM Pacific PRICE: Free to all attendees About the Webinar Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these…

Sep 4 DGPO Presents: Sutter Health's Data Governance Journey

DATE: September 4, 2019 TIME: 2 PM Eastern / 11 AM Pacific PRICE: Free to all attendees This Webinar Series is Developed by: About the Webinar Amber will share Sutter Health's data governance journey over the past five years, including:      Strategies for Healthcare Organizations to support a Governance…

Latest Blogs by Industry Experts

Five Capabilities to Look for in a Metadata Management Solution

Click to learn more about author Mike Brody. Big Data keeps getting bigger. We’re generating so much information these days that we’re running out of sustainable places to put it and have begun tinkering with the idea of storing it in space. Yes, outer space. With this much information on our hands, it’s no wonder the enterprise Metadata Management market is projected to hit USD 9.34 billion by 2023. If there’s…

The Future History of Time in Data Models

Click to learn more about author Thomas Frisendal. Timely Concerns in Data Models In June I published a blog post called Timely Concerns in Data Models. In summary the concerns that I mentioned in June were: Roles of time (such as Valid Time, Recorded Time, As-Is vs. As-Of, Read timelines, Time Series), The scope of the time vs. the functional dependencies and agile schema evolution. I followed this up in…

The Allure of the Cloud for Data Lakes

Click to learn more about author Sanjay Vyas. Many organizations are considering whether it’s the right time to shift from an on-premises data lake to a cloud data lake. From scalability issues to software incompatibility and security, on-premises data lakes haven’t delivered on the promise to give organizations fast and unfettered access to all their data from any source. Are cloud data lakes the answer? For many organizations, the answer…

Case Studies

What is...?

What is Data Curation?

Data Curation, as defined by The University of Illinois’ Graduate School of Library and…

What is Ontology?

Ontology is often considered a subset of taxonomy. An ontology: Is a domain; contains…

What is Taxonomy?

Taxonomy represents the formal structure of classes or types of objects within a domain.…

Most Recent Articles

A Brief History of Master Data

Master data is generally described as essential business data about people, places, and things. Master Data Management or MDM describes a system for managing the data, which is different than the actual data. Master data is not typically transactional data, but in some situations transactional data can be treated as master data. For instance, if information about products, suppliers, and vendors is contained only within the transactional data (orders and…

Artificial Intelligence and Enterprise Knowledge Graphs: Better Together

At the heart of things, an enterprise knowledge graph supports decision and process augmentation based on linked data. These models of knowledge domains are created by subject matter experts with the help of machine learning algorithms. They live as virtual data layers on top of existing databases or data sets to link both structured and unstructured data at scale and across disparate data silos. Introduce new data and the graph…

So You Want to be a Machine Learning Engineer?

Ideally, a machine learning engineer would have both the skills of a software engineer and the experience of a data scientist and data engineer. However, data scientists and software engineers usually come from very different backgrounds, and data scientists should not be expected to be great programmers, nor should software engineers be expected to provide statistical summaries. Nonetheless, a background in machine learning algorithms and how they can be implemented…

Data Governance and Data Quality Use Cases

Given the high volume of erroneous data floating across the enterprise, it is increasingly difficult for industry operators to make business decisions or plan any course of action based on such poor-quality data. Moreover, what’s the point of applying advanced analytics or BI technologies on data that is flawed? A whitepaper titled Getting Ahead of the Game: Proactive Data Governance reviews the role of Data Quality (DQ) in an enterprise…

The Internet of Things: Architecture, Edge Computing, Use Cases, and the Future

The Internet of Things (IoT) is not, by itself, a technology. The IoT is the “concept” of connecting electrical devices to the internet. This includes smartphones, washing machines, coffee makers, lamps, and wearable devices. The IoT also applies to the components of machines, such as jet engines or the drills of oil rigs, and is used by smart cities and manufacturing plants. The use of the IoT is also changing…

Data Governance and Data Science: What is the Intersection?

With the rising concern surrounding the manipulation of data and misuse of statistical methods in Data Science, it is becoming imperative that strong Data Governance policies and practices are put in place to curb any degeneration of data and the scientific methods used to arrive at data-driven conclusions. Data Governance (DG) is expected to play a key role in future Data Science (DS) practices as it offers phased, validity checks…

Latest Industry News

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