Whether the enterprise uses dozens or hundreds of data sources for multi-function analytics, all organizations can run into data governance issues. Bad data governance practices lead to data breaches, lawsuits, and regulatory fines — and no enterprise is immune.
Our valued partner, Deltek is a leading provider of software and information solutions for project-based businesses. Headquartered in Herndon, Virginia, the company leverages its expertise to maximize efficiency and revenue for clients through project intelligence, management, and collaboration.
Feature engineering is a crucial part of any ML workflow. At Continual, we believe that it is actually the most impactful part of the ML process and the one that should have the most human intervention applied to it. However, in ML literature, the term is often overloaded among several different topics, and we wanted to provide a bit of guidance for users of Continual in navigating this concept.
Learn methods for ensuring that data replication remains robust even as schemas change.
Tell us if this analytics scenario sounds familiar: your organization employs an analyst team that uses old technology, desktop data visualization tools, or homegrown reporting systems to manually build out static dashboards and multiple weekly or monthly reports. As a result, the data analysts have to manually generate and update business reports regularly, and teams cannot keep up. In turn, the rest of the organization is unable to make timely business decisions.
How to improve ROI on social media ads as regulations and technology change.
