Managing access to sensitive data is the name of the game when it comes to security and data governance. It’s required to protect sensitive data from unauthorized changes or exposure, and it’s now a mandate as part of privacy regulations such as GDPR and the California Consumer Privacy Act (CCPA). Companies all over the world are now focused on protecting sensitive PII associated with their customers and employees.

Over the last several years, as digital services and interfaces have become the primary way businesses interact with their customers, maintaining ‘business as usual’ has demanded digital transformation. In difficult times such as these, however, it may be tempting to put digital transformation projects on pause until budget surpluses return. This is a mistake.

Learn how and when to apply transformations in your data strategy

The COVID-19 pandemic has forced government organizations to reassess their strategies, plans, and aspirations for digital transformation. Despite this uncertainty, IT leaders must quickly identify and act on strategies and plans that lead to positive outcomes. In many cases, governments will expand the role of digital technologies in service and program delivery.

Evaluating anomalies and unpredicted events like pandemics and ESG concerns In part II of the series, we sat down for an interview with Dr. Richard Harmon, Managing Director of Financial Services at Cloudera, to find out more about how the industry is adopting new technology. You can catch-up and read part 1 of the series, here. Thank you for joining us for part two of our discussion around data, analytics and machine learning within the Financial Service Sector Dr. Harmon.

With only about 35% of machine learning models making into production in the enterprise (IDC), it’s no wonder that production machine learning has become one of the most important focus areas for data scientists and ML engineers alike. As you may remember, we recently announced a full set of MLOps capabilities in Cloudera Machine Learning, our cloud native machine learning tool for the cloud.

Today, we announced the acquisition of the assets and IP of Knarr Analytics, an innovative start-up that provides real-time collaboration, sophisticated data exploration and insight capture capabilities, to complement Qlik’s cloud data and analytics platform.

Depending on your projects and your QA team’s nature, your choice of automation testing tool must be compatible with many different aspects, such as project scope and requirements, in addition to its reputation. The best tool available does not guarantee the best testing outcome. It must be the right one.

In the last blog with Deloitte’s Marc Beierschoder, we talked about what the hybrid cloud is, why it can benefit a business and what the key blockers often are in implementation. You can read it here.