When a data breach occurs involving a cloud service, the impulsive reaction is to denounce using the cloud (at least for sensitive information). Since cloud security is not widely understood, it may be difficult to delineate it in the context of more general information security.

Machine learning is a branch of Artificial Intelligence that makes it possible for systems to learn without being programmed. Applying this technology to real world Marketing is often misunderstood and is within your reach.‍

Today, Unravel announced a new cloud migration assessment offer to accelerate the migration of data workloads to Microsoft Azure, Amazon AWS, or Google Cloud Platform. Our latest offer fills a significant gap in the cloud journey, equips enterprises with the tools to deliver on their cloud strategy, and provides the best possible transition with insights and guidance before, during, and after migration. Full details on the assessment and business value are detailed below in our announcement below.

Great to hangout with Noah Learner and Jordan Choo and talk about automation in agency life, with some thoughts around Machine Learning thrown in there too.‍

AI and its enabled tools continue to enthrall business with its promise of efficiency and innovation. But, one of the things AI is also clearly enabling is the bias. We’ve all read the news and heard the scaremongering stories around potential flaws and biases in Artificial Intelligence systems. I believe for this technology to reach its full potential, addressing bias will need to be a top priority.

Talend Cloud is an Integration platform as a service (iPaaS) offering by Talend. It is a fully managed cloud option which has the capabilities of data integration, data stewardship, data preparation, API designer and Tester and Pipeline designer. These tools can be used for lightweight ETL and detecting the schema on the fly. One of the unique features of Talend Cloud is it provides both on premise and cloud execution environments.

We know that data is a key driver of success in today data-driven world. In fact, according to Forrester, data and insight-driven businesses are growing at an average of more than 30% annually. However, becoming a data driven organization is not easy. Companies often struggle with speed in accessing and analyzing their data, as well with ensuring delivery of trustworthy data that is free of critical errors.

As machine learning continues to address common use cases it is crucial to consider what it takes to operationalize your data into a practical, maintainable solution. This is particularly important in order to predict customer behavior more accurately, make more relevant product recommendations, personalize a treatment, or improve the accuracy of research.