How many statisticians do you need to build a new data model? Zero, according to Tableau Software: It says the next version of its widely used analytics tool will do it itself. Tableau demonstrated this, and a new feature called Ask Data that allows users to create visualizations by describing what they want in natural language, at an event for customers in New Orleans last week. It also showed off new automation functions in its data preparation tool. Read article…

Each of the big three cloud providers—Amazon, Microsoft, and Google—recently announced earnings, with Amazon Web Services (AWS) and Microsoft Azure each touting impressive revenue gains. (Google was largely silent on its Google Cloud Platform business.) For AWS, cloud revenue grew 46 percent to put the company on a $27 billion run rate. Microsoft’s growth slowed to 76 percent, but that’s on an estimated $7.7 billion run rate in 2018, with the company’s hybrid cloud story selling well to enterprises. Read article…

Knime is a highly rated data analytics platform with wide applicability and many integrations with other products, such as with databases, languages, machine learning frameworks, and deep learning frameworks. The philosophy of Knime is to be inclusive and “blend” whatever software and data sources you want to use. Read article…

The data team structure that your company has used for a few years needs to turn its focus to results and putting analytics tools in the hands of business units. Read article…

Unfortunately, some tech organizations are deceiving customers by proclaiming using AI on their technologies while not being clear about their products’ limits. Robert Iriondo helps explain the difference between AI and Machine Learning. Read article…

The growing reliance on AI and machine learning in the coming decades can have a negative impact on many jobs while raising new social issues. Yet, businesses can minimize those impacts. Read article…

Continued broad development of AI technologies and concepts require new approaches to collaboration between industry organizations and academia. Read article…

Data lakes have helped organizations deal with the massive amounts of data generated daily. They are intended to serve as a central repository for raw data, a treasure trove for data scientists to analyze and gain actionable insight. They also serve as the foundation for many "self-service" analytics initiatives. While getting data into a lake is simple, getting insight and value from all of that data, however, has proven to be challenging for many organizations. A recent Forrester report found that 60%-73% of all enterprise data goes unused for analytics. This statistic exposes some of the harsh realities of data lakes. Read article…

Rob Mandeville asks as we enter a world of machine learning and data science, are there any gotchas or negatives? It sounds as if it is all sunshine and rainbows, but, as the title to this post alludes, there are. Read article…

There is no shortage of content flowing through today's enterprises, including data, documents, graphics, videos, and more. This is raw material that provides a wealth of opportunities, many of which are untapped, to businesses. The catch is that this data and content is scattered across various systems inside and outside of enterprises. Plus, there is not enough understanding of who views which content, and what is motivating them to consume the content. As more organizations seek to embrace digital transformation, they are turning to content automation as a way to deliver information quickly and effectively to their customers. Read article…