REA Group’s Braden Clarke is laser-focused on audience management and automated trading and using the latest adtech and martech solutions to drive brand and direct response outcomes for buyers and sellers alike.

Furthering its push to deliver user-friendly analytics across the enterprise, Salesforce on Thursday unveiled a new series of apps that are each tailored for a specific business context and role.

SAS wants to supercharge your business analysis, through new software that automatically builds multiple models of data and picks those that best predict future events.

Give a marketer a sale, and you'll keep his company afloat for a day; teach him to predict future sales, and you may just ensure his longevity.

A bumper crop of new analytics tools has arrived for business users over the past few weeks, and on Tuesday SAP rolled out two fresh examples. Both promising simplicity and usability for non-experts, SAP Predictive Analytics 2.0 offers updated capabilities for enterprises seeking insight into large volumes of data such as from the Internet of Things, while a new Edge edition of SAP's Lumira tool targets small and medium-size businesses with data-discovery features.

In a move to beef up its portfolio of analysis software and services, Microsoft is acquiring Revolution Analytics, a major commercial distributor of the R statistical programming language.

Half the money spent on advertising is wasted -- the question is, which half? That's the age-old question in adland. Now Oracle is hoping to help its customers answer that question with the acquisition of Datalogix, which collects offline consumer spending data in a bid to help online advertisers pick the right targets.

It turns out that a vital missing ingredient in the long-sought after goal of getting machines to think like humans -- artificial intelligence -- has been lots and lots of data.

Add Tibco to the list of vendors pushing a full stack of so-called "customer engagement" software, which companies use to track and analyze consumer behavior in hopes of building deeper relationships with them and ultimately, selling more products and services.

Companies are focusing more and more attention on building out big data analytics capabilities and data scientists are feeling the pressure.

Taking on Google, Databricks plans to offer its own cloud service for analyzing live data streams, one based on the Apache Spark software.

Taking what many see as the next step in big data analysis, Google is previewing a service called Google Cloud Dataflow that analyzes live data, potentially giving users the ability to view trends and be alerted to events as they happen.

SAP is scooping up SeeWhy, maker of real-time targeted marketing software, in a bid to flesh out the omni-channel commerce platform it gained through last year's acquisition of Hybris.

Microsoft is targeting the growing volume of data being generated by both machines and humans: CEO Satya Nadella on Tuesday showed off tools that could help organizations better understand -- and profit from -- this trove of information.

The article in TIME is headlined "Google's Flu Project Shows the Failings of Big Data." However, critics says the real failing here is not with big data but with Google.

While the path-to-mastery pattern is conceptually simple, successfully executing it requires courage, perseverance and patience.

Marketing organizations are continuing to increase their budgets for big data marketing initiatives, but most are focusing on technology, not talent. That could be a costly mistake.

MemSQL, a database startup founded by former Facebook engineers, has raised US$35 million in a series B round of venture funding.

Big data and analytics permeate virtually every move Ford makes, from forecasting the worldwide price of commodities to figuring out what exactly consumers want, what it will build, where it should source parts and how to power its lineup of vehicles.

CIOs must encourage data scientists to solve real business problems, not just play with data.

The buzz is all about Big Data and how best to use it to generate actionable intelligence. To do this, companies will need to hire loads of highly trained, highly paid data scientists -- or will they?