Google Analytics Update, Ex-Watson Exec Launches Startup: Big Data Roundup

11 Tech Jobs That Pay The Most: Glassdoor

There's big news in big data this week from several players, including Google, Zendesk, and others, plus some insight on big data trends from Forrester Research. We'll also take a closer look at the frenzy of predictions in the celebration of college basketball that is March Madness.

But let's start with Google. This week the company introduced Google Analytics 360 suite, a bundle of data analytics products for enterprise marketers. The suite aims to help digital marketers grapple with changes in the landscape as we have moved from capturing Web traffic and interaction data to capturing data from a host of mobile devices as well. The suite includes Analytics 360, Tag Manager 360, Optimize 360 (beta), Attribution 360, Audience Center 360 (beta), and Data Studio 360 (beta).

Is it a coincidence that Google announced this analytics suite the week before the Adobe Summit 2016, where Adobe is expected to release its own Web and mobile analytics marketing updates? Probably not. We'll bring you Adobe's response to the Google announcements from the Summit next week.

Big Data Startups to Watch

While there are big companies in big data and analytics, such as Google and Adobe, there are also many startups looking to solve problems and build on a market that is growing quickly. With that in mind, InformationWeek published a list of 9 Hot Big Data Startups to Watch. Is this a comprehensive list? Not at all. So if there are companies you think should be on this list but aren't there, please add them in the comments to the story. Meanwhile, check out that Who's Who of startups in big data right now.

Ex-IBM Watson Exec Surfaces at AI Startup

Stephen Pratt who joined IBM to lead its Watson Global Business Services effort, but then left nine months later, in February 2016, has surfaced again, this time leading a new startup with funding from TPG Growth, an investment firm that had employed him as a consultant.

Pratt will serve as CEO for the startup called Noodle.ai, an artificial intelligence firm. The company's website says it "combines the hard sciences of AI technologies and business process optimization with the arts of organizational change and design thinking -- all optimized to help you succeed quickly and affordably."

Enterprise Priorities for Big Data: Streaming, Self Service

We also have a report about how businesses are taking the next step with big data, moving from simply putting the infrastructure in place to the next phase of developing capabilities in order to leverage real-time streaming analytics and embed self-service capabilities into the very fabric of their operations. The report comes from Forrester Research, which looked at enterprise plans for implementing the technology, and compared them to their strategies a few years ago.

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Zendesk Leverages Machine Learning

This week also, SaaS-based customer and IT service desk provider Zendesk announced plans to incorporate machine learning into the customer experience. The company is launching Satisfaction Prediction, a machine learning and predictive analytics feature for customer satisfaction available to Zendesk customers on the Enterprise plan.

Zendesk said that the system can predict how likely a ticket is to receive a good or bad rating, which can help organizations take action to ensure positive outcomes. The feature reads and transforms text description, the number of replies, and total wait time into a model that predicts customer satisfaction. Agents can use this to prioritize workflows, drive business rules, and trigger downstream integrations based on the analysis, the company said.

March Madness

The annual college basketball frenzy has inspired many a betting pool, and so there's no shortage of articles that look at predicting the outcome of this event based on data. On the FiveThirtyEight website alone there are multiple takes.

For instance, late NCAA bracket submissions show we are a nation of procrastinators, according to one post. Another post provides the meta on how FiveThirtyEight came to its conclusions about its predictions in the tournament.

Another one from a different site explains how to clear up March Madness with predictive analytics, and one illustrates the process of creating a March Madness bracket using machine learning. Let us know what approach you take in the comment section.