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Today, we measure the size of the Web in exabytes and are uploading to it 15 times more data than we were 3 years ago. Technologies for sensing, storing, and sharing information are driving innovation in the tools available to help us understand our world in greater detail and accuracy than ever before. The implications of analyzing data on a massive scale transcend the tech industry, impacting the environmental sector, social justice issues, health and science research, and more. When coupled with astute technical insight, data is dynamic, accessible, and ultimately, creative. Marissa Mayer will speak to the power of data and the role it plays in Google's innovation. She will present on the technology trends that are changing our relationship with data, discuss fresh Google products that creatively put data to work, and offer her vision for the future of data in driving the Web forward.

Data alone isn't the answer. In fact, from a business perspective, the data is still part of problem. Insight is the answer, which is derived from the data.

But the bigger point is, adding more, independent data usually beats out designing ever-better algorithms to analyze an existing data set. I'm often suprised that many people in the business, and even in academia, don't realize this.

There are plenty of reasons to be skeptical about the BD market. But big data probably deserves a place in overall enterprise IT strategy for generating business insight. Best practices include generating a list of important challenges or questions that the current approach to data does not address. Could big data deliver the answers enterprises are looking for? If so, then it's all about discipline. A disciplined, targeted approach to big data - one focused on answering very specific questions. (my emphasis - I'll get to those later)

Linked Data is about using the Web to connect related data that wasn't previously linked, or using the Web to lower the barriers to linking data currently linked using other methods.

The plan unveiled on Thursday designates money to create a "dashboard"-an interactive, online database-to give students, parents, and legislators access to detailed measures of departments' and colleges' productivity and efficiency. Data on individual professors will probably also be included, although Dr. Cigarroa stressed that each campus would be able to develop its own system of metrics and the details have not yet been worked out

The majority of the information that we use in our daily lives is "dumb", or unconnected. The next step is "linked data", or data that talks to each other.

The availability of new sets of data has changed the way we live our lives: here are 10 examples of data which have changed everything from how we assess wars to how companies deliver milk.

Which brings us to the underlying point of the piece - we are all leaving digital trails behind us, as we make our way around our individual lives. These trails are largely considered individual - even ethereal - yet technology is making these trails more visible and more readable everyday.

Over the last year, I've produced five data-driven pieces for Wired UK. Four of them have been for the two-page infoporn spread that can be found in every issue. I've looked at the UK's National DNA Database, used mined Twitter data to find people's travel paths, and mapped traffic in some of the world's busiest sea ports.

Last week ReadWriteWeb asked: "Is Linked Data Gaining Acceptance?" Our answer: definitely yes. Projects like DBPedia, a community effort to structure the information from Wikipedia and provide it as Linked Open Data, have come a long way and work really well. For example, you can search for all scientists born in Zürich, Switzerland.

Demdex captures behavioral data on behalf of Websites and advertisers and stores it in a "behavioral data bank." The company then score each user across more than 40 behavioral and demographic variables to come up with a "traitweight," which helps websites segment their audiences better and advertisers target their messages more exactly.

Infochimps receives thousands of visitors every week hungry for interesting data. Unlock the value of your data by selling it on our open platform. Or put it up for free for public benefit, and get exposure for your work.

Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning. EDUCAUSE's Next Generation learning initiative offers a slightly different definition "the use of data and models to predict student progress and performance, and the ability to act on that information". Their definition is cleaner than the one I offer, but, as I'll detail below, is intended to work within the existing educational system, rather than to modify it. I'm interested in how learning analytics can restructure the process of teaching, learning, and administration.

Data analytics is shorthand for the method of warehousing, organizing, and interpreting the massive amounts of data accrued by online learning platforms and student information systems - now as elemental to higher education as classrooms and filing cabinets - in hopes of learning more about what makes students successful, then giving instructors (and the platforms themselves) the chance to nudge those students accordingly.

We've written a series of how-to guides explaining how we collected the data. Most of the techniques are within the ability of the moderately experienced programmer.

20 years ago, Tim Berners-Lee invented the World Wide Web. For his next project, he's building a web for open, linked data that could do for numbers what the Web did for words, pictures, video: unlock our data and reframe the way we use it together.

Data is present in all the systems and processes in the world. IBM helps analyze this multitude of information to make intelligent decisions, while enabling business efficiency and adding value to many industries.

We thought it would be useful to propose one possible taxonomy - we call it the Snice* taxonomy - of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret (or, if you like, OSEMN, which rhymes with possum).

Needlebase allows you to view web pages through a virtual browser, point and click to train it in understanding what fields on that page are of interest to you and how those fields relate to each other. Then the program goes and scrapes the data from all of those fields, publishes them into a table, list or map, and recommends merges of cells that appear to be mistakenly separate. It's very cool and it lets non-technical people do things with data quickly and easily that we used to require the assistance of someone more technical to do.