ReadWriteHack

The Power View tool is meant to be run remotely, including from a service linked to a SQL Azure cloud-based database, so this is indeed a Web application. Technically, it may run from any browser that supports Silverlight. As with charting in Excel, you point Power View to your source and adapt the visualizations to best suit how that data may be explained to a viewer, and you do so without impacting the data itself.

One new tool called the slicer, added to the project during its public preview phase (proof that Microsoft can indeed incorporate good suggestions during a public preview), lets the user select a segment of data in a table to pull out and either highlight in the context of the bigger chart, or demonstrate within a separate graph. Then by creating what Power View calls cards, you can take a record about one of the items depicted in a chart - for example, one of the factors that the chart is comparing, like HP's share price to Dell's or Angus cattle prices compared to Hereford - and generate what Metro would call a "tile" for that item.

The business of information technology has made verbs of many nouns, not the least of which is "siloing." On the one hand, workers in an enterprise tend to operate against their own interest when they continue to do their business from disparate silos. On the other, corporations that actively try to strike down their silo walls often find themselves dealing with information chaos.

Many vendors have characterized the emerging field of Big Data as a revolution, a collapse of the Berlin Wall-like structures that collect businesspeople into separate enclaves. You might be surprised that IBM isn't one of them. Okay, so silos are bad, says IBM's vice president for big data, Anjul Bhambhri, in part 2 of our ReadWriteWeb interview. But you can't expect database re-architecture to provide you with freedom, and in some cases, there are good reasons why enterprises are departmentalized in the first place.

Just a few short years ago, the problem of database size scaling to colossal capacities that exceeded the scope of entire network storage units, seemed insurmountable. Today, it's practically under control, with a wealth of open source technology emerging not from database engineers but rather from Internet architects. Hadoop has transformed the very nature of transformation, becoming one of the most readily adopted technologies in the history of the data center.

But is it mature? And will businesses have access to the right people with the skill sets necessary to master this new aspect of information management? After having spent five years as a senior engineer at Sybase, another six years as a development director at Informix, and over three years managing DB2 development for IBM, Anjul Bhambhri is arguably one of the most skilled plain data architects in the business. In September 2010, IBM promoted her to the new post of Vice President for Big Data and Streams. In an interview with ReadWriteWeb, we asked Bhambhri whether the big data tools developed in so short a time are mature enough to be used by IT workers everywhere, or whether they will truly require a scientist to master.

In enterprises everywhere, including even the largest ones, the transition to cloud-based architectures has brought a new class of managers into the computing process. Suddenly, personnel managers and folks whose purview had been limited to finance and personnel, are being doubled-up with oversight roles for cloud deployments. The back office is no longer in the back (or the basement), and now these new managers are wondering: What is all this we're dealing with?

Donna Burbank - who's a senior director of product marketing for CA Technologies' long-time data visualization tool, ERwin, has a new phrase for this class of customers: business sponsors. "When I talk to our customers, they tell me it's a whole new... thing, for lack of a more technical word. They've heard of SQL Server, but what is this SQL Azure thing? They don't have the skill sets, and may be nervous about that. These business sponsors might not be moving the information, but they want to see it. And they don't want to look at those database scripts. They want to look at something they can understand."

toxiclibs is a library of computation design tools built in Java and Processing. The classes can be used for a variety of purposes, including, "generative design, animation, interaction/interface design, data visualization to architecture and digital fabrication, use as teaching tool and more."

toxiclibsjs is a translation of this library into JavaScript. It doesn't depend on any external libraries or frameworks, but works with Processing.js, Three.js and Raphael.js among others.

R, the statistical programming language, continues to grow in popularity. A recent poll at KDnuggets found that 34% of respondents do at least half of their data mining in R. Although it's a domain specific language, it's versatile. Here are three different presentations, each on a different aspect of R.

Protovis is a data visualization tool from the Stanford Visualization Group. We mentioned it last month when we covered the group's Data Wrangler project.

Rubyvis is a port of Protovis, which uses JavaScript, to Ruby. Both use SVG for output.

Pattern is a collection of open source (BSD license) web mining modules for Python from the Computational Linguistics and Psycholinguistics Research Center. It contains tools for data retrieval, text analysis and data visualization and comes with over 30 sample scripts.

What We Pay For is a simple website that lets you enter your income and filing status and find out how your tax dollars are being spent. It breaks down the amount you're likely to pay in taxes by various spending categories, such as Social Security, national defense and Medicare.

The developers behind What We Pay For have released an API for the service, and Google and the non-profit organization Eyebeam are sponsoring a contest for visualizations based on the site's data.

Today at the Strata conference The Stanford Visualization Group debuted a Web-based visual tool for cleaning up messy data called DataWrangler. According to its website, "Wrangler allows interactive transformation of messy, real-world data into the data tables analysis tools expect." Data can be exported as a CSV or TSV or as JSON data.