Thank You!

Hello everyone! This will be my last post as the Visual Insights moderator. It has been a pleasure getting to know many of you. A big thank you to those who contributed to the conversation, whether it was through a blog post, comments, Twitter or Facebook shares, etc.

I’d like to introduce the new blog moderator, Elizabeth Hefner. Visual Insights started out as a platform to discuss all things data visualization, but since we’ve been seeing a growing demand and interest in visual analysis, Elizabeth will be taking this blog into this new direction. Please join me in welcoming Elizabeth!

– Stella Lau

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“Big Data” and “Next-Generation Analytics” on Gartner’s Top 10 Strategic Technology Trends for 2012 List

On Monday at the Gartner Symposium, David Cearley presented Gartner’s annual list of the Top 10 Strategic Technology Trends for 2012. Among those that made the list are big data and next-generation analytics. Although the two items are listed separately, the two technologies go hand-in-hand and can provide the most compelling and effective experience for exploring and navigating through complex data.

[Source: PC Magazine]

It comes as no surprise that the two items were listed, as organizations are besieged by an explosive growth of information coming from disparate data sources every day. This data can be structured, semi-structured, and unstructured. With the annual growth rate well over 100%, analyzing and understanding big data has become a top priority.

For far too long, organizations have spent too much money and resources on collecting data, scrambling around to ensure data integrity, and finally, wondering how to make use of this data. More and more, organizations are starting to realize the need for technologies that can help them maximize the value of their data assets.

Traditional analytics that have relied on computers and algorithms to do the work for them are starting to be deemed limiting, especially with the growth of unstructured data. As Gartner continues to push big data and next-generation analytics, organizations need to adopt technologies that can drive business decision-making in ways not possible before. This new wave of analytic techniques, or advanced visual analysis, harnesses the most powerful pattern recognition system available — the human brain. Advanced visual analysis will help organizations discover key insights that were hidden in the past and turn the challenges of big data into opportunities.

Key Features to Look for in Advanced Visual Analysis Software

“In 2009, more data will be generated by individuals than in the entire history of mankind through 2008.”
– Andreas Weigend, former Chief Scientist, Amazon

“Between the birth of the world and 2003, there were five exabytes of information created. We [now] create five exabytes every two days. See why it’s so painful to operate in information markets?”
– Eric Schmidt, CEO, Google

Enterprise data will grow 650% over the next five years. Of that, 80% of that will be unstructured. The amount of new information generated next year alone will amount to more than the previous 5,000 years. Finally, the number of text messages sent in the last 24 hours is one for every human on the planet.
– David Cappuccio, Analyst, Gartner Group

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We all recognize that big data is only getting bigger, and with all the hoopla surrounding big data, what’s not to be concerned about? Big data is typically presented as a monstrous beast that is growing exponentially and spiraling out of control, and our ability to make sense of this data has not been keeping pace.

However, when equipped with the right tools to organize and manage your data, you can navigate seamlessly through the growing universe of data and turn the big data problem into big insights. Traditional analytics techniques are slowly starting to be deemed limiting. Many forward-looking organizations are incorporating advanced data visualization coupled with social network analysis capabilities, or advanced visual analysis, into their data analytics toolkit to proactively identify relevant actionable insights in a timely basis in ways not possible before.

Advanced visual analysis is the new generation of transforming potential liabilities caused by big data into invaluable business assets. Graph-based visualization, together with social network analysis capabilities, are most effective for seeing critical relationships, trends, anomalies, and patterns in your data that are not readable by computers but by the most powerful pattern-recognition system available – the human brain.

As you or your organization start to include advanced visual analysis into your analytics toolkit, here are some features to look for that are essential to maximizing the business value of your big data.

1. Access to Any Data Source, Any Time
Aside from traditional data sources such as Excel, XML, and text formats, new types of data are flowing into organizations at an exponential rate, such as clickstreams, GPS, biometric, cell phone intercepts, blog posts, Twitter feeds, point of sales, text, video, audio, seismic, Web logs, and RFID data. It is important that your advanced visual analysis application can access data from disparate data sources and integrate them into a single view.

2. Incremental loading
When you have a lot – even up to millions and billions – of data points, it is never effective to load or display all of this information at once. Loading all your data at once is slow and the amount of data presented might be overwhelming. With an incremental loading mechanism, you can incrementally load a subset of data from the data source that is relevant to your visualization purposes and analyzed in-memory for interactive analysis.

3. Filtering
Once you have your data mapped out, a key element of data and insight discovery through these applications requires sophisticated data filtering techniques. This enables you to focus on key trends, patterns, and outliers that matter to you for uncovering actionable insights without being distracted by other data.

4. Customizability
No matter what industry you’re in, you are the domain expert. Everyone’s usage models will differ from organization to organization. There are plenty of “out-of-the-box” solutions, but many of them don’t understand the unique context in which you are working in. Customizing your advanced visual analysis applications to fit your usage model will enable the most effective visual analysis experience, and help you identify insights that matter most to you.

How are you dealing with big data?

Congratulations to the Winner of the Graph Drawing 2011 Contest!

Last week, a few of us at Tom Sawyer Software attended the International Symposium on Graph Drawing in Eindhoven, Netherlands. The Graph Drawing Symposium is the main annual event that brings together top researchers and practitioners working in the area of graph drawing. The event is a forum where like-minded researchers gather to generate novel ideas, work together to create innovative solutions, and foster a forward-thinking community.

One of our core philosophies is to promote data visualization best practices and to provide the most compelling visualization techniques possible. At Graph Drawing, we contributed a challenge graph to the Graph Drawing Contest. For this particular topic, we challenged researchers to visually present a complex data set in the most compelling way possible by combining graph drawing algorithms and complexity reduction techniques, such as filtering and varying the graphical attributes. The graph is a composers graph with a musical theme, where the nodes represent Wikipedia articles about music composers, and the edges represent links between these articles.

A lot of great visualizations came out of this contest. Congratulations to the winner, the folks over at Meurs HRM! Check out the interactive visualization they produced for this challenge; awesome stuff.

Tom Sawyer Perspectives, Version 3.0: Combining the Power of Data Visualization and Analytics

In today’s data-driven, dynamic, and fast-paced world, it is more important than ever for organizations to have the tools to cut through the sea of information and better leverage their data assets. In response to this reality, there has been an increasing number of applications to help organizations get the answers they need from big data at the speed of thought.

To address this growing demand and to provide organizations with best-of-breed data visualization and analysis software, we here at Tom Sawyer Software recently released Tom Sawyer Perspectives, Version 3.0. Tom Sawyer Perspectives, Version 3.0, is focused not only on enabling organizations to quickly build more advanced visual data analysis applications without sacrificing quality, but also providing end-users a visually compelling experience to analyze larger, more complex, and disparate data sets faster and more intuitively.

We’ve done a lot of work both above and under the hood, introducing game-changing technology that provides Tom Sawyer Perspectives the foundational architecture for agile expansion in future releases. This release combines the power of data visualization and analytics to enable organizations to gain faster insights into their large and complex data sets.

This release includes the first of a series of innovative features to help organizations go beyond pure data visualization. New features include the Resource Description Framework (RDF) data integrator and Social Network Analysis (SNA) capabilities.

See a short demonstration of the new RDF data integrator and SNA capabilities in action:

The RDF data integrator feature is targeted at organizations that are building visual data analysis applications using RDF data sources as part of their semantic web initiatives or as part of their composite data suite for social network analysis. Although Tom Sawyer Perspectives, Version 3.0, was designed to support all RDF data sources, Tom Sawyer Software developed this product with Oracle Spatial and Semantic Technologies team so that it is tuned to work efficiently with Oracle Database Semantic Technology.

The SNA feature is designed to enable organizations to tightly integrate sophisticated centrality analysis capabilities into their visual data analysis applications.