Juice Analytics Combined Feed

JuiceKit Sighted in Federal IT Dashboard

2009-07-02T13:36:52Z

We were excited to see that Federal CIO Vivek Kundra and his team used our open-source JuiceKit treemap on the recently released Federal IT Spending Dashboard.

While Tim O'Reilly mistakenly gave credit for all the visualizations to Fusion Charts, we know better. A mother always recognizes her baby. I bet Google also recognized their Motion Chart.

The Best of Business Intelligence: Innovation at the Fringe

2009-06-28T21:49:04Z

Enough complaining about the broken bits of Business Intelligence; it's time to highlight the things that are good and right in the industry. Like most industries, the renewal and innovation occurs at the fringe, beyond the comfort zone of established vendors.

I've created five categories and a catch-all to capture the solutions and companies (not so much technologies) that are leading the next generation of Business Intelligence. The categories are:

Analyst tools
Dashboards
Targeted solutions
Open-source and free
Advanced visualizations
Other stuff

Naturally I've focused on areas of Juice expertise and focus -- not coincidentally, the places where we feel BI has neglected end-users. According to a study by the Business Application Research Center, BI end-user adoption sits at a lowly 8%.

I'm happy to take your suggestions (and update the post) for things I've missed in these categories or for entirely new categories.

Analyst tools

Tools that make it easy for analysts to pull data from multiple sources, analyze, visualize and share it.

Winner: Tableau, the reigning king of visual analytics tools, has added more web-based functionality to allow for online sharing and collaboration.

Runner-up: Good Data has arrived on the market with a web-first platform designed to democratize analytics. I had a chance to get a demo from the management team and was impressed with the ease of use and high-quality data presentation.

Dashboards

"A frequently updated analytical display that is clear and concise" (via a recent post)...and not likely to draw the rage of Stephen Few.

Winner: BonaVista Systems wants to make Excel a "first choice dashboard tool." From the humble position of sparkline plug-in vendor, BonaVista has taken a leadership role in encouraging more effective dashboard design.

Runner-up (tie): Two BI companies, Qlikview and Microstrategy, seem to be following BonaVista's lead. Unfortunately, they may only be dipping in a toe as I found just a couple examples that break from the traditional over-glossy, gauge-riddled dashboard interface.

Targeted solutions

Companies that serve a narrow slice of the BI world extremely well. The desire to be all things to all people has been an Achilles Heel of the BI industry. The general purpose BI platforms often prove too broad and too generic to serve the unique problems of specific industries or functional areas.

Winner: Wall Street on Demand is a brilliant, below-the-radar provider of information solutions to the financial sector. Their sparse, articulate marketing text and few screenshots hint at a company that knows exactly what they do and deliver high-quality BI solutions. I wish I knew more.

Runner-up (multiple): The following are just a few companies that have focused on an industry or functional segment to deliver targeted BI solutions:

Quantivo for customer behavior analytics
Visual I|O for pharmaceuticals
LucidEra for sale pipeline reporting and analytics

Open-source and free

(I know there is a difference.)

Winner: Pentaho offers an open-source end-to-end BI suite that is a competitive alternative to the big-guys. Of course, the implementation it isn't necessarily cheap or easy.

Runner-up: If anything should scare the BI industry, it is the possibility of a Google Analytics model extended into more general data analysis and visualization tools. Google Fusion Tables may just be the tip of the iceberg.

Advanced visualizations

Bringing leading-edge visualization techniques out of academia and into the business world.

Winner: Many Eyes continues to impress with high-quality visualizations. They are easy to create and clean in design and usability. Impress your boss with a slick visualization in your next presentation.

Runner-up (tie): Openviz / Advanced Visual Systems and Panopticon appear to be the two BI vendors battling it out for leadership in advanced visualization solutions. Unlike Many Eyes, these guys lack Tufte-esque sophistication in infoviz design. That said, there is a big difference between creating a one-off New York Times-quality visualization and delivering a toolset that is re-usable in many different situations.

Other stuff to be admired

Free charts with good default design. InetSoft's Style Chart and Google Charts offer free, embeddable charts.

Jargon-free BI marketing. With few exceptions, BI web sites are densely populated with those awful stock-photography people sitting around conference tables (or worse, the ethnically-diverse V-formation marching at you) and meaningless business jargon and techno-babble. I really appreciate Blink Logic's web site with its straight talk and clean, readable design.

Beyond the desktop. RoamBI has a great-looking iPhone application that is designed to "transform your data into insightful, interactive visualizations delivered to the iPhone." It makes the Oracle and Qlikview iPhone apps look old-school.

Five Features of Effective Filters

2009-06-05T12:08:30Z

I've developed a bit of a penchant (obsession?) for decomposing the pieces of analytical applications and framing the good and the bad characteristics. So far I've taken on treemaps, real-time dashboards, alerts, composite measures, success metrics.

Next up the poor, neglected, and taken-for-granted filter. For such a common and essential component, it seems rare that designers take a moment to consider how to make the best possible filtering mechanism. Here are the five elements I consider critical to a good filter selector along with examples from exemplary interface designs.

Selections
Impact
Context
Persistence
Short-cuts

Selections

Good filters make it obvious to users what has been selected. That might seem like an obvious necessity but consider what happens when you filter in an Excel list. The filter section, even if it is a single item, is immediately hidden from view.

Jonathan Harris' frequently referenced We Feel Fine visualization offers one of my favorite filtering examples. Notice how the selected items are highlighted and the non-selected items are de-emphasized. The bar at the top clearly shows what has been selected, even after the filter selector is "put away."

Impact

The best filtering mechanisms also give instant feedback about the impact of your filters. This can be as simple as a subtle indicator that the filters are being applied. Even better, as demonstrated in the The New York Times' Rent or Buy site, the graph animates in real-time as filters are applied. This creates a very tangible connection that helps the user understand the impact of the filtering choices.

Context

Filters should provide information around the items being selected. What does it look like? How many are there? Take the simple font selector in Office applications: Isn't it a no brainer that the names of the options are shown in the actual typeface? Here are a couple other fine examples of context:

Click shirt is Bret Victor's brilliant t-shirt design interface. In it, he offers an elegant filter implementation where all the selections show images of what you are about to select.

Elastic lists is one of the most innovative approaches to filtering. The height of individual blocks in the selectable stack shows the frequency of the items, an embedded sparkline shows the trend, and brightness indicates "weight of the metadata value compared to the overall distribution" (a bit too ambitious/confusing, in my view).

Persistence

Given the importance of filters to most information applications, it is surprising how often the interface makes them hard to find. As I mentioned in an earlier post, the failure of many analytical and reporting applications is that "they assume users know precisely what they need before they’ve begun the analysis." Filtering shouldn't be a one shot deal; the functionality should always be accessible.

Kayak, a travel site, integrated the selection filters into the results so users can easily change their trip criteria without having to start a new search.

Short-cuts

Finally, filters should make it easy to apply common selections (All, None) or complex sets (My Saved Filters, Northwest Region).

Moodstream by Getty Images recognizes that users aren't always going to want to configure a bunch of filters individually. The presets wheel solves this problem by offering a series of pre-defined "filter sets."

Finally, I'd be remiss if I didn't mention the sophisticated and powerful filtering functionality delivered in Tableau. In addition to providing filtering by selecting graphs (i.e. in context filtering), the application allows for multiple selector types, wild-carding, conditional filters, top/bottom filters, and on and on. If you want a comprehensive catalog of potential ways to offer filtering, watch the Filter Data video here.

Wordtree for Visual Text Exploration

2009-05-22T09:09:39Z

Analytics can be all about having the right tool for the job. When your data is text, traditional analysis tools (e.g. Excel, OLAP tools) are like peeling a mango with a chainsaw.

There are a number of visual exploration tools specifically designed for text data, including:

Word clouds like Wordle (fun but superficial);
Network diagrams like Visual Thesaurus (good for individual words, not text);
Trend graphs like Baby Name Voyager or Google Trends;
Granular presentations for interacting and exploring individual phrases, e.g. We Feel Fine and Twistori
"Word trees" that let you navigate through lines of text to understand the most frequent words, relationships between words, and common phase and sentence structures.

It is quite difficult to find a Word Tree in the wild. The brilliant team at IBM's Many Eyes were the first to make Word Tree's generally available. The same ManyEyes team have also created an alternative approach for visual text exploration with a tool called Phrase Net.

Recently, we built a slightly different take on the Word Tree in Concentrate, our tool which allows users to explore huge search query lists to see how people use search keywords. For geeky entertainment, we created a special Concentrate demo account with the lyrics of songs from Rolling Stone's 500 Greatest Songs of All Time. Click here to sign-in to the demo (Press submit and then choose WordTree at the top).

Here's how our Word Tree works:

The box at the center is your starting point. When you open a Word Tree, it will contain the most common word in the text data. You can edit this box to "re-center" the wordtree (name that tune):

Stretched out on either side are words and phrases that are tied to that center word. The size of the words represents their relative frequency.

Rolling over the words/phrases will highlight the connections to your center word and on the other side. You'll also see a pop-up box with examples of the phrases containing selected words.

You can open or close branches by clicking on a word. Words with hidden branches are highlighted in orange. We also have an ability colorize the words based on a metric in your text data.

While these more advanced visualizations are a start, I suspect there is a lot of room for other tools and techniques to visually explore text data. I'd be curious to hear about other tools you've seen along these lines.

Gartner Identifies the "Last Mile" of BI

2009-05-07T08:11:52Z

Over the past few years we've made the point that today's BI vendors stop short of joining data to decision makers at the point of decision and action. We like to call this problem the "last mile". As it turns out, Gartner does, too.

According to a recent article, Gartner analyst Kurt Schlegel states in the report "Overcoming the Gap Between Business Intelligence and Decision Support" that most companies still aren't able to link BI to "the last mile" of making decisions that actually help their businesses.

Gartner joins a short list of other prominent voices (Tableau, SAS) in the BI community that have already come on board with Juice on this concept. We're very glad to see others addressing the gap of making information really and truly useful for decision makers.

While we're at it, that's not the only theme that has seeped into the Gartner perspective: Gartner's global BI manager Ian Bertram says the fundamental problem with BI isn't about technology, it has to do with making BI work better for people. In other words, "BI isn't a technical problem, it's a social one"

So Gartner Folks, if you're out there and following our blog, we're excited to see you coming along side with us. And as long as you're listening, here's a few other ideas we'd love to see you consider as well:

The maturation process of data analytics
How today's BI tools should be designed to be used
The proper application of substance over decoration
and last (but certainly not least), the importance of good Information Experiences™

Breaking Free of the One-Page Dashboard Rule

2009-05-04T21:08:36Z

Conventional wisdom says that an executive dashboard must fit on a single page or screen. The argument hinges on a pair of assertions about this constraint: it provides necessary discipline to focus on only the most critical information; and it enables the audience to see results "at a glance."

The "discipline" argument is made forcefully by Avinash Kaushik (among others).

"if your dashboard does not fit on one page, you have a report, not a dashboard...This rule is important because it encourages rigorous thought to be applied in selecting the golden dashboard metric."

I buy wholeheartedly into the value of constraints. However, defining a useful constraint as a "rule" assumes there is only one viable means to achieve the desired ends. Confining visual real estate is but one way to focus your thinking. There are others: How about limiting yourself to five key measures? How about demanding that a dashboard can be understood in 3 minutes by a new user? How about only presenting exceptions?

The argument that a one-page dashboard necessarily provides an view of your business "at a glance" is more self-deceiving. Well-known information-ista Stephen Few uses this rationale in his definition of a dashboard:

A visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance. PDF

I check my speedometer "at a glance". I "glance" at a Heads-up Display (HUD) on a video game showing how much energy my character has remaining. These displays communicate but a single number that is already hovering on the corner of my consciousness. If we follow this advice literally, we'd show:

Assuming one page gives you quick, easy comprehension is like assuming all red cars are fast. That's simply not true. It must be duly noted, however, that all red cars are cool.

More often, people follow the one-page dashboard rule off a cliff like these folks.

There are real problems with this definition:

In reality, the one-page rule leads to jamming information into the available space.
When everything must fit on a page, there isn't room to describe the connections between information or fashion a story from the data.
A good dashboard raises more questions than it can answer. Sticking to a static piece of paper limits any ability to find or present explanations.

Don't get me wrong: A one-page dashboard is often an effective way to create "a visual display of the most important information needed to achieve one or more objectives." But with streaming video, interactive visualizations, podcasts, Kindles, smart phones, video projectors...is it really necessary to limit ourselves to 8.5" x 11" piece of paper. Or might we open ourselves up to some more creative solutions to sharing the numbers; a short movie, a few slides, a short text narrative, or 140 characters.

I'd like to use this definition instead and will be back soon with some ideas on how to make your dashboards clear and concise.

Vasco de Gapi: Google Analytics API Explorer

2009-04-29T13:14:32Z

Are you ready to explore the Google Analytics API?

At Juice, we were very excited about the public release of the Google Analytics Data Export API. Our product Concentrate has been running on a hackish home-brew Google Analytics export tool since its release last November, and we were happy to be able to relaunch as a Customer Example of the Google Analytics Data Export API.

Today, we are releasing a new, free tool called Vasco de GAPI. Vasco is a web-based tool for exploring the API, for downloading complex slices of data using the API, and to even automatically generate code that will allow coders easy replication of the API calls in question. Instead of describing it in more detail, I am just going to demo it.

I am going to start with a relatively rare but curious functionality of Google Analytics. I keep track of who wrote each blog using a Google Analytics user-defined setting that is set to the author's name for each specific blog post. Slicing our blog by author can be cool for me as an employee so that I can brag during my yearly review about how many visitors I bring in or what natural search visits we get for free as a result of my posting. For the demo, I'm going to discover the natural keywords that bring traffic to my blogposts on the website.

Let's get started.

The first step is to authenticate using Google's OAuth system.

I select ga:keyword as a dimension.

ga:pageviews is the metric I am interested in. The results will automatically get sorted by the first metric, so I do not need to explicitly specify a sort value.

I set ga:userDefinedValue as a filter, and filter it to saluryasev, and select this last week as a reference point.

Here is the list of parameters that Vasco de GAPI is passing to google.

What are my results?

It turns out that of all my posts, the Google Trends API that I put out about a year ago drives the most natural traffic to our site. Hopefully, this will change with a few more blog posts, but this is still rather interesting data. I could target that specific audience with something Google-trendy. On an unrelated note, a slap to my face was that Zach's name sent fifteen users to my blogposts. Go figure. Sixteen users searched on my last name, and were probably looking for my more popular father.

To get at the rest of the data, I can click the download link at the bottom of the page or, for developers, another link downloads working code that will replicate this exact pull.

Vasco runs using an open source Python gdata wrapper for the API that can be downloaded here. This wrapper is powerful, and I will write another blogpost about it next week. It is plugged into the Google gdata module, and as such allows all forms of authentication available to gdata users, including OAuth, AuthSub, and clientside.

Hopefully, Vasco de GAPI can help all other potential explorers sail smoothly through the API. When it comes to data, Google is just an great company. They have had powerful APIs for most of their major services for years, and while the Analytics API is a latecomer, it actually is more powerful than the analytics interface itself. This sort of openness is something to be envied by all other analytics and web companies in the market.

By the way, please let me know if the explorer theme works well. It was a lot of fun working on a project with a slightly esoteric approach.

Enhanced Google Analytics: Firefox Plugin

2009-04-13T14:10:09Z

There is new life in the tool that shows change in Google Analytics. A year after releasing our Greasemonkey script, we are pleased to release an updated version of the Enhanced Google Analytics script as a free Firefox Plugin. For those already using the older Greasemonkey script, you can skip ahead to the What's new? and How do I get this plugin? sections of the page. For the rest, you may be wondering: Why does my Google Analytics need change?

Change, and why it is important

When I first started working at Juice Analytics, my boss Zach showed me a part of his daily Google Analytics routine. He would open up the Referring Sites page, glance at all of our 942 referrers. Using his superior intellect and capacity for remembering random urls, Zach would discover interesting deviations in the traffic from sites linking to our blog.

Our top referrers looked more or less similar day to day. Even once you get past the more recognizable top sites such as Twitter and Google, the various somethingblog.com pages, without context, often look a lot like somethingelseblog.com. To top it off, most of the information is not even specifically interesting. Our chartchooser.juiceanalytics.com domain sends us consistent regular referrals, but so what? Day to day, I don't even really care about Google or Twitter unless something changes. With change, I know whether someone has posted something new about me, sending valuable traffic. A good read on the topic is Avinash's rant about "actionable analytics".

Our Firefox plugin is designed to allow analysts to get more action out of what changed in the Referring Sites and Keyword Reports. Here are a couple examples of the plugin in action from our Google Analytics account:

What's new?

Our focus for this release has been to improve functionality, to reduce the barrier to entry for new users, and to allow automatic updates for the plugin. The new version of the script works nearly instantaneously, and the installation involves only two clicks (in contrast to the 7 clicks of the Greasemonkey version). As a Firefox plugin, updates are now automatic and require no reinstall. Keyword sensitivity has been raised to 50% for consistency. As a slight bonus, the design and layout of the form and buttons is now sleeker and the table stands out in a pretty Google blue.

Greasemonkey itself is no longer required for the plugin, but you may want to keep it around for any of the other cool scripts available from the community. If you ever find yourself wishing that something about the web looked different, acted different or had different functionality, there may be a Greasemonkey script to ease your pain.

How do I get this plugin?

First, you need Firefox 2.0+.

If you are a user of the equivalent older Greasemonkey version of this script, you may want to go ahead and uninstall it. Go to Tools=>Greasemonkey=>Manage User Scripts..., select Google Analytics Downloader, and uncheck the Enabled box.

If you never had the script installed, or once you removed it, simply click here to go the mozilla addon site, select the checkbox and click the button. Once installed, navigate to Google Analytics, and go to either the Referring Sites or Keyword pages, and click the blue button.

Happy analyzing!

Twitter Analytics for "Analytics"

2009-03-27T12:38:52Z

Twitter’s wild popularity hasn’t obscured the fact that the service needs to eventually make money. The concept of “Twitter analytics” as a revenue stream has come up often enough to make my ears itch and my nose burn.

Twitter’s new business development lead explains that the company is “developing a range of analytics and metrics products and services built around the information contained in tweets”…and “trying to figure out what are the appropriate metrics around engagement and how to convey those.”

Web Strategist Jeremiah Owyang raises the concept of a Twitter CRM solution, in which Twitter would offer their own analytics system to brands, that will help them to track and manage the conversations.

The Twitter ecosystem has responded with a wide range of tools for analysis of Twitter data. Web analytics behemoth Omniture recently announced the integration of Twitter data into their platform. At the same time, web analytics consultant Eric T. Peterson has been vigorously marketing Twitalyzer, a tool to evaluate individuals’ use of Twitter and metrics of influence. Google’s Chrome Experiments released a cool visualization tool called Social Collider that reveals cross-connections between conversations on Twitter. Here are a few more Twitter analytics tools that I’ve run across:

Despite all the activity, I haven’t yet seen a solution that offers the kind of valuable analytics that a company could use to understand the Twitter conversation relevant to their business. The applications above are either focused on the measurement of individual Twitter users or offer a high-level tracking of words and phases in the general conversation. They treat tweets as transactions — How many? How valuable? Who’s listening? Who’s responding?

To me, the great and more rewarding challenge in Twitter analytics is to synthesize the substance of those conversations. Imagine if you went to a party and could overhear everything that everyone else was saying. Who talked the most and who had the greatest audience is less interesting than what topics people were discussing and what was said.

I wanted to take a shot at this type of Twitter analytics.

Analysis Approach

First I had to define a particular domain or topic area. For expediency, I focused on all the tweets that included the word “analytics.” Using the Twitter search API, I pulled the first 500 tweets for each day in March and parsed the results to pull out users, urls, and other characteristics of the tweets.

To analyze the words and phrases being used, I uploaded the resulting 11,300 tweets into Concentrate, our search analytics tool. Concentrate is optimized for search query text (i.e. short phrases without a lot of punctuation). Nevertheless, it has a number of features that make text analysis easier, including breaking out the most common words, phrases and patterns. It also allows for filtering by words to create frequency statistics.

There were two main questions I wanted to address:

What topics are people discussing?
What is the structure of the conversation?

Topics of Conversation

The content of the Twitter conversation can be analyzed as words, sites/links, people/groups, and company/products.

Words

I used Concentrate to find the most common words, then I dumped those words into Many Eyes to create this “Wordle-brand” word cloud. Many Eyes has a nice feature that takes out the “common English words.” Clearly Google dominates the conversation, and I even had to artificially reduced the value to make the other words legible.

Below are the top 10 (non-common) words that show up in the analytics conversation

Sites and Links

Twitter has become a mechanism for sharing interesting links (I’ll get to data on that in a bit). Looking at the most popular sites and specific links gives a sense for what people in this community are reading and talking about.

People and Groups

Twitter users have a few conventions for connecting tweets to people or groups:

”#” (i.e. hashtag) associates the message with associated with a group, topic or event.
“RT” (or “via”) is to repeat or “retweet” something someone else has said.
”@” associates a tweet with another user, whether retweeting their message or directing a comment to them.

Here are the most common groups and people referenced in the Twitter data.

And the people with the most tweets using the word “analytics”

Companies and Products

I was also interested in what companies or products were referred to most frequently. It is no surprise that Google dominates the conversation. Microsoft gets on the board with the recently closing of their adCenter product. I think we can safely assume they won’t be showing up that often in the future.

Conversation Structure

Beyond the specific content of the conversation, I was also curious about how people who are talking about analytics tend to use Twitter.

Types of Tweets

Eric T. Peterson has four things he considers “signal” (versus “noise”) in the Twitter conversation:

References to other people (defined by the use of “@” followed by text)
Links to URLs you can visit (defined by the use of “http://” followed by text)
Hashtags you can explore and participate with (defined by the use of “#” followed by text)
Retweets of other people, passing along information (defined by the use of “rt”, “r/t/”, “retweet” or “via”)

While I’m not fond of this definition, examining these different types of tweets (along with question-based tweets) provides a good lens into the nature of the conversation. The following chart shows the percentage of tweets that fall into each of those categories.

It would be all the more interesting if you could follow the types of tweets across time and compare against other topic areas. I suspect that the URL linking within Twitter is on the rise and is turning Twitter into a Delicious-style bookmark sharing service — without the functionality to save, tag, annotate, and view the bookmarks at your leisure.

Link Evolution

Given all the sharing of links, I wanted to get a clearer picture of what happens when a link becomes popular. The graphic below shows some of the top links during the month and the amount they showed up in tweets by day. The red bars represent days where ten or more tweets included the link. A couple links demonstrated popularity over a week or so, but the rest sizzled then disappeared in a day or two.

Activity Distribution

Finally, I took a look at the distribution of users by the number of tweets including the word “analytics.” It was no surprise that the vast majority of the 7,700 twitterers only used the word once in March (of course this doesn’t tell us about their other twittering activity). Obviously there is a small population of people at the core of the discussion.

While you'd have to go into more depth to answer detailed questions, there are a number of interesting take-aways for me, including:

“Analytics” means “web analytics”, not business intelligence or general reporting about sales, operations, or marketing.
Google Analytics is the star of the party. Of course, the fact that the brand name includes "analytics" is an advantage, but I didn't see a giant "Juice" in the word cloud.
Twitter is an echo-chamber. The content clusters around particular subjects, with people retweeting and sharing links about the big news of the day. There are a dozen or so stories that dominated the conversation over this time period.

What’s next?

There are a lot more views of this data that could be enlightening for a company interested having a real-time understanding of their marketplace. For example, it would be interesting to provide more insight into:

Who is at the center of these conversations?
What is the positive or negative tone of the discussion (Twitter actually offers this information as part of their API)?
How has is the conversation changing over time?
What is the best way to define the boundaries of a domain-specific conversation?

These are the types of questions that I’d like to see addressed in a more complete Twitter analytics tool.

Visitors Guide to the Juice Blog and Website

2009-03-23T09:18:14Z

With almost 300 blog posts and dozens of free tools and demos, we thought it would be useful to offer some of the highlights from the Juice blog and website.

Our Views on Analytics and Communicating Data

Information Experiences™, Dashboards and Metrics

Demos

Stimulus Bill Explorer (introductory post)
JuiceKit™ Economic Census Treemap (introductory post)
Examples of JuiceKit™ visualizations
Contact us to see examples of our client work

Analytics Tools (Free stuff!)

Visualization

JuiceKit™, an open-source visualization SDK (introductory post)

Web analytics

Concentrate™, a long-tail search analytics solution (and how it works)
Enhanced Google Analytics Firefox add-on (introductory post)
Google Trends API
Google Analytics API

Excel and charting

Mapping