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Productizing Tableau - Upping Your Game with Customers and Partners
Experian Automotive developed and launched a major initiative using Tableau Desktop and Tableau Reader to provide data to their clients who businesses touch the Automotive industry. Experian helps its customers increase market efficiency with cutting-edge vehicle statistics that help them make fact based decisions to better 'drive' their business directives.
Heidi Haupt described how her team used Tableau to upgrade their product by providing fast, easy to use distribution of data with compelling visuals. Previously Experian delivered to their clients an "archaic, macro-driven pivot table," which had little visual appeal and was inefficient to produce. Heidi's team only took two days to develop the first demo-ready prototype, and had a production-ready version in two weeks - just in time for the national auto parts convention.
The key benefits of building their latest product upon Tableau were:
- Tableau Reader allowed easy distribution of extracts of Experian's data to their clients. Thanks to extract enhancements in 5.0, workbooks were significantly smaller than the prior product.
- Experian found Tableau to be the best quick-to-market, low-cost solution.
- There is a clear migration path to Tableau Server, allowing Experian to scale out to larger audiences and with faster update cycles.
- Tableau has world-class visuals, presenting a very clear story of the underlying data.
- Experian wanted a path towards introducing ever richer visuals and interactivity, without overwhelming their clients with an idiosyncratic or nuanced product.
Heidi also reported that her team takes advantage of the latest technology in Tableau 5.0, including extensive use of filter actions and efficient extracts. For the latter, she explains how powerful it is to be able to hide unused dimensions and roll-up / aggregate data to the exact level of detail needed.
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TwitterVisualizing Customs Fraud in the European Union
Jürgen Marke is an Operational Intelligence Analyst with the European Anti-Fraud Office (OLAF), which conducts internal and external investigations into irregular or fraudulent activities concerning the financial interests of the European Union. Tableau is used to help analysts visually identify patterns in fraud, and is often more effective than automated statistical analysis.
The specific examples of fraud Jürgen discusses is the misdeclaration of values or origin for textiles, which is an evasion of ad valorem duties. A scatterplot of commodities versus time helps visually cluster cases that meet certain criteria that imply fraudulent activity. Interestingly, the clusters will often - and suddenly - alternate between neighboring countries, depending on which is known to be enforcing customs regulations at the time. In his office, Jürgen proudly displays a wall-sized plotter printout of the scatterplot across all nations.
My favorite part of the presentation came when Jürgen described a request he heard from Ellie Fields, our Director of Marketing: could you show how this was visualized before Tableau? Jürgen smiled as he showed a blank PowerPoint slide, and remarked "I didn't even try - it would be ridiculous!"
Apple: Kaizen Cost Reduction Planning Algorithms
Bruce Boston started using Tableau several years ago at C-Net. He now works at Apple Inc. in the AppleCare Analytics division, but he wasn't shy about insisting on using Tableau in his new role. Software such as BootCamp and Parallels eliminated the platform barrier, allowing Bruce to successfully deploy Tableau Desktop and Server within Apple.
The key to Bruce's evangelism of Tableau was his insight into the clear path to saving money within the company, while actually enhancing the quality of Apple's product. This quality-oriented focus to product management is known as Kaizen: "a Japanese business philosophy of continuous improvement of the total product experience."
Bruce focused his improvements on automating the number-crunching parts of analysis, while empowering human analysis suited to our strengths such as: metadata awareness, qualitative valuation and contextual interpretation. Using Tableau, he was able to offer timely analysis of product defects, exploring the number of reported incidents per product each week. Bruce couldn't share any of Apple's data, but he mocked up some dummy data sets to explain his approach, as in the attached image. He used time on both axes, showing the weeks since product launch on the horizontal axis versus the number of elapsed weeks from a customer's purchase to their reporting of product defects. Diving deeper into the data, statistical analysis exposed patterns in the reported defects and allowed analysts to confirm causation from the correlation - for example, by root-causing problems with suppliers, software glitches, or integration defects.
His guide to the division of labor for Kaizen 10-step analysis is:
- Discovery (software automation)
- Statistical validation (software automation)
- Correlation (software automation)
- Financial valuation (software automation)
- Review results of discovery (Tableau)
- Engineering evaluation (human evaluation)
- Business explanation (Tableau)
- Process improvement (human evaluation)
- Engineering re-evaluation (human evaluation)
- Verification and review (human evaluation)
Career Growth with Tableau
Dana Zuber is a self-described Tableau junkie. Find out how Dana's evolution as a Tableau user related to the trajectory of her career growth at Wells Fargo over the past year.
Dana launched into her presentation with a catchy subtitle: "From cubicle monkey to analytics guru in three steps (made easy with Tableau)." She presented the challenges and rewards of growing a Tableau user base across Wells Fargo, growing her professional network in the process. Her successful techniques for hosting group problem-solving and brainstorming have given her the confidence and skills to take Tableau dashboards directly to the CEO. In this respect, I'm reminded of the success story Dr. Jon Nakamoto presented at last year's customer conference, and this represents a trend of discarding stale Powerpoint slides in favor of live, rich analytics with Tableau. Of course, Dav Lion presented a technique during Devs-On-Stage for embedding Tableau within Powerpoint when slides are inescapable.
Dana's success story begins with last year's customer conference, where she met another Tableau user from Wells Fargo. Their early successes within their respective teams led to requests for demos by managers from multiple business lines, and they built enough interest to launch a user group. Dana has been invited to attend executive business strategy meetings, and Tableau has helped grow her network into Wachovia as the two companies merged.
How to use Tableau to get your analysis out of PowerPoint and into action
Turning to the topic of group problem solving, Dana described the key points for successful brainstorming sessions using Tableau.
- Sessions should involve 2-5 people
- Expect to need an overhead projector
- Make popcorn! (or other techniques to make folks comfortable)
- QA your data: ensure that it's clean, and do some preliminary analysis. Dana recommends preparing around five worksheets in advance. This provides a launching point for discussion, and gives the meeting leader some familiarity with the data.
- Schedule as 50-60 minute meeting
- Create an agenda and stick to it
Identifying outliers is a key goal of Dana's sessions, and she recommends a few techniques for glancing at the data from a different perspective:
- Table-calcs, such as % change, help expose trends that might otherwise be hidden.
- A "Q-tip" diagram is a compact view similar to a sparklines, but uses line width and color encodings to draw attention to outliers across a dimension such as time.
- An x-y scatterplot with size & color encoding for key dimensions are good at clustering related data points, leaving outliers as isolated marks.
Putting Your Data on the Map
I've been enjoying the second annual Tableau Customer Conference, and the first customer case study I visited was entitled "Put Your Data on the Map." As with any great presentation, using Tableau to present the story instead of static images in Powerpoint really drives home the points discussed. This session focused on how mapping helps drive deep analysis of geographic trends.
The speakers - Ellie Fields and Brandon O’Brien - covered mapping basics and then demonstrated how to explore customer loyalty for frequent-buyer programs, covering these key points:
- Your data is the center of the analysis
- Mapping is treated as another way of visualizing data
- Geographic analysis is interactive
- Tableau is "geography-aware" without extra work
At a glance, using pie charts on a map is an effective presentation of large-scale geographic patterns. Consider for example this view on customer loyalty:
This invites further exploration of New England, as they do in the following view:
The general principles that Ellie and Brandon espouse for building effective map visualizations include:
- Use size
- Use color
- Use transparency
- Use shapes
- Use pie charts
- Use map demographic layers
Finally, the speakers and audience touched on a few advanced mapping topics - custom geocoding, WMS servers, and so on. These topics are covered in more detail in the Wednesday, 3:15 PM session entitled "Advanced Mapping Techniques."
Other Resources on www.tableausoftware.com
- Web seminars
- Employing Geographic Information in Education and IR – Dr. Bill Toobin, DePauw
- Whitepapers
- Visual Analysis for Everyone - Understanding Data Exploration and Visualization
- IDC Case Study: The Path Toward Pervasive BI at Cornell University
- Improve Your Vision and Expand Your Mind with Visual Analytics
- Selecting a Visual Analytics Application
- X Marks the Spot: Integrating Mapping and Data Analysis to Find Hidden Trends, Patterns and More
- Data sets
- Case studies
- Cornell
- DePauw
- Jacksonville State University
- Free trial
Christian Chabot's Keynote Address: Leather Belts and Line Shafts
Did you know the 2009 Tableau Customer Conference has more attendees than a recent national BI conference? According to Elissa Fink, VP of Marketing for Tableau, there are over 300 registered attendees up from about 180 just a year ago. As the 2009 opening keynote kicked off, there was standing room only in large ballroom.
Tableau’s CEO, Christian Chabot, captured the audience with his story on leather belts and line shafts. He made the analogy that where electricity was initially distributed through leather belts and line shafts, business intelligence software got its start as a developer driven software using primitive visualization that constrained user experience.
Chabot’s goal is to change all that and rock the business intelligence world. Extending that, he says Tableau’s dream is to provide users the freedom to answer any questions and follow any thoughts. And he means ANY. After describing the typical user experience of wizards and formulas, he pulled up the Tableau interface and double-clicked his way to a 4-page interactive, multi-dimensional, multi-perspective, visually intelligent report. He then took that a huge step forward by posting it to a public server and embedding a link back in a blog post. It was as easy as posting a YouTube video.
After Tableau’s VP of Marketing, Elissa Fink had shared the story of how she first heard the company’s mission statement of helping people use their data, Chabot elaborated on the company’s goals for their next release, Tableau 6.0. He still believes Tableau should fundamentally be able to answer all questions with data and that those answers should come fast. But it was his mention of the goal to provide public information and bring data visualization to the public web that was most intriguing. He envisions accessing consumer-owned sites like fantasy sports and being able to interact dynamically with statistical data creating views specific to the needs of the reader, who could then save and share them with other web visitors.
Chabot sees Tableau Public as a revolution in online data and hopes that in five years you can’t go anywhere on the web without running into Tableau data and visualizations.
The Ring of Fire and the Bottom of the Ocean: Visualizing Earthquakes
I haven't yet experienced an earthquake since moving to Seattle, but with all the volcanoes in our vicinity I'm definitely curious about this region's plate tectonics. Thanks to data.gov, we were able to download earthquake data since 1973 and take a look at activity across the globe.
First, let’s consider the Cascade Volcanic Arc. This is part of the area known as the Ring of Fire, a ring of volcanoes around the Pacific Ocean which is responsible for about 90% of the world’s earthquakes (Wikipedia). The Cascade Volcanic Arc includes the volcanoes that extend up the Pacific Northwest coast (Mt. St. Helens, Mt. Adams, Mt. Rainier, Mt. Baker to name a few), into the Aleutians in Alaska and the Pacific Ocean. Let’s take a look at the earthquakes in the Cascade Volcanic Arc.
Many of the largest earthquakes are off the coast, but earthquakes off the coast can cause other problems.
photo credit: tromasbronot
On that note, let’s look at another major earthquake region: the Mariana Trench. The Mariana Trench is the deepest part of the ocean, right off the coast of Japan.
It’s interesting how a mapping of the earthquakes over the years evokes the shape of the Mariana trench: you could almost map it by its seismic activity. The trench is a subduction zone, which is the cause of its high earthquake activity and intensity. While the trench is an impressive 11 km deep, scientists have detected earthquakes at depths of more than 600 km. In contrast, divergent tectonic plate boundaries such as the mid-Atlantic ridge generate fewer, shallower and less violent earthquakes.
Finally, let’s take a look at all this activity in summary. Note that most earthquakes have a magnitude between 4 and 5 on the Richter scale:
Earthquakes near the surface are known as "shallow-focus", and are far more prevalent. The second spike at 33 km in the graph below is consistent with the depth of the upper mantle.
The frequency of "shallow-focus" earthquakes may reflect limitations of our detection technology, which has improved over time as demonstrated in the "Depth vs. Magnitude" view:
For an online, interactive view of this data since 2004, visit the Tableau Public page on earthquakes. Or, download this workbook and explore the data on your own (using the free Tableau Reader, or a free trial of Tableau Desktop). And if you see a big one coming, call us and let us know.
The Best Cities for Jobs, Visually
I was pleased to recognize my hometown skyline in a recent Forbes.com article exploring the best cities for jobs. But skylines were the only visuals that supplemented the article, so naturally I used Tableau to study the rankings geographically.
Joel Kotkin authors the New Geographer column, and thoughtfully shared the raw data. The cities are ranked within three groups: Best Big Cities, Best Mid-Sized Cities and Best Small Cities. Here's the overall rank of job growth on a map, which clearly identifies the region Joel describes as "zone of sanity" – cities that did not participate in the housing boom and subsequent bust.
It's also interesting to look at the most and least improved cities when compared to the prior year's rankings:
I used a scatterplot of total employment vs. movement in job growth rank to visualize interesting outliers:
Of the larger cities, Boston, the DC area, and New York show improvement in job growth rank. That corresponds to what we saw on the maps above – the Northeast and Midwest are improving their rank more than other areas.
State Electoral Votes over Time
Robert Kosara has an interesting post up on EagerEyes where he uses Tableau to visually highlight trends in states' presidential party preference. Two readers suggested that the states should not be arranged alphabetically, instead clustering them in some fashion that groups like-minded states.
A reasonable approximation would be to sort states according to the percent of time they voted for a given party. To break ties we can sort by the latest year that each state voted for that party. Here's the resulting image:
The sort order is based on party preference aggregated over time, which is difficult to perform in the view above since time itself is a dimension; we must first sort in the absence of time, and allow that sort order to persist as we evolve the visualization. To create the view above let's start with a simple view of party vs. state.
Next we define a calculated field 'CntPartyLastYear' that will aggregate the count of a state's party preference over time, along with the tie-breaking value of the most recent year a state voted for each party (converted to a fractional value).
- CntPartyLastYear: COUNT([Party]) + MAX([Year]) / 10000
Place this field on the "Text" shelf and create a table calculation for the percent of a state's total vote to see the split in party preference aggregated over time:
Finally select a column (I arbitrarily chose the Republican party) and sort. This sort order will persist as we modify the visualization to create the final image. Remove 'CntPartyLastYear' from the Text shelf. Move 'Party' from the Columns shelf to the Color shelf, and place 'Year' (discrete, all values) on the Columns shelf. Note that this final step now incorporates time without disrupting our sort based on aggregation over time.
If you'd like to experiment, here's the packaged workbook (TWBX). We offer a free trial that you can quickly download and install to play with the data analysis yourself.
Teradata Geospatial Extensions: a Case Study in User-Defined Types
Many database systems are highly extensible through user-defined types (UDTs), methods (UDMs) and functions (UDFs). Database system vendors often provide UDFs to assist users with migrating from a competitor's DBMS to their own. But UDTs, UDFs and UDMs also provide domain-specific functionality, as seen in Teradata's Geospatial Extensions. Tableau offers a simple, powerful way to exploit these DBMS extensions that can enhance the analytical expressiveness of your data visualizations.Teradata's implementation of their Geospatial Extensions largely follows the SQL/MM standard. The ST_POINT datatype allows for two floating point values representing coordinates. To Tableau this appears as a binary data string, but Tableau's calculated fields can extract the longitude and latitude components using Teradata's user-defined methods for geospatial types. For example, given an ST_POINT column named AIRPORT_LOC we would create the following calculated fields named "lon" and "lat":
- lon: RAWSQL_REAL("%1.ST_X()", [AIRPORT_LOC])
- lat: RAWSQL_REAL("%1.ST_Y()", [AIRPORT_LOC])
Note that these are automatically recognized by Tableau as their appropriate geographic roles. To demonstrate Teradata’s Geospatial Extensions, we will explore a sample data set taken from twenty years of flight records. Teradata's user-defined method called 'sphericaldistance' performs great-circle calculations between two ST_POINT types. To determine the distance between an origin and destination (assuming a spherical path at sea level), we can create the following calculated field which includes a meters-to-miles conversion:
- distance: RAWSQL_REAL("%1.sphericaldistance(%2)", [ORIGIN_LOC], [DEST_LOC]) * 0.000621371192
With the distance computations we can explore a new aspect of the sample airline data. The view below examines delays by distance, in bins of 500 miles, and reveals an interesting spike in delays for flights between 3000 and 3500 miles. Interestingly, there are rarely weather delays at that distance.
Another quick way to view the flights in this category is a map of origin and destination points, filtered on distance. The business dashboard below helps isolate the delays to a handful of popular destinations, most notably Hawaii.
Tableau's calculated fields also allow us to construct UDTs that we can use in queries to the database. For example, the plot below shows all airports within 100 miles of New York City. The marks are size-encoded by the number of unique destinations served by each airport, and color-encoded by the average severity of delays. Of course, it’s no surprise that the three airports typically associated with NYC have high numbers of unique destinations; note that of the three, JFK has the highest severity of delays.
In order to create this view, we first need a calculated field representing New York City:
- New York City: RAWSQL_STR("NEW ST_POINT(-73.986951,40.756054)")
We can create a distance calculation and place it on the Filters shelf to limit our view to distances of less than 100 miles:
- Distance from NYC: RAWSQL_REAL("%1.sphericaldistance(%2)",[New York City],[ORIGIN_LOC])*0.000621371192
With fairly simple SQL expressions we can extend Tableau's support for new types of data and data analysis on any database system that supports user-defined types, methods and functions.
Seattle Walkability
I recently relocated to Seattle to join Tableau Software, and as I searched for a place to live I realized that I could dramatically reduce my dependence on driving. I used WalkScore.com to find a neighborhood with great access to restaurants, stores and a vibrant community. The attached Tableau visualization shows how lucky I am to live and work in Fremont, the Seattle neighborhood affectionately referred to as "The Center of the Universe!"
WalkScore.com provides a mashup of Google Maps and points of interest ranging from parks and libraries to restaurants and bars. Given an address, they compute a score based on the variety and quantity of places you can reach on foot. Lee Byron posted a cool visualization on his blog which aggregated data from WalkScore.com for a heatmap of San Francisco. Using his tools I scraped data for Seattle and loaded it in Tableau to produce the attached visualization (download the TWBX). Green areas indicate the most pedestrian-accessible parts of town and you can see Seattle is dominated by walkable neighborhoods.
In fact, according to WalkScore.com, Seattle ranks in the top 10 most walkable cities in the United States. Fremont itself ranks in the top 15 neighborhoods in Seattle; personally I've reduced my driving to once a week, and I fill my car's tank less than once a month. While other locations such as the Edgewater Hotel and Pioneer Square score higher than Tableau's headquarters, it's only because the scoring algorithm doesn't consider accessibility to a giant Lenin statue, a glowing rocket or a troll under the bridge.
Can You Improve this Graph?
One of the blogs I read regularly is Flowing Data, which discusses effective visualization techniques for making sense of data. A recurring topic is a challenge to the readers: can you improve this graph?
The most recent challenge at Flowing Data is a graph that attempts to demonstrate a correlation between suicide rates and unemployment levels in Japan. Nathan identifies some areas for improvement and links to the source data, which I've used to build a Tableau visualization. You can see my results in the attached image.
The first step I took was to transpose the row/column orientation of the Excel file, and then connect to it with Tableau. Both the "Unemployment Rate" and "Suicide Rate" have missing data points, which were fairly straightforward to resolve. In the former case, I converted "Unemployment Rate" to a numeric Measure instead of a textual Dimension, and then filtered the data to start at the year 1980. I created a simple line graph to show the unemployment rate against time, and used "Suicide Rate" to control the width of the line. To fill in the missing data points, I used a Table Calculation in Tableau to make a moving window for the suicide rate, averaging up to two data points within +/- 4 years.
I've attached a Tableau 4.0 Packaged Workbook for Beta users to explore. One week from today we release Tableau 4.0, and you will be able to download the free trial if you're interested in exploring Tableau Desktop!
Raising the Bar: Team-Oriented Brainstorming with Tableau
"Insight brings value out of data," explains George Smirnoff. As Managing Director at Trexin Consulting, George enlists a multidisciplinary staff for collaborative root-cause analysis. Tableau is well suited for the iterative process of developing insight, and is an exciting centerpiece of his teams' dynamic brainstorming sessions.
His teams have investigated unexplained revenue shortfalls, examined securities fraud and shored up corporate defense strategies. This often revolves around outlier analysis: when slicing data in different ways, the same trend becomes apparent. Using simple rules to segment the data helps isolate outliers by establishing conditions which become red flags in combination.
George's presentation was exciting and fast-paced, drawing in excellent questions from the audience. He paced the presentation with some humor as well, making a dig at lawyers' superiority complex: "they are looking at this awesome Tableau stuff and acting like it's normal!" For some time afterward, a number of clustered conversations lingered in the room; customers with similar backgrounds were discussing their successes with Tableau in their own interactive settings. One customer encouraged another to "go for it" in an upcoming, high-profile storytelling meeting - take Tableau to the CEO, and leave PowerPoint as an afterthought.
Some key takeaways for successful collaborative sessions with Tableau:
- Accept that you will find some erroneous data, and resolve to address it as soon as possible.
- Explain the nature of the meeting in advance to the participants: it is a problem-solving session, not a blame-finding one. Defensiveness can completely kill the productivity of these sessions.
- Explore the data in different ways to find recurring outliers. Beware of sampling issues, since aggregations of low-count data will have a high margin of error.
Understanding Surveys using Highlight Tables
Surveys on topics such as customer satisfaction are rich with qualitative data, but analysis often requires quantitative comparisons, aggregation, etcetera. Steve Wexler, Director of Research at the eLearning Guild, discusses how some straightforward techniques in Tableau lead to "visualizations that people can grok from the back of a conference room."
The first samples of customer survey results that Steve demonstrated used stacked bar charts to reveal the proportion of responses in each of the categories, such as "Disagree" or "Strongly Agree". Sorting the data on one of the categories could reveal strong customer preference across a range of products, for example. However overall customer satisfaction should take into account all categories, and stacked bar charts made the remaining categories difficult to visually compare. The root of this problem is the qualitative nature of the data.
Steve tackled this challenge by building calculated fields to convert responses into quantitative values on a 0-5 scale, known as a Likert scale. This allows for aggregation such as averages to measure responses across a range of questions. To demonstrate this, Steve showed results from the eLearning Guild research which compared corporate plans / progress in developing mobile learning platforms versus these corporations' opinions of the value of mobile learning.
To render this in a way that could be grokked from a distance, Steve turned to a data visualization known as highlight tables. Like heatmaps applied to textual tables, highlight tables quickly identify the magnitude of values in each cell. While one dimension separates responses to each question, the second dimension of the table could be used in many ways: for example to aggregate responses by company size, by mobile platform preference, or by the company's plans for mobile learning. In interacting with the audience, Steve demonstrated that the survey response trends and correlations are clearly visible.
Compare and Contrast: Focusing on Data, not Tools
Under the leadership of Cindy Cedlacek, Director of Data Administration and Reporting at Cornell University, her team is on track to deliver Key Performance Indicators for the university using both Tableau Desktop and Tableau Server. In her words, discovering Tableau was "the best thing that ever happened for the project."
The contrasting examples below succinctly describe her success with Tableau versus the prior systems and tools used at Cornell.
Before: 90% time was spent developing custom tools and subsequently working around their limitations.
After: Using Tableau, Cindy's team can focus 90% of their time where it is best spent - with the data.
Before: The original implementation of Key Project Initiatives used in-house tools over an 8 month period with limited success. The team was exhausted by the effort, and some no longer wanted to participate in the project. Developing reports left analysts with the attitude of: "here, take this report - I never want to see it again!"
After: A successful Tableau implementation took only four months of effort. Cindy described how she revisited a pet project abandoned under the grind of the old process: "I wanted to get back to this project with Tableau!"
Before: The team spent significant time on workarounds for limitations in the original tools that were never addressed.
After: Tableau has "INCREDIBLE technical support! I have never seen technical support like this in my life.
I spent some time with Cindy afterward to discuss her experience. She's grateful to have Tableau products -- and we're grateful to have customers like her!
Battling Anecdote with Analysis using Tableau
Jon Nakamoto, M.D., Ph.D. describes a key challenge of his job as battling the anecdotes and innuendos that fuel his customers' stereotypes about inherent inefficiency. As Managing Director at the Quest Diagnostics Nichols Institute, his successes with Tableau have inspired a sense of trust and partnership with his very demanding customers.
Dr. Nakamoto prefers analysis over anecdote, and true to form much of his presentation involved live storytelling with Tableau! He demonstrated how Quest helps customers identify operational efficiency problems, for example with medical test turnaround-time, whose solutions are often surprisingly simple. Dr. Nakamoto showed how a simple day-of-week view revealed a customer's peak volume each Tuesday for medical test processing, elucidating the need for increased staffing. Exploring a bit deeper, he examined time-of-day for test processing to reveal that much of Tuesday's volume came from sample collection late in the day on Monday, when it could reasonably be pulled in earlier on that day.
Typically within a week of customer meetings they have demonstrated substantial improvements to their processes, and within four weeks they have completed their implementation. Quest's presentations to their customers are a lot like Dr. Nakamoto's presentation today: using Tableau for a live analysis of their data allows immediate answers to questions, fueling decisive and effective meetings.
What is Dr. Nakamoto looking forward to most in the conference? He expects the hands-on training to help break some of his bad habits, or "Tableau ruts", for performing data analysis in Tableau in a rigid fashion as one would with Excel. Tableau's ease of non-linear exploration allows for successively better insight, as each answer prompts new questions and deeper understanding of the data.
Liveblogging the Tableau Customer conference
Welcome to the first-ever Tableau Customer Conference! Whether you are joining us in person or following this event remotely, follow our blog posts as Tableau employees liveblog this exciting event across a variety of social networks.
Several Tableau employees will liveblog on their favorite social networks, ranging from LinkedIn and Facebook to Twitter and Flickr. Search for the Social Media Tag #TableauConf08 (Twitter, Ustream.tv, YouTube and Flickr), or follow the links below:
Tableau Software Blog
Twitter
LinkedIn
Facebook
While you are visiting these groups feel free to link, join, friend or subscribe to keep up to date. And if you're sharing your experiences from the conference, be sure to include the Social Media Tag #TableauConf08.
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