Tag Archives: data analysis software

Tableau and Dynamical System Models

Kenneth Black has a post in his blog

http://3danim8.wordpress.com/2014/04/26/a-look-back-at-my-mmr-groundwater-modeling-work-circa-2003/

about analyzing and visualizing dynamic system models.

He writes:

“One of my biggest regrets is that I had to work the first 20 years of my career without Tableau desktop software.  Looking back, I can see how so many computer codes I designed and wrote, or software teams that I directed, were necessary because we didn’t have a tool like Tableau to help us perform our analysis…..

There were capable tools we used …, but much of the quantitative analysis had to be programmed on a case-by-case basis.  If I had Tableau throughout my career, things would have been much easier, more insights would have been possible, and better models could have been built.

Now that Tableau is here, I’d like to take another crack at analyzing some of my earlier work.”

I had a similar experience and in a comment to his post I write:

“I found this post very interesting. I reminded me of how I became interested in dynamical systems. I happened to come across an article about a hydrologic model of the Okefenokee Swamp in Georgia. The articIe contained differential equations representing the model. I developed various dynamical models of the swamp in various programs like STELLA, Vensim, Mathematica, and even LiveMath (which for some reason still is close to my heart). When I later became chief of Acute Psychiatric Services at a Psychiatric Hospital I developed a lot of models of health services, especially acute psychatric services, in the hope that these models would increase my understanding and the understanding of health service administrators and politicians and thus lead to an improvement in the sevices. This however did not happen and has still not happened. Now, STELLA has quite good presentation methods, so I thought that the fact that administrators and politicians did not see the light of day immediately was an indication that they were not really interested in solving the serious problems of the acute psychiatric health services in Norway but mainly interested in decreasing the cost of those services. This is surely at least partly true. After reading your post I began to wonder whether it might partly be due to their inability to gain the necessary insight and understanding from the STELLA presentations and my comments on them. Perhaps it would be possible to increase their insight and understanding by presenting the underlying data and the data from simulations in Tableau Workbooks. Perhaps the clarity of these presentations and their availability to the public would make it imposible for them to avoid doing something about the problems.”

Tableau Integration with Trifacta

Trifacta has announced deep integration with Tableau. Tableau users now have the option of writing the output of Trifacta data transformations directly to a Tableau Data Extract format.

Trifacta provides Tableau users with an intuitive Data Transformation Platform for Hadoop so they can more efficiently transform and analyze common data formats in Hadoop

The integration between Trifacta and Tableau removes a key barrier between the raw, semi-structured data commonly stored in Hadoop and the self-service process for analyzing, visualizing and sharing of insights provided by Tableau.

Working with big data poses specific challenges. The most significant barriers come from structuring, distilling and automating the transfer of data from Hadoop.

Water Shortage

One of the most important question/problem areas for humanity is water and especially freshwater. The available amount of freshwater is decreasing and the problems caused by this are increasing. The risk associated with freshwater shortage is increasing.

“Water scarcity is one of the defining issues of the 21st century. …In its Global Risks 2013 Report, the World Economic Forum identified water supply crises as one of the highest impact and most likely threats facing the planet.”

The World Economic Forum ranks water supply crises as being more likely and having a greater impact globally than the risk of food shortage crises, terrorism, cyber attacks, and geophysical destruction.

Fresh water supply crises are clearly of great importance and it is imperative to increase knowledge about them by generating new data by research, generating new knowledge from the data, and spreading this knowledge. It is also imperative to apply the knowledge to making decisions and implementing the decisions by actions designed to decrease the risk of water crises.

In view of this I have been working on a water shortage project using data from aquastat and Tableau Software in the hope that I may contribute to an increase in the knowledge about such crises and thereby decrease the risk of their occurrence.

You may read about this project under

Freshwater Supply Crises

Water Shortage

Tableau Software

During recent weeks and months I have been studying data and decision analysis in general and especially the use of Tableau Software for applying data analysis to large datasets in order to transform the data into knowledge which then can be used to answer important questions, solve important problems, make decisions and implement them by effective actions.

Tableau software has its roots in the Stanford University Science Department research project which is aimed at increasing people’s ability to rapidly analyze data. Tableau’s main approach to visual design is to connect to a data source, and drag data fields to its workspace.

Tableau Desktop is a software package for data analysis. It’s easy to learn, easy to use, and extremely fast. It allows you to use your natural ability to see patterns, identify trends and discover visual insights.

You can connect to data and perform queries without writing a single line of code. You can follow your natural train of thought as you shift between views with drag-and-drop technology.

You can connect directly to data for live, up-to-date data analysis or extract data into Tableau’s fast data engine and take advantage of breakthrough in-memory architecture, or do both, for 2, 3, or even 10 different data sources and blend them all together. Tableau has a large number of native connectors to data sources.  A list of connectors can be viewed at

http://www.tableausoftware.com/products/desktop?qt-product_tableau_desktop=1#qt-product_tableau_desktop

Multiple views can be combined into interactive dashboards. Data can be filtered and highlighted to show relationships between variables. Content can be shared using the web-based Tableau Server or Tableau Online. Content can also be embedded into website pages, including blogs.

Tableau has powerful analytical tools. You can filter data dynamically, split trends across different categories or run an in-depth cohort analysis. You can double-click geographic fields to put data on a map. In addition it can be integrated with R.

You can go deeper into your data with new calculations on existing data. You can ake one-click forecasts, build box plots and see statistical summaries of your data. Run trend analyses, regressions, correlations, ….

There is a large amount of material on Tableau and its application to data analysis available on the Tableau website (http://www.tableausoftware.com/), in blogs, and in a number of books. Some of these books are available on Kindle.

Tableau is an ideal analysis and visualization tool in that it possesses the following attributes:
Simplicity – easy for non-technical users to master
Connectivity – seamlessly connects to a large variety of datasources
Visual competence – provides appropriate graphics
Sharing – facilitates sharing of knowledge, understanding and insight
Scaling – handles large data sets