Fusion Charts with Ruby on Rails

Seasonality is an important component of forecasting, and thanks to Google Trends, we can quickly get multi-year seasonality for a category such as 'Turtlenecks'. After downloading the CSV file, I wanted to reproduce the chart from the data on our web-page.

Fusion Charts is a fantastic charting package, although so far we can only afford the free version. I will show a particular trick of extending Fusion Charts Free to manipulate the X-axis label. The time series from Google Trends had 234 weeks of data, which meant 234 labels. These ran into each other, creating an illegible axis. Also, the default mode for Fusion Charts is to display the values of the data points. With a lot of data points, these crowd the graph. The results, as seen here, are a far cry from the beautiful graphs of Google Trends.

A little bit of Ruby goes a long way to quickly fix this problem. First of all, I need to tell Fusion Charts to suppress the value on the graph. This is done by setting the showValues parameter to 0 in the xml header. I am using the excellent Builder library, where I can pass this as a key-value pair :showValues => 0. Next, I want to show the week for every 13th week. The paid version of Fusion Charts has a parameter setting for this. But for the cheapskates of the world, Ruby to the rescue again. The trick is to set the showName parameter to 0 in every thirteenth line of the xml. Finally, let's rotate the labels so they are vertically oriented, reducing the overlap. This is done by passing another key-value pair to the xml head, :rotateNames => 1. The final code to generate the xml is posted below. The new graph, after the changes, is much cleaner.

counter = 0

xml.graph (:showValues => 0, :rotateNames=> 1) do

@trend_weeks.each do tweek

counter = counter + 1

if counter.modulo(13) == 0 xml.set :name => tweek.week_date, :value => tweek.index

else

xml.set :name => tweek.week_date, :value => tweek.index, :showName => 0

end

end

end

I am still new to Ruby, and am sure there is a more idiomatic way of doing this. Please post it in the comments.

Also, it took me a very long time to get Fusion Charts to work the first time in Rails. It was mostly little mistakes that I kept making, as well as an incomplete understanding of how routes worked in the 2.0 world. I may get along to posting a full tutorial on this subject. If somebody needs to get started quickly, please contact me and I can try and help you. Happy Charting!

The Evolution of Analytics

Enterprises today are awash in data. POS systems capture every transaction for a retailer, CRM systems maintain rich profiles of customer preferences and ERP systems maintain transactional information at the most granular levels. Until the mid-nineties, only very large companies, the ones who could afford SAP or Oracle, could afford a reliable data infrastructure. However, even the large companies had to rely on smaller, niche players for analytical and optimization capabilities. The late nineties saw a number of companies starting to offer analytical products around supply chain optimization (i2, Manugistics). Soon, companies followed with Price Optimization, based on advanced scientific algorithms (ProfitLogic, DemandTec, etc.). Analytical evolution had come a long way: with Oracle, SAP, Teradata providing the data infrastructure, reporting vendors (Cognos, MicroStrategy) providing ad-hoc access to information and knowledge, and specialized vendors providing prescriptive modeling and optimization capabilities.

What has changed in the last couple of years is that high level data infrastructure is no longer limited to large companies. The giants like SAP are now catering to small and medium businesses (SMBs) with simpler offerings, and players like NetSuite are now providing extremely robust ERP systems as a hosted service. While data infrastructure is now available to the masses, science-based analytical software is still very expensive to build and distribute, and mostly beyond the SMB's price-range. SMBs are still lagging in the analytical evolution, although the appetite for analytical excellence is definitely palpable.

A combination of factors is changing this picture, giving SMBs access to high quality analytics at a price point that they can afford. The biggest contribution comes from the open-source community, in the form of relational databases (MySQL, PostGres), web-development frameworks (Ruby on Rails, Django), and optimization programs and solvers (COIN-OR, Scipy). Ruby on Rails makes it possible to go from a business meeting to a working application in a matter of weeks, not months or years. Amazon Web Services provides the physical infrastructure which allows running the complex algorithms on their compute clouds for less than a cup of coffee at Starbucks. As these innovations are driving the costs down, and enhancing the supply of high-quality analytics, the demand for these services are increasing as well. Enterprises large and small are becoming increasingly aware of the power of data and data-driven models, and seeking to gain competitive edge through analytics (Competing on Analytics).

The future centers of analytical excellence may be quite different from what we are used to today. Today's paradigm is niche analytical companies selling to large enterprises. The sales cycle is long and complex, licenses and implementation costs run in six figures, and hosting and maintenance are just as expensive. The future may be ruled by small, nimble shops, providing high-quality, low-cost intelligence over the web, in a simple Do It Yourself (DIY) mode. Are you ready for the brave new world of analytics?