Tuesday, November 24, 2015

Recent Press Highlighting Cloud9 Charts

Two noteworthy articles about Cloud9 Charts have been recently published -- one by The New Stack and another by 451 Research.

The New Stack’s recent article, “Cloud9 Charts Unveils Analytics-as-a-Service Offering,” features a conversation with Cloud9 Charts’ CEO, Jay Gopalakrishnan, about the process of traditionally building BI tools, and how Cloud9 Charts has transformed the entire process into a simpler, faster and more efficient approach.

Information technology research and advisory company, 451 Research, published a review of Cloud9 Charts titled, “Cloud9 Charts emerges with reporting and visualization service for multi-structured data.” The research report goes into details about Cloud9 Charts’ product and current position in the market, its strategy and competitors in the analytics space. The full article is gated to subscribers.

Both articles emphasize the challenge of working with multi-structured data and how Cloud9 Charts solves that problem.

Special thanks to Susan Hall at The New Stack and Krishna Roy at 451 Research.


Wednesday, November 4, 2015

Introduction to Prediction Modeling

Cloud9 Charts now offers predictive analytics capabilities that can be applied to any data.

The data is passed through a variety of prediction models automatically - including Moving Average Models, Exponential Smoothing, Regressions and others - to determine the best fit based on historical data. 

Example: Let's take monthly stock prices for Amazon. The monthly stock prices looks like this:

Date, Price
06/01/16, 719.14
05/01/16, 683.85
04/01/16, 598.00
03/01/16, 579.00
02/01/16, 574.81
01/05/16, 633.79
12/01/15, 679.36
11/2/15, 628.35
10/1/15, 625.90
9/1/15, 511.89
8/3/15, 512.89
7/1/15, 536.15
6/1/15, 434.09
5/1/15, 429.23
4/1/15, 421.78
3/2/15, 372.10
2/2/15, 380.16
1/2/15, 354.53

1. Copy and paste the above dataset into https://www.cloud9charts.com/docs/predictive-analytics.html

2. Apply the following Cloud9QL into the query section:
select predict(Price, Date, 12/01/2015,1m,2)

This will backtest the dataset to determine the model with the best fit to then predict the price for future dates. (I hasten to add that historical data may not be always be a true indicator for future prices!)


Similar to the example above, you can predict any metric across any of your data.