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Awesomely informative post on sascom magazine (whose editor I have I interviewed before here at http://www.decisionstats.com/interview-alison-bolen-sas-com/ – )
Great piece by Michael Ames ,SAS Data Integration Product Manager.
Also see SAS’s big data thingys here at
Solutions and Capabilities Using SAS® In-Memory Analytics
- High-Performance Analytics – Get near-real-time insights with appliance-ready analytics software designed to tackle big data and complex problems.
- High-Performance Risk – Faster, better risk management decisions based on the most up-to-date views of your overall risk exposure.
- High-Performance Liquidity Risk Management – Take quick, decisive actions to secure adequate funding, especially in times of volatility.
- High-Performance Stress Testing – Make faster, more precise decisions to protect the health of the firm.
- Visual Analytics – Explore big data using in-memory capabilities to better understand all of your data, discover new patterns and publish reports to the Web and iPad®.
(Ajay- I liked the Visual Analytics piece especially for Big Data )
Just came across this very awesome website.
Did you know there were six kinds of wordclouds in R.
(giggles like a little boy)
Tweets about some given topic
Tweets of some given user (ex 1)
Tweets of some given user (ex 2)
This guy – the force is strong in him
Data Analysis + Visualization + Statistics + R = FUN
| Contact Info
|About||Currently, I’m a postdoc in Rasmus Nielsen’s Lab in the Center for Theoretical Evolutionary Genomics at the University of California, Berkeley. I’m also collaborating with the Biology Scholars Program (BSP) at UC Berkeley, and I am affiliated to the Program on Reproductive Health and the Environment (PRHE) at UC San Francisco. In my (scarce) free time outside the academic world, I often work on collaborative projects for marketing analytics, statistical consulting, and statistical advising in general.|
Business Metrics (a partial extract from my upcoming book “R for Business Analytics”
Business Metrics are important variables that are collected on a periodic basis to assess the health and sustainability of a business. They should have the following properties-
1) What is a Business Metric-The absence of collection of regular update of the business metric could cause business disruption by incorrect and incomplete decision making.
2) Cost of Business Metrics- The costs of collection, storage and updating of the business metric is less than the opportunity costs of wrong decision making cause by lack of information of that business metric.
3) Continuity in your Business Metrics- The business metrics are continuous in comparing across time periods and business units- if necessary the assumptions for smoothing the comparisons should be listed in the business metric presentation itself.
4) Simplify your Business Metrics- Business metrics can be derived as well from other business metrics. If necessary and to avoid clutter only the most important business metrics should be presented, or the metrics with the biggest deviation from past trends should be mentioned.
5) Normalize your Business Metrics- Scale of the business metric units should be comparable to other business metrics as well as significant to emphasize the difference in numbers.
6) Standardize your Business Metrics- Dimension of business metrics should be increased to enhance comparison and contrasts without enhancing complexity. This means adding an extra dimension for analysis rather than a 2 by 2 comparison, to add time /geography/ employee/business owner as a dimension .
An interesting use case of technology for better health is HANA Oncolyzer at http://epic.hpi.uni-potsdam.de/Home/HanaOncolyzer
“Build on the newest in-memory technology the HANA Oncolyzer is able to analyze even huge amounts of medical data in shortest time”, says Dr. Alexander Zeier, Deputy Chair of EPIC. Research institutes and university hospital support from HANA Oncolyzer by building the basis for a flexible exchange of information about efficiency of medicines and treatments.
In near future, the tumor’s DNA of all cancer patients needs to be analyzed to support specific patient therapies. These analyses result in medical data in amount of multiple terabytes. “These data need to be analyzed regarding mutations and anomalies in real-time”, says Matthias Steinbrecher at SAP’s Innovation Center in Potsdam. As one of the aims the research prototype HANA Oncolyzer was developed at our chair in cooperation with SAP’s Innovation Center in Potsdam. “The ‘heart’ of our development builds the in-memory technology that supports the parallel analysis of million of data within seconds in main memory”, saysMatthieu Schapranow, Ph.D. cand. at the HPI.
research activities result in 500.000 or more data points per patient.
With the help of a dedicated iPad application medical doctors can access all data mobile at any location anytime.