Obesity is common, serious and costly. More than one-third (34.9% or 78.6 million) of U.S. adults are obese - see here Journal of American Medicine (JAMA). The estimated annual medical cost of obesity in the U.S. was $147 billion U.S. dollars in 2008; the medical costs for people who are obese were $1,429 higher than those of normal weight.Read More
Tags: internet of things
According to Goldman Sachs, the Internet of Things (IoT) is going to be biggest wave triggered by the invention of the internet. The 1990s’ fixed Internet wave connected nearly a 1 billion users, while the 2000s’ mobile wave connected another 2 billion+ users. However IoT has the potential to connect 10X as many (28 billion according to some estimates) users to the Internet by 2020 - where these "users" are inanimate objects ranging from bracelets to cars.
However, there are several major challenges to overcome before we can realize the potential of this exponential connectedness.
- Improvements in sensing technology
- Improvements in battery or power technology (scaling down, instead of up)
- Increase in computing power
- Improved data communication bandwidth
- Ingesting massive amounts of data within a short time span and finally, the most critical
- Analyzing and making sense of this data
Tags: internet of things
How should analytics help businessesRead More
What is Analytics?
Analytics is the professional field of discovering insightful relationships amongst data. The insights can be used to control or predict outcomes of processes or systems of any type based on what has happened in the past. When the data is simple, we can figure out causes and effects intuitively. However, in real world situations with numerous potential influences on an outcome, such relationships or correlations are less obvious or easy to determine. This is when analytics becomes useful.Read More
In the first article of this series, we described the choice of variables for starting a multiple linear regression model. In the second article, we discussed how to build the model and evaluate/explain it to a business user. In this final article, we will make sure that we can correctly interpret the coefficients of the model. But before that we will need to ensure that the coefficients are statistically valid or meaningful.Read More
Steve Ballmer recently came out strongly in favor of machine learning, calling it the next era of computer science. Recently another business legend, Elon Musk, said the rapid pace of developments in artificial intelligence signals the end of humanity. So who is right? Musk may have a point - humans struggle to balance the benefits and risks of any new path breaking technology. For example, we are still struggling to balance the pros and cons of nuclear technology more than 70 years after its breakthrough. But in this case, my vote is for Ballmer, as much as I recognize the dangers posed by the rush of any new technology. The truth as always is in between the extremes, as the Buddha says. I think Musk's fears of a Terminator style SkyNet may be a tad overblown. Here is my reasoning with a realistic example.Read More
The most successful companies today are the ones who have acted upon their data assets by leveraging advanced analytics and big data technologies. If you think of Apple, Netflix, Google or FaceBook, the one thing they all have in common (in addition to being "tech" companies) is that they have highly evolved analytics strategies and practices. One can safely say that all of these companies are "data" companies, and not device or video rental or search or social media companies. Add to this list, a company like Domino's Pizza. I had the opportunity to visit their campus yesterday and was amazed by how data driven the business is. In fact, Domino's proudly announces in their lobby that, outside of Amazon and Google, they are the largest consumer of big data analytics. So who exactly are the actual users of all of the big data and analytics? Today the largest users of big data in business are the folks in marketing. They need to leverage Hadoop for everything from sentiment analysis to real time product recommendations.Read More
In the previous post, we described the first couple of steps required for setting up multiple linear regression models for prediction. These steps focused mainly on exploring the predictors or variables in the data set that would influence the outcome. It was also mentioned that wrapper type feature selection methods such as forward selection or backward elimination are usually used to select the variables which will go into the model. In this article, we will look at how one of these methods can be employed to build a model and once the model is built how to quantify the model performance. In particular, we will explain the differences between using the adjusted R2 and standard error of the regression estimate to evaluate model performance.Read More
Today’s business world is filled with opportunities to review the information on the way that business organization, collect, and present information. If you were to evaluate your organization’s current ability to report and analyze information, what analytic stage would you say your organization is at? According to the now-classic book "Competing on Analytics" by Thomas Davenport and Jeanne Harris, there are 5 main stages in the analytics maturity model:Read More
Regression models have been around for a couple of centuries now, and yet the utility of the technique is unsurpassed because it is a good candidate for any present day application which requires a numerical prediction or statistical abstraction. In analytics consulting practice, it is usually the go-to technique whenever there is a need for a tool to explain to customers what their data means.Read More