Cancer is a pretty scary word and is something that affects most of us, either directly or indirectly. It is a complex condition with possible causes including: poor diet, environmental toxins, genetics, infections, viruses, stress, tobacco use, lack of exercise and more. While no one knows the exact cause of
When simulating data or testing algorithms, it is useful to be able to generate patterns of missing data. This article shows how to generate random and systematic patterns of missing values. In other words, this article shows how to replace nonmissing data with missing data. Generate a random pattern of
The State Fair in North Carolina is just a few miles from SAS headquarters, and therefore it's virtually impossible for it to slip by without me noticing it. There are two aspects of the fair that usually get lots of news coverage - what's the latest fair-food, and did we
.@philsimon says that data-governance professionals will need to be more agile than ever.
This is the third of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. In the first two articles of the series we drilled down to Data Management and Data Quality as the basis for insurance fraud detection analytics and also to the Business
SAS Enterprise Guide has come a long way since version 1.0 was released in 1999! Are any of you original users that remember the Help characters, Clippy, Peedy or Merlin? I was working as a statistician for another company that year, and I attended a SAS user group meeting where
The Aphorisms of the New Defensive Paradigm I want to finish this blog series with a set of 7 aphorisms – concise statements of principle – that characterize the new Defensive paradigm for business forecasting. The first is that: Aphorism 1: Forecasting is a Huge Waste of Management Time This
A Spider Plot is another way of presenting the Change from Baseline for tumors for each subject in a study by week. The plot can be classified by response and stage. Another way of displaying Tumor Response data was discussed earlier in the article on Swimmer Plot. This article is prompted
Requirements that are the most easily described can often be the most difficult to implement. I’m referring to requests like: Display a gauge with the most recently collected metric. Plot a 18 month rolling window of profit. Display last month’s products percent of total metrics for visual comparison. Okay, so
Analytics provides better insights into why something happened, or helps provide decision makers with information about what will happen in the future. That allows organizations to act now to improve outcomes instead of reacting to events after they happen. But it takes more than analytics alone. Achieving this level of
Living to 100 isn't as simple as just paying a certain amount of money for your healthcare. But that is an interesting aspect of longevity, so let's have a look at the data ... In my previous blog post, we analyzed how much people from various countries spend on healthcare.
Academic Research In an approach akin to FVA analysis, Paul Goodwin and Robert Fildes published a frequently cited study of four supply chain companies and 60,000 actual forecasts.* They found that 75% of the time an analyst adjusted the statistical forecast. They were trying to figure out, like FVA does,
I've written several articles about scatter plot smoothers: nonparametric regression curves that reveal small- and large-scale features of a response variable as a function of an explanatory variable. However, there is another kind of "smoothness" that you might care about, and that is the apparent smoothness of curves and markers
Have you ever needed to run code based on the client application that you are using? Or have you needed to know the version of SAS® software that you are running and the operating system that you are running it on? This blog post describes a few automatic macro variables
Typical Business Forecasting Process Let’s look at a typical business forecasting process. Historical data is fed into forecasting software which generates the "statistical" forecast. An analyst can review and override the forecast, which then goes into a more elaborate collaborative or consensus process for further adjustment. Many organizations also have
In recent healthcare blogs I’ve looked at the need to drive more value from the UK’s National Health Service (NHS) and how this relies upon the ability to make decisions based on robust, data-driven insights. But what value will these decisions have if they're not founded on a mature data
In a previous blog article, Risk Data Aggregation and Reporting – Why now more than ever? – Part 1, I have discussed the requirements of the paper of the Basel Committee numbered 239 and one of the reasons the execution or application of the principles discussed therein is important for
As an instructor for SAS, I receive a wide variety of queries before, during and after delivering my courses. Most frequently, I am asked questions such as: Should I learn SAS programming or a point and click tool instead? I know lots of code, should I go straight to the
Over the past few weeks I have heard about the "Consort Diagram". This was mentioned in a Communities article, and also by a couple of users separately. This topic was also covered by Anusha Mallavarapu and Dean Shults from Cytel in a poster at PhUSE 2016 as shown on the
Data governance plays an integral role in many enterprise information initiatives, such as data quality, master data management and analytics. It requires coordinating a complex combination of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data stewardship and change management. With so much overhead involved in
The Means of the Defensive Paradigm The Defensive paradigm pursues its objective by identifying and eliminating forecasting process waste. (Waste is defined as efforts that are failing to make the forecast more accurate and less biased, or are even making the forecast worse.) In this context, it may seem ridiculous
The study of social networks has gained importance over the years within social and behavioral research on HIV and AIDS. Social network research can show routes of potential viral transfer, and be used to understand the influence of peer norms and practices on the risk behaviors of individuals. This example analyzes the
In a previous blog post, I demonstrated combining the power of SAS Event Stream Processing (ESP) and the SAS Quality Knowledge Base (QKB), a key component of our SAS Data Quality offerings. In this post, I will expand on the topic and show how you can work with data from
Why the Attraction for the Offensive Paradigm? In addition to the reasons provided by Green and Armstrong, I'd like to add one more reason for the lure of complexity: You can always add complexity to a model to better fit the history. In fact, you can always create a model
Eighteen years ago, on a cold and rainy day, I was frustrated. It had been three weeks since I made the discovery. Yes, the ultrasound declared it a solid mass and not a cyst. No, the mammogram didn’t know exactly what it was. I agreed to wait eight weeks to
Fresh from chairing the Foresight Practitioner Conference on “Worst Practices in Business Forecasting,” hosted two weeks ago at the Institute for Advanced Analytics at North Carolina State University, Foresight editor-in-chief Len Tashman previews the Fall 2016 issue. Preview of the Fall 2016 issue of Foresight In the provocative article, “The
A previous post discusses how the loess regression algorithm is implemented in SAS. The LOESS procedure in SAS/STAT software provides the data analyst with options to control the loess algorithm and fit nonparametric smoothing curves through points in a scatter plot. Although PROC LOESS satisfies 99.99% of SAS users who
This is the second of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. While Data Management and Data Quality are the basis for every analytical journey, and this becomes even more true for fraud detection analytics, the domain knowledge and business expertise plaid
Implications for the Offensive Paradigm The worldview promulgated by the Offensive paradigm is that if we only had MORE – more data, more computational power, more complex models, more elaborate processes – we could eventually solve the business forecasting problem. But this just doesn’t seem to be the case. Operating
Utility leaders are struggling with a world that's quickly changing and barely recognizable from the one they knew growing up. Many of the old assumptions are gone, and the business model upon which careers have been built is on the verge of disappearing. So what does the internet of things
