In a world of fast, convenient, personalized service, customers expect businesses to go the extra mile to meet their expectations. How can organizations establish closer relationships, especially in crowded markets? With industry disruptors nipping at their heels, established companies are looking for fresh ways to strengthen relationships with new and
Most insurance companies depend on human expertise and business rules-based software to protect themselves from fraud. However, people move on. And the drive for digital transformation and process automation means data and scenarios change faster than you can update the rules. Machine learning has the potential to allow insurers to
Datenmodellierung ist sicher eine der komplexesten Aufgaben beim Aufbau eines Data Warehouse (DWH). Dies liegt vor allem daran, dass in der Phase der Modellierung unterschiedlichste Analyseanforderungen zu berücksichtigen sind. Und teilweise ändern sich diese Anforderungen schneller, als man mit dem Datenmodellieren vorankommt. Aktuelle Gründe für ständige Änderungen sind zum Beispiel
For every project in SAS®, the first step is almost always making your data available. This blog shows you how to load three of the most common input data types—a data set, a text file, and a Microsoft Excel file—into SAS® Cloud Analytic Services (CAS) tables. The three methods that
This article is a follow-on to a recent post from Jeff Owens, Getting started with SAS Containers. In that post, Jeff discussed building and running a single container for a SAS Viya runtime/IDE. Today we will go through how to build and run the full SAS Viya stack - visual
Want to learn SAS programming but worried about taking the plunge? Over at SAS Press, we are excited about an upcoming publication that introduces newbies to SAS in a peer-review instruction format we have found popular for the classroom. Professors Jim Blum and Jonathan Duggins have written Fundamentals of Programming
In the article Serverless functions and SAS Viya - a good match I discussed using serverless functions to deliver SAS Viya applications. Ignoring all the buzz words, a serverless function boils down to a set of REST APIs. So, if you tried the example you are now a REST API
Customer risk rating models play a crucial role in complying with the Know Your Customer (KYC) and Customer Due Diligence (CDD) requirements, which are designed to assess customer risk and prevent fraud. Today, the most common form of the Customer Risk Rating model is a score-based risk rating model. This
A persistent analytics talent gap creates big opportunities for people who can wield analytics to help organizations make better decisions. Innovative analytics users and students who are rushing to fill that gap, and those who teach them, are being honored this week at SAS Global Forum. A special Sunday event
I've previously written about how to deal with nonconvergence when fitting generalized linear regression models. Most generalized linear and mixed models use an iterative optimization process, such as maximum likelihood estimation, to fit parameters. The optimization might not converge, either because the initial guess is poor or because the model
Box plots are a great way to compare the distributions of several subpopulations of your data. For example, box plots are often used in clinical studies to visualize the response of patients in various cohorts. This article describes three techniques to visualize responses when the cohorts have a nested or
Plotting just your data often helps you gain insight into how it has changed over time. But what if you want to know why it changed? Although correlation does not always imply causation, it is often useful to graph multiple things together, that might logically be related. For example, recessions
Machine learning differs from classical statistics in the way it assesses and compares competing models. In classical statistics, you use all the data to fit each model. You choose between models by using a statistic (such as AIC, AICC, SBC, ...) that measures both the goodness of fit and the
This issue's preview is provided by Ralph Culver, Foresight's manuscript editor. Preview of Winter 2019 issue of Foresight The Winter 2019 issue of Foresight—number 52—kicks off with Simon Clarke’s enthusiastic review of The Little Illustrated) Book of Operational Forecasting by Dr. Steve Morlidge. Every year brings us new, inexperienced business-operations
Amidst the growing popularity of modern machine learning and deep learning techniques, one of the biggest challenges is the ability to obtain large amounts of training data suitable for your use case. This post discusses how the analytical approach for Named Entity Recognition (NER) can help.
After almost 32 years, I am retiring from SAS.
Emerging technologies enable retailers to differentiate with data and analytics that enhance the customer experience. In four key ways, retailers can partner with the analytics using data associated with past and present interactions and, through systemic innovation capitalize on future customer interactions.
Who says technical people can't have fun!?! Similar to Throwback Thursday / #TBT (when people post one of their old/nostalgic photos on social media), I like the tradition of Fun Friday when I use a fun data topic to test our software - a test can be just as rigorous using
Think that the company has let up in the last two years? Think again.
Over the years, the US has drilled for crude oil in several locations, such as Pennsylvania, Texas, Alaska, and the Gulf of Mexico. A few years ago, as the US started drilling more in North Dakota, there were forecasts that we would surpass Saudi Arabia in crude oil production. And recently,
To succeed in any data-focused hackathon, you need a robust set of tools and skills – as well as a can-do attitude. Here's what you can expect from any hackathon: Messy data. It might come from a variety of sources, and won't necessarily be organized for analytics or reporting. That's
The data I was analyzing was about “trust.” Maybe that’s what got me thinking about Stephen Sondheim, the Broadway composer and lyricist of musicals like Sunday in the Park with George and Into the Woods and the lyricist for West Side Story. Trust is a heavy emotional topic. Developmental psychologists
A frequent topic on SAS discussion forums is how to check the assumptions of an ordinary least squares linear regression model. Some posts indicate misconceptions about the assumptions of linear regression. In particular, I see incorrect statements such as the following: Help! A histogram of my variables shows that they
When you realize your organization has a forecasting problem, what do you do to solve it? In particular, if you realize you need new forecasting software, how do you begin to find it? All too often, the first step in a software selection process is the Request for Proposal (RFP)
Have you ever lied about your age? When you were younger, perhaps you exaggerated your age to watch an R-rated movie, buy cigarettes, get into a night club, or drink alcohol? And when people reach their 30s or 40s, they might subtract a few years when people ask their age.
During SAS Global Forum 2018, I sat down with four SAS users to get their take on what makes them a SAS user. Read through to find valuable tips they shared and up your SAS game. I’m sure you will come away inspired, as you discover some universal commonalities in being a SAS user.
The sweep operator performs elementary row operations on a system of linear equations. The sweep operator enables you to build regression models by "sweeping in" or "sweeping out" particular rows of the X`X matrix. As you do so, the estimates for the regression coefficients, the error sum of squares, and
If you think machine learning will replace demand planners, then don’t read this post. If you think machine learning will automate and unleash the power of insights allowing demand planners to drive more value and growth, then this article is a must read.
Sebastian Dziadkowiec and Piotr Czetwertynski presented the talk “An Agile Approach to Building an Omni-Channel Customer Experience” at SAS Global Forum 2018. Keys to building a successful and future-proof omni-channel customer experience Most organizations acknowledge that building a seamless and consistent customer experience is critical to long-term success. The big question is:
Much has been written about the value that North Carolina’s Criminal Justice Law Enforcement Automated Data Services (CJLEADS) system has brought the state’s court personnel and law enforcement officers. CJLEADS integrates dozens of NC criminal justice and law enforcement data sets, a vast improvement over the state’s legacy processes. Law
