Whether you’re applying for your first credit card or shopping for a second home – or anywhere in between – you’ll probably encounter an application process. As part of that process, banks and other lenders use a scorecard to determine your likelihood to pay off that loan. Naturally, this means
This post is the second in a series on data preparation based on a webinar about its role in the analytical life cycle. The first discussed how data preparation fit into the analytical life cycle. This post considers some trends in data preparation and some of the structures and processes
Learn about the latest product features, changes and upgrade information in new sections of SAS product documentation for SAS® 9.4 and SAS® Viya®. SAS' Kathryn McLawhorn tells us all about them.
The increased availability of data is opening up a new world of analytics to insurance companies. There are both new sources of data and also increased volumes of information, and together, these are being used to generate insights and influence decision making. This post, the second in a series on
Tool tip and drill-down functionality is commonly used to explore plot data in a graph, particularly on the web. Occasionally, you might even have the need to add this drill-down functionality to your titles or footnotes, possibly to reference more details or source information. The TITLE and FOOTNOTE statements in
They say "The Sun never sets on the SAS Empire" ... and it's true! There are SAS users all over the world, and SAS output & results could be in any language. Therefore, if you're a SAS programmer, you might need to know how to create SAS graphs with international
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
Guest blogger Khari Villela says data lakes are not a cure-all – they're just one part of a comprehensive, strategic architecture.
Wenn wir über künstliche Intelligenz (KI) und Ethik sprechen, dann beziehen wir uns nicht in erster Linie auf dystopische Anwendungen, in denen ein autonomer Roboter stur und ohne menschliche Kontrolle in Terminator-Manier Entscheidungen über Leben und Tod trifft. Das soll natürlich nicht heißen, dass ein kritischer Diskurs zum Beispiel über
Data preparation is often seen by companies as a difficult and dangerous job, one best left to IT. However, business departments often do not want to wait for their data, so thick SQL books and spreadsheet applications are booming in most offices. This does not really make sense, however you
Data may be expanding exponentially, but this expansion in itself is not the be-all and end-all. Data is very important, but only because it enables organisations to learn more about their customers and offer them a better service. Therefore – and this is crucial – data allows organisations to make
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.