Blend, cleanse and prepare data for analytics, reporting or data modernization efforts
To foster digital business transformation, banks need to redesign both internal and customer-facing processes to embed data-driven decision-making.
Watch list screening has been one of the rules with highest false-positive rate. Watch list screening has been one of the pillars for know your customer (KYC) and anti-money laundering (AML) regulatory requirements since the beginning. It was introduced to prevent known criminals (or known high risk entities) from utilizing
Data management has never been the shiny object that caught the imagination of the mainstream. And let’s be honest, it's not nearly as interesting as analytics, machine learning or artificial intelligence. In fact, entire movies get created about analytics, and people actually pay to see them! Data management? Not so
Hace algunas semanas les contábamos algunos de los secretos que están utilizando los grandes del retail en Latinoamérica para mejorar la planificación de la demanda. Lo hacíamos con base en ejemplos presentados durante el NRF 2020, para muchos el principal evento de innovación y tecnología aplicadas al sector retail o
Learn how to get started with self-service data prep in these go-to articles.
This article discusses how to use SAS to filter variables in a dataset based on the percentage of missing values or duplicate values. The missing value statistics can be implemented by either DATA step programming on your own or reusing the existing powerful PROC FREQ.
Jeff Stander helps us understand the different options of preparing data for analytics.
A business glossary improves data quality – one of the top five ways it makes analytics better.
Think differently about using storage in the cloud with your SAS Grid jobs, and learn about SAS Cloud Data Exchange for security/caching strategies
When I started working in data and analytics 30 years ago, information security wasn’t high on the agenda for organizations. That's changed with the rise of the Internet, and now that cloud is becoming more and more prevalent in organizations, information security is no longer just the domain of specialists
Por Javier Rengifo Gerente de Customer Advisory para SAS Colombia y Ecuador El éxito en el desarrollo e implementación de las iniciativas analíticas empresariales requiere que se tengan propósitos claros, una alineación con los objetivos del negocio, una adecuada captura y calidad de datos, una gestión y mejoramiento continuo de
Surprise! The data team does more than you think to implement certain legislative actions.
In part 1 of this post, we looked at setting up Spark jobs from Cloud Analytics Services (CAS) to load and save data to and from Hadoop. Now we are moving on to the next step in the analytic cycle, scoring data in Hadoop and executing SAS code as a
Put simply, data literacy is the ability to derive meaning from data. That seems like a straightforward proposition, but, in truth, finding relationships in data can be fraught with complexities, including: Understanding where the data came from, including the lineage or source of that data. Ensuring that the data meet compliance
Just when you think you’ve seen it all, life can surprise you in a big way, making you wonder what else you've missed. That is what happened when I recently had a chance to work with the SAS® Scalable Performance Data Server, a product that's been around a while, but
.@philsimon chimes in about data-related accomplishments and challenges.
This article is not a tutorial on Hadoop, Spark, or big data. At the same time, no prerequisite knowledge of these technologies is required for understanding. We’ll give you enough background prior to diving into the details. In simplest terms, the Hadoop framework maintains the data and Spark controls and
David Loshin gives CIOs 4 suggestions for shaping successful digital transformation initiatives.
You can now easily embed a python script in a SAS decision with SAS Intelligent Decisioning. If you want to execute in MAS, you do not need to wrap it in DS2 anymore. The python code node does it for you. Here is how you can achieve it in less than 5 minutes.
In parts one and two of this blog series, we introduced the automation of AI (i.e., artificial intelligence) and natural language explanations applied to segmentation and marketing. Following this, we began marching down the path of practitioner-oriented examples, making the case for why we need it and where it applies.