12月 042018

In this blog series, we’ve spoken directly to professors to find out why it’s so important to teach analytics, their advice for students, and to learn how they create interest in analytics programs at their universities. For this third and final post, we’ll hear how SAS has played a role [...]

Two professors’ perspectives on SAS and the future of analytics was published on SAS Voices by Georgia Mariani

11月 162018

In my first two posts of this blog series, we heard why two students chose to pursue a STEM field and what appealed to them about data science. We also heard how they put their knowledge to work on a real-world data science project. Today, we'll hear their advice to future [...]

Two students’ advice on data science, SAS and more was published on SAS Voices by Georgia Mariani

11月 102018

"The customer is always right," was popularized by pioneering, successful retailers such as Harry Gordon Selfridge, John Wanamaker and Marshall Field. I remember a variation on this idea — Rule 1. The customer is always right. Rule 2. If the customer is wrong, go back to Rule 1.

This fundamental premise of customer service remains true regardless of channel: brick-and-mortar store, mobile device or website.

Emerging technologies provide retailers the opportunity to differentiate themselves with data and analytics that enhance the customer experience. Retailers can partner with the analytics using data associated with past and present interactions and through systemic innovation can capitalize on future customer interactions.

SAS has been a Red Hat partner for more than 15 years. Its retail customers use Red Hat technologies across many parts of their organizations. Red Hat Enterprise Linux is the preferred choice for many SAS retail customers because it provides a stable, reliable platform with a low total cost of ownership. SAS® Analytics, when paired with Red Hat Middleware, allows teams to seamlessly integrate retail data movement from the edge to the data center. In addition, both companies have developed a joint workflow to ensure that customer problems are resolved quickly.

But what does this partnership really mean to the retail customer?

Four factors that make retail analytics real

Better scalability. Seasonal factors can significantly impact the retail industry. A spike in demand around signature events -- planned and unplanned – can result in order-of-magnitude variations in the volume of data to be processed. The larger the data volume, the higher the compute and storage resources required.

That’s where cloud can come in. SAS Analytics with Red Hat open cloud technology allows retailers to scale their analytics up and out as their business climate evolves by automatically provisioning additional resources.

Faster time to analytics. The digital customer is not only motivated by the products available through the retailer but also to the overall shopping experience. A robust IT strategy has become even more important to the retailer.

Retailers need to continually develop new features that draw customers to the store for the experience. The goal is to entice a digitally-minded customer to get offline and come to the store. Red Hat solutions power DevOps implementations that speed time to market.

Increased flexibility. The digital world would be a lot simpler if everyone a single cloud solution. But it is a hybrid world out there with a multitude of workloads that are best suited for a diverse array of environments including bare metal, virtual machines, private and public clouds.

Workloads may need to be moved across these environments as well. Red Hat technologies allow retailers to virtualize their SAS analytics over a range of secure deployment options, including public, private, and hybrid clouds.

Added security. Data being such a precious commodity, digital retailers may have to be more concerned in some cases about the security and privacy of their customer’s data than the goods they sell! ‘Adversaries R Us’ are always on the prowl in the digital neighborhood, continuously innovating newer ways to penetrate the enterprise to access the customer data.

Prevention is better than cure, even when it comes to data security. With the SAS and Red Hat platform, customers benefit from continuous built-in security, offered end-to-end on trusted platforms and augmented by automated patching and proactive remediation in compliance with regulatory standards.

There you have it!

SAS and Red Hat provide a platform that supports every phase of the analytics life cycle to ensure that the Customer will always be right! Let me take it a step further. If such partnerships are not leveraged to benefit the customer, Retailers will be proven wrong!

How else can they drive a partnership with analytics?

The customer will always be right with open analytics was published on SAS Users.

11月 012018

I've worked at SAS for over 27 years and have often been asked: What does SAS do? or Why should I chose SAS? It all boils down to one question: Why SAS? While there are many approaches to answering this question, I recently came up with three short, yet powerful, [...]

Why SAS? was published on SAS Voices by David Pope

10月 312018

What skills will students need in order to pursue a lucrative career in analytics? I recently interviewed two professors to find out. In my first post, the professors discussed the importance of teaching and learning analytics. Today, we'll hear their top advice for students studying analytics, such as: get a [...]

Teaching analytics: Advice from two experts was published on SAS Voices by Georgia Mariani

10月 312018

An important step of every analytics project is exploring and preprocessing the data.  This transforms the raw data to make it useful and quality.  It might be necessary, for example, to reduce the size of the data or to eliminate some columns. All these actions accelerate the analytical project that comes right after.  But equally important is how you "productionize" your data science project.  In other words, how you deploy your model so that the business processes can make use of it.

SAS Viya can help with that.  Several SAS Viya applications have been engineered to directly add models to a model repository including SAS® Visual Data Mining and Machine Learning, SAS® Visual Text Analytics, and SAS® Studio. While the recent post on publishing and running models in Hadoop on SAS Viya outlined how to build models, this post will focus on the process to deploy your models with SAS Model Manager to Hadoop.

SAS Visual Data Mining and Machine Learning on SAS Viya contains a pipeline interface to assist data scientists in finding the most accurate model.  In that pipeline interface, you can do several tasks such as import score code, score your data, download score API code or download SAS/BASE scoring code.  Or you may decide – once you have a version ready - to store the model out of the development environment by registering your analytical model in a model repository.

Registered models will show up in SAS Model Manager and are copied to the model repository.   That repository provides long-term storage and includes version control.  It's a powerful tool for managing and governing your analytical models.  A registered version of your model will never get lost, even it's deleted from your development environment.   SAS models are not the only kind of models that SAS Model Manager can handle:  Python, R, Matlab models can also be imported.

SAS Model Manager can read, write, and manage the model repository and provide actions for model editing, comparing, testing, publishing, validating, monitoring, lineage, and history of the models.  It also allows you to easily demonstrate your compliance with regulations and policies. You can organize models into different projects.   Within a project it's feasible to test, deploy and monitor the performance of the registered models.

Deploying your models

Deploying, a key step for any data scientist and model manager, can assist in bringing the models into production processes. Kick off deployment by publishing your models.  SAS Model Manager can publish models to systems being used for batch processing or publish to applications where real-time execution of the models is required.   Let's have a look at how to publish the analytical model to a Hadoop cluster and run the model into the Hadoop cluster.  In doing so, you can score the data where it resides and avoid any data movement.

  1. Create the Hadoop public destination.

The easiest way to do this is via the Visual Interface.  Go to SAS Environment Manager and click on the Publish destinations icon:

Click on the new destination icon:


10月 112018

We hear a lot about data science nowadays, but do you ever wonder how it’s being used to help solve real-world problems? In my first post of this blog series, we heard why two students chose to pursue a STEM field and what appealed to them about data science. Today, we'll hear [...]

How two students used data science to analyze ‘real-world’ problems was published on SAS Voices by Georgia Mariani

10月 022018

Once a disaster is over, and the frenzy of news stories and social media posts has subsided, it can seem like the crisis has passed. However, for those Hurricane Florence survivors left with ruined homes and businesses, damaged schools and buildings, there remains a struggle to return to normalcy. As [...]

SAS mobilizes volunteers, analytics experts to support Hurricane Florence relief was published on SAS Voices by I-sah Hsieh