Blogs
Is 2012 the year of Agile Analytics? Recent publications show growing interest in the application of Agile methods to analytics:
- Ken Collier, an Agile pioneer, tackles analytics in his aptly named new book Agile Analytics.
- A quick Google search surfaces a number of recent blogs and articles.
- Curt Monash recently published an excellent two-part blog on the subject.
Customers who leverage Netezza through SAS report radical performance gains. Here are just a few examples of how Netezza improves the performance of traditional SAS programs:
| Tags: | | | Permalink | | | 5 Comments |
Most Netezza customers use SAS, so it’s natural that customers ask about the best way to integrate SAS and Netezza. This article outlines key best practices to consider when implementing a solution with SAS and Netezza.
Suppose you could implement an analytics platform with comprehensive out-of-the-box capabilities, a flexible programming environment, good visualization capabilities and a growing body of skilled users. Suppose this platform leveraged a massively parallel architecture for high performance and scalability. And suppose you could do this without investing in software fees.
| Tags: | | | Permalink | | | 2 Comments |
Many analysts have a strong preference for commercial analytic workbenches such as SAS or SPSS. Both packages are widely used, respected by analysts, and each has strong advocates. The purpose of this article is to point out that analytic users can benefit from the performance and simplicity of Netezza in-database analytics without abandoning their preferred interface.
| Tags: | | | Permalink | | | 0 Comments |
Who are the users for analytic solutions? In developing solutions, we tend to think of one group of users, those who work with statistical packages like SAS or SPSS. In reality, every analytical solution has three distinct user communities whose needs must be addressed.
| Tags: | | | Permalink | | | 0 Comments |
Enterprises seeking to increase the volume and scale of deployed analytics frequently run into what we call the model migration bottleneck -- that's where analytical models developed offline in a statistical package must be deployed into a scalable IT-managed production environment. Customers and software vendors have tried numerous approaches to managing this constraint, but for many firms the solution is to physically recode and test the model -- a process that can take weeks or even months. When analytics are a key competitive tool and speed is of the essence that is not an acceptable approach.
Clients sometimes ask is of PMML is a solution. PMML, or Predictive Model Markup Language is a standard published by the Data Mining Group, an independent consortium of leading analytical software vendors, service providers, analytics consumers and thought leaders. An XML-based standard, PMML has evolved progressively since first released in 1997, and is currently in Version 4.0.
| Tags: | | | Permalink | | | 2 Comments |
