How SAS analytics uses machine learning to power data analysis

sas analytics

Organisations have always trusted SAS analytics platforms to handle large volumes of data and complex functions. Machine learning has been an integral part of SAS platforms and a huge reason why SAS platforms have performed so well (along with deep learning).

Machine learning distinguishes itself from other data analytics platforms by its ability to learn from the data it analyses. It is the latest buzzword in the world of BI because of its ability to make data analytics platforms smarter and more efficient than before.

That said, it’s important to note that machine learning is not a new technology for SAS analytics applications.

Indeed, machine learning and SAS have been synonymous with each other for a few years now, so it’s important that we address how SAS uses machine learning when integrated into its platforms and as a stand-alone system.

How does machine learning work?

First, a quick guide on how machine learning in SAS works.

Machine learning algorithms learn in three different ways: supervised learning, unsupervised learning, and semi-supervised learning.

Supervised learning occurs when machine learning algorithms train on labelled data and utilise logistic, regression, and gradient boosting algorithms.

Unsupervised learning is when machine learning algorithms train on unlabeled data and use several algorithms, like K-means, clustering, and PCA.

Then, there is the middle ground in semi-supervised learning, which utilises a combination of labelled data and unlabeled data with autoencoders and TSVM algorithms.

How do SAS analytics integrate with machine learning?

SAS machine learning algorithms add tremendous value to SAS analytics because of their ability to perform several algorithm techniques. Some of these algorithms include neural networks, regression, decision trees, random forests, and gradient boosting (And that is just scratching the surface! Machine learning can execute several other algorithms as well.).

SAS analytics integrates machine learning, utilising it for several reasons. For example, SAS Enterprise Miner uses machine learning to perform both linear and logistic regression analysis.

Meanwhile, SAS Viya uses machine learning to unify SAS platforms on multiple mediums and improve their accessibility, so that all officials can use the platform, no matter their technical skills.

Indeed, one of the reasons why SAS Viya is such a versatile platform is because it uses machine learning to deploy multiple SAS platforms. SAS Viya uses machine learning to resolve complex problems that would otherwise delay results. Moreover, SAS Viya uses other technologies like parallel processing to streamline data collection and processing.

Machine learning has been an integral part of SAS offerings for several years, both as part of SAS software and as a stand-alone offering designed to optimise data analysis even further.

Machine learning as a standalone feature

In fact, machine learning algorithms can simplify the data collection and analysis process, meaning less work for data analytics professionals. For example, machine learning can expedite the creation of predictive analytics models using features like automatic code generation and reusable code snippets.

By using machine learning, SAS analytics professionals can perform operations a lot faster. For example, autotuning capabilities can help analysts build optimal data models in shorter timeframes.

Machine learning can also optimise data discovery and data finding processes to help you spend more time on insights and less time exploring data.

SAS machine learning makes data collection and analysis more manageable. This is because machine learning is more than capable of collecting and analysing both structured and unstructured data. This allows data analytics platforms to be more efficient in their data collection and analysis processes.

Additionally, analysts spend less time cleaning data and more time uncovering patterns within the data itself. This is a better use of an analysts’ time and makes them more productive.

Optimising machine learning for SAS platforms

SAS platforms have gone a long way in optimising and improving the data collection and analysis process for most organisations. This is just the tip of the iceberg. Machine learning algorithms can learn when fed data, making it the perfect tool for performing several sophisticated functions like fraud detection.

The ability to optimise pivotal procedures and even expand into new operations makes machine learning a vital aspect of SAS analytics platforms.

Visit our website to know more about SAS analytics and its value in data analysis.

Cameron Lawson