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.
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.
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.
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.
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.
Machine learning (ML) refers to a software application that learns processes from past data much like how a human would learn. ML integrates into and augments business intelligence (BI), an expansive software that collects and analyses business data to deliver better insight into company operations. BI is vast, comprising several applications like predictive analytics, data mining and performance management systems. Hence, integrating ML improves the function and capability of BI.
Machine learning expands BI capabilities
Reduces damage and injury
Certain industries like the oil industry depend on several factors, like worker safety and weather for continuing operations. If a worker gets injured, or a machine fails, it disrupts productivity. BI utilises analytics and predictive modelling to monitor machine operations in real time. Machine learning learns from past data to discover the cause of worker injuries and critical failures in machines. Using the data, ML then “predicts” the chances of a worker getting injured or a machine reaching a critical level before it happens.
Thus, with BI and ML, oil companies not only collect and analyse data but also take pre-emptive action to prevent a disaster before it happens. Therefore, BI can increase productivity, reduce worker injury rates and improve the lifespan of equipment using ML. It’s not just the oil industry that will benefit – any industry where workers are at risk reduce the chances of injury through ML and BI.
Machine learning receives a lot of attention because it boosts productivity significantly. BI collects and analyses data from several processes, but ML can streamline and automate several processes. The automation process takes place through intelligent automation, where systems can survey thousands of operations in a single day and flag exceptions. Human agents examine the flagged cases.
As a result of this, companies make better use of their human capital. Instead of having human agents examine thousands of processes, they only look at the most critical cases, which are beyond automated systems. A process using both automation and human intuition is useful in specific instances like fraud detection.
ML can also streamline processes like customer service, risk management, business capabilities management and more. The combined appeal of automation and streamlining means ML can boost productivity by a significant amount.
Boost sales and marketing
Businesses are using BI to gain deeper insights into customer purchasing habits. With machine learning, companies will know audience reactions to new products or marketing campaigns. BI collects information on customers from different sources like browser searches, purchases and much more. ML leverages this information, analyses the trends and predicts customer reactions.
Companies use technology to discover how their audience will take to new products or campaigns before either launch. With this capability, businesses increase their chances of success while also sidestepping any problems that damage the brand.
Businesses are now working in a knowledge economy, which means research is important for success. BI and machine learning tools can improve research processes through a BI-search platform. Search platforms based on BI and ML are more responsive to consumers, providing suggestions that are change based on the questions asked. The search platform responds to the needs of the user and not the other way around. Thus, users can get more concise answers in less time with these new search platforms.
Forecasting has evolved over the years from excel sheets to predictive modelling but will evolve even further with BI and machine learning. Forecasting is a huge part of improving productivity from predicting sales to optimising supply chains. Machine learning improves the process by taking terabytes of data and using it to predict trends. In the future, forecasting will be so sophisticated that algorithms will answer specific questions rather than generate models.
Find real-time anomalies
BI systems enable businesses to find anomalies in real-time, but ML builds and improves on this system. Fine-tuning of this system is crucial because it allows firms to sharpen specific processes like fraud detection. Finding real-time anomalies opens up several opportunities for businesses not seen previously. One possibility is the option is to see someone browsing your website in real-time instead of just knowing about the people who bought from you. It will give you better insight into what you have been doing wrong and reveal the best way to increase the conversion rate.
Machine learning can improve the functionality of BI because the software collects and analyse terabytes of data to predict future trends. Anticipating what will happen before it happens is one of the best investments a business can make and it can only be obtained through machine learning.
Want to learn more about data analytics, machine learning and BI? Visit our blog for more information.
Since its announcement last April at the 2017 SAS Global Forum, SAS Viya has been heralded as the analytics platform’s stepping-stone into the future. As a cloud-enabled platform, with a powerful in-memory analytics engine that enables quick, accurate, and consistent results at all times, Viya is scalable and has the processing power required to address some of today’s most complex analytical challenges. By combining all its existing functionality with machine-learning and artificial intelligence technology, SAS software is not only becoming more efficient, but smart in the process. This scalability and dexterity are precisely what makes users of SAS software optimistic about the platform’s value in the future.
So, how exactly does SAS utilise machine-learning and AI capabilities? What noticeable benefits has this technology fostered? We explore all that and more in the following sections.
Which SAS Viya product has the strongest machine learning and artificial intelligence capabilities?
While SAS Viya comprises of at least 12 varying products – from SAS Data Preparation to SAS Visual Text Analytics – SAS Visual Data Mining and Machine Learning is by far one of the platform’s most intelligent and insightful. Given that the solution runs on SAS Viya, it is bolstered by the platform’s ability to manage any analytics challenge. Its relative user-friendliness means that anyone from data scientists to business analysts to developers, and executives can collaborate with another to realize insights and results faster.
SAS Visual Data Mining and Machine Learning comes equipped with an incredibly broad set of modern machine learning, deep learning, and text analytics algorithms that are all accessible within a single environment. This makes the solution ideal for all kinds of business users, given the solution’s diverse analytics capabilities that include clustering, different modes of regression, random forests, gradient boosting models, support vector machines, natural language processing, and topic detection – to name a few. SAS users not only gain access to a platform that is highly functional, but one that is equipped with powerful predictive and decision-making capabilities that were previously limited.
Now, let’s look at how this benefits end-users – we begin with how the platform helps solve complex analytical problems much faster.
Since the software runs on the latest edition of the SAS Platform – SAS Viya – it has the ability to deliver predictive modeling and machine-learning capabilities at unprecedented speeds via powerful in-memory processing. Given the processing prowess and persistence associated with this in-memory data, the need to load data multiple times during various iterations of analysis is no longer required. This means that multi-user collaboration has been simplified where users across all segments of the business and/or organisation can explore the exact same raw data and build their respective models simultaneously. Through the SAS Viya platform, analytical modeling can be done in a matter of minutes, which enables organisations to find answers to their questions and challenges much quicker and efficiently.
A few years ago, the idea of building complex analytics models to drive data-based decision-making within an organisation was heralded as an extremely difficult task that required significant technical expertise. However, through the evolution of the SAS Platform and the development of SAS Viya, users now have access to a platform that comprises of interactive visual and programming interfaces that significantly reduce the amount of time it takes to set up data, build complex and insightful machine learning models, and, finally, make decisions based on these insights.
Even users who lack coding expertise and knowledge can leverage the platform by generating advanced machine learning algorithms via the platform’s built-in visual drag-and-drop interface without ever having to know or drop a line of code into the system. This is something we like to call complete organisational empowerment.
For users who are more technically-versed, data sources and code snippets can be shared among themselves and across departments to better improve organisational collaboration. Additionally, business users of the platform would not have to exclusively know how to code in SAS – other languages, such as Python, R, Java, and Lua can be used to code as well. The SAS code is automatically generated behind the scenes!
With machine learning capabilities built in, business users have the benefit of leveraging the platform to evaluate and compare all available options/approaches, prior to making or recommending a decision. Scenario-based decision-making is facilitated via the system’s “automated model tuning”, which lets users identify the best-performing model.
The machine learning programs can integrate both structured and unstructured data, enabling users to derive more insights from new data types by adjusting their models accordingly.
SAS Viya has many benefits, with its machine learning and artificial intelligence capabilities being among its best features. If you would like to know more about Viya and its features, in addition to how you can install, administer, and host your organisation’s own SAS environment, feel free to reach out to us, or stay tuned to this feed.