The advent of container technology is revolutionising software development and data analytics because these software units allow you to run applications on different platforms, including desktops, physical servers and virtual servers.
Containers present a consistent interface that allows developers to easily migrate software to different environments making the work easier for your IT teams and analysts.
While container technology does benefit many development workflows, what does using SAS analytics for containers mean in data collection?
Today, you can implement and manage your SAS analytics tools within containers, making it easier to manage critical business data in a unified platform.
In this post, we explore how SAS for containers is transforming data management processes across the globe.
One of the primary advantages of using SAS Analytics for containers is the flexibility it offers to the users.
SAS Analytics has the tools to help you deploy the SAS Viya platform on Docker and Kubernetes.
You can even get support for Docker deployment on SAS 9 with the SAS Recipe, a set of instructions that lets you access the traditional SAS software depot, extract the software components to perform a Docker image build, and create dedicated containers.
This approach enables the customisation of the operating system to the way you desire for building the Docker image and running the container.
With an integrated platform, developers can access various tools they can deploy in different contexts. For data analysts, this means readily available predictions and insights delivered without bottlenecks.
In most traditional cases, data analysts rely on the expertise of the IT team to access the data hubs.
With container-based data science platforms like Domino Data analysts, analysts have access to the SAS platform without assistance or approval from the IT team.
Once the container image for SAS is deployed, anyone in your analytics team can access and use it to gain insights faster.
Unlike the conventional SAS platforms, SAS for containers has a wide range of container images that include a combination of SAS and open-source software—analysts can easily leverage a range of tools necessary for data collection, examination and analysis.
Another benefit of using SAS Analytics for containers is that you don’t need to worry about new updates. If you want to upgrade to a new container, you can create a new image with the latest version and use it to build an updated container.
This feature gives you the chance to upgrade your software without disrupting other users, eliminating the need to wait for weekends or pausing your operations midweek to deploy new updates.
Additionally, you can also test the new container and deploy it when it is stable and optimised. It is also possible to keep multiple container images for different versions; users can test their code against different versions for stability and efficiency before deployment.
Moreover, you can implement just the required SAS software, analytical models, and supporting code in the form of a small execution engine bundled into a lightweight container.
Data collection is the heart of business strategizing.
That’s why optimising the data collection process using SAS analytics for containers guarantees an efficient data analytics pipeline, helping you establish simplified and streamlined business operations within your corporation.
With our range of managed processes, we can help you access and establish a seamless and sophisticated SAS experience.
If you would like to know more about how you can transform your corporation with better data collection, don’t hesitate to give us a call to learn more about utilising SAS analytics for containers.
The fast pace of the market and the competition make business management unpredictable unless you have the right tools like data-driven business intelligence (BI).
By providing insights into market patterns, buyer behaviour, and other economic factors, business intelligence can help you make better decisions to improve business performance. BI tools allow you to explore large datasets and leverage them as a resource to gain useful insights.
By leveraging BI tools, you can enjoy improved efficiency, fraud identification, better product management, improved brand image and more.
While there are many BI tools in the market, SAS has always been a leader in data analytics. With AI-driven platforms that provide you with an extensive range of tools to enhance your data analytics capabilities, SAS can help you streamline your business processes.
Here are the reasons why you should choose SAS analytics business intelligence and data management for your brand.
While traditional data analytics tools can deliver quality insights, most often than not, they fail to deliver these insights to all parties in the decision-making process.
That said, with SAS, you can overcome this challenge and improve information access across your business functions.
One of the key features of the SAS business intelligence suite is the ability to easily integrate with MS Office tools like Excel and Outlook.
Through this integration, you can distribute information and exchange important insights with others involved in the decision-making process. Storyboard and narrative creation features available in the platform assist with presenting data to decision makers in an understandable manner.
In addition, all these tools access data through metadata representations, making it easier for everyone involved in decision making to receive quality insights and orderly create action plans.
Navigating a data management system and analytics tools is not always straightforward unless you are well-equipped with the knowledge of information technology. Most of the time, you will have to rely on IT pros when managing your data, making the whole process time-consuming.
With the SAS business intelligence platform, you have access to integrated tools that perform multiple functions like analytics and reporting, making it easier to navigate. This also allows you to access and manage data, make decisions and draw inferences without relying on IT professionals.
With visual data analytics, you will also have valuable data represented in graphs, charts, and other visuals, making information and insight gathering convenient and comprehensive.
Additionally, the Business Intelligence app gives you 24/7 access to business functions with devices such as smartphones—you can monitor your business from anywhere, anytime.
SAS analytics business intelligence and data management ensures accuracy and high precision in functions like predictive and descriptive modelling, forecasting, simulation, and experimental design.
As a result, you can leverage SAS to build an effective analytics strategy and formulate data-driven decisions to improve your marketing, accelerate your operations, or enhance the customer experience.
The focus on consistency and standardisation of data also allows you to avoid erroneous or false data that could lead to wrong decisions that can endanger your business.
Today, the business environment is more challenging than ever before. You need the right tools to survive and succeed in this landscape, and SAS Analytics helps you do that.
Here at Selerity, we are committed to providing you with a seamless SAS experience through our range of managed services.
Don’t hesitate to contact our team to learn more about SAS analytics business intelligence and data management.
What do you think the future on earth looks like?
We all think of a future where humans and machines interact seamlessly. Our idea of the perfect future is influenced by how it looks in movies and TV shows.
Most of these fictional future worlds have one thing in common: super-smart devices.
Whether it’s the ultra-futuristic world of Blade Runner 2019 or the toned-down future of Black Mirror episodes, all of them feature smart devices that interact with the characters.
These fictional worlds, however, are not all that rooted in fiction. Some of this technology already exists in the present world, albeit with limited functionality and capabilities. These devices are called IoT devices—interconnected devices equipped with sensors and cameras that allow them to communicate with neighbouring devices over the internet.
Businesses, though, are not resting on their haunches. They are trying to create smart devices with greater functionality and capabilities, just like in the movies. Data analytics tools like SAS analytics for IoT are helping engineers create these uber-smart devices.
Early IoT devices had simple functions and limited capabilities due to the lack of data needed to improve their intelligence. As a result, they were not smart—only a little less dumb.
Early smart doorbells, for example, only relayed a video feed of the front door to occupants inside the house. While this is undoubtedly useful, it was not very smart.
Modern smart doorbells not only relay a video feed to occupants but also identify the person at the front door. In addition, they notify occupants about the arrival of guests, all through the power of analytics.
This identification function, in particular, uses cameras, AI and data analytics to identify different people. Cameras capture the image and data analytics helps identify the individual by comparing the image to information in a database.
Analytics tools like SAS analytics for IoT have been critical in improving home assistants powered by the likes of Google Nest Home and Amazon Echo.
These digital home assistants use AI-powered data analytics to study our behaviour through various sensors located. Studying our behavioural patterns helps these devices automate actions like setting the temperature through a thermostat or switching lights on and off.
IoT devices are not only making a splash in our homes but also in our factories and commercial spaces. Many organisations are now using IoT devices in their manufacturing lines, which previously required manual labour.
Early manufacturing equipment relied on human knowledge about each machine for maintenance. Unless there were trained machine operators on-site, these expensive machines were rendered useless when they broke down.
Modern manufacturing lines include IoT devices so that maintenance and troubleshooting become easier. Modern manufacturing equipment includes sensors and cameras that not only monitor the production process but also monitor the optimal operation of these machines.
These sensors stream large amounts of data to servers to deliver visual analytics insights about the manufacturing process and components inside the machines. With these insights from IoT devices, employees are able to detect faults in the machines and fix them, reducing downtime across the manufacturing process.
Modern devices not only share data but also use AI to pinpoint faults, eliminating the hassle of troubleshooting for employees.
In the future, data analytics tools for IoT devices may help us build intelligent manufacturing lines that fix themselves, making production seamless.
All of us envision a future powered by smart technology that automates our mundane tasks.
Thanks to data analytics for IoT, this reality is not too far away. The IoT analytics challenges that prevented widespread adoption is fast becoming a thing of the past as more and more smart devices are used across industries.
Soon, we might not need special effects to create the fantastical scenarios we see in movies.
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.
We have talked about the importance of SAS services in higher education. However, we have yet to address how SAS analytics addresses the administrative challenges that come with running a higher education institution.
As universities and colleges look to attract and retain top students, being more efficient in administrative operations can make a significant difference in the student lifecycle, making it easier to recruit and retain a suitable student body. SAS analytics can help higher education institutions optimise their operations to provide a better experience to students and improve recruitment, marketing, and administrative practices.
To optimise administration across the student’s lifecycle, higher education institutions require analytics platforms that can integrate data from different sources, utilise advanced analytics to improve decision-making, and self-service analytics to give non-technical personnel access to data-driven insights.
Higher education institutions have vast amounts of data spread across different sources. To compensate, IT teams often stretch resources to provide support to different departments. The trade-off to this method of working is that it takes time to render information, making it difficult to pull off complex research in quick time because information can become redundant quickly.
Furthermore, most administrators don’t get the holistic perspective they need to make informed decisions, forcing them into a tunnel view when making decisions. SAS analytics can help higher education institutions work around this problem by integrating different data sources to provide a holistic perspective on the student’s lifecycle.
This added perspective makes it easy for decision-makers to identify stages where students tend to drop-out, making it easier to build strategies around maximising student retention.
Compiling reports can be a pain. Most administrative personnel don’t have the technical know-how to use complex IT systems, meaning that if they need to compile a report or make adjustments, they have to turn to the IT department to complete the process. This means tedious back and forth between two departments that reduce efficiency.
Self-service SAS Analytics puts powerful but accessible analytics platforms in the hands of people who need them, generating plenty of benefits. Self-service analytics eliminates the back and forth taking place between departments. Administrative personnel can explore data quickly and effectively, especially when it comes to student-related matters to discover underlying problems or opportunities to improve the student lifecycle.
Advanced analytics like statistical analysis, machine learning, and data optimisation open up administrative personnel to analytical opportunities that were not possible before. Using advanced analytics platforms, administrative personnel can seek answers to large-scale trends taking place throughout the university.
Advanced analytics is key because it allows administrators to draw up significant questions about future trends. With advanced analytics, university administrators can anticipate trends about the student’s lifecycle and take steps to address any fallout. For example, by using advanced analytics, institutions can predict trends about graduation, drop-out, and other key questions regarding student life.
Once they find answers to these questions, administrative personnel can draw up plans to address the necessary pain points in the student lifecycle, while also simplifying administration operations.
Clever use of SAS analytics allows higher education personnel to be more proactive and efficient in operations related to student recruitment, retention, and graduation. Some of these operations include devising sophisticated recruitment strategies, support for at-risk students, and streamlining administrative operations to ensure the department operates more efficiently than before. By using SAS analytics, universities and colleges can be more proactive when supporting students, making it easier to support at-risk students and making sure they graduate.
The key to optimising student lifecycle management lies in the higher education institution’s ability to support the student body in all aspects of their lives, like course enrollment and scheduling, which can only be done by streamlining administrative operations.
SAS analytics comes with advanced functions and sophisticated data management procedures to make the collection and analysis of data much easier to execute. With better data management strategies, it becomes much easier to discover flaws in student lifecycle management and take measures to fix them.
Furthermore, by using SAS analytics, it becomes much easier to plan out future objectives when it comes to student retention and recruitment, ensuring a student body that is content and well-supported.
To learn more about how SAS analytics can optimise student lifecycle management, check out out our higher education page. Selerity and SAS are offering a free whitepaper explaining how data visualisation, data integration, and advanced analytics can transform higher education.
It might seem counterintuitive to talk about optimising cloud storage. After all, the cloud was built to host large amounts of data, why spend time and effort optimising storage on the cloud? But as the capacity of big data expands, servers will be pushed to their limits, and this will compromise the efficiency of data collection and analysis. When cloud storage is not optimised, it hinders efficiency in data analytics.
Several clients have come to us, seeking advice on how to optimise their environment for SAS analytics. One particular firm in fintech struggled with its data collection and analysis because cloud storage was not properly optimised. According to their CIO, working on their data analytics pipeline was like “Trying to swim up a sludge-filled river,” because completing basic functions was much harder than it should have been.
Given the connection between data analysis and cloud storage, organisations need to find ways to optimise their data storage to get the best results. Optimised cloud storage allows for responsive and efficient transfer of data, making data analysis more efficient. This maximises the value of SAS analytics and reduces operating costs.
However, despite the obvious benefits, optimising cloud storage will not be easy, especially with SAS analytics. This is because SAS analytics works with several cloud databases, like AWS and Azure. To optimise storage, you need to be familiar with these different platforms. However, there are still some things that can be done to optimise storage, in general.
One of the most common methods for optimising cloud storage is minimising data duplication and replication. Data duplication has several steps in the process, like chunking and securing hash algorithms. But the advantages are significant because it essentially eliminates all duplicates from the dataset, making it easier to work with and providing high-quality data for SAS analytics platforms to process.
Autoscaling is one of the best practices for optimising cloud storage. When auto-scaling solutions are implemented, the cloud platform scales automatically to match the volume of the workload.
Autoscaling makes cloud storage more efficient because cloud resources can expand and contract dynamically to match demand, reducing the workload for SAS experts and technical users.
You can configure auto-scaling solutions on AWS and Azure, although it should be noted that theprocess for implementing auto-scaling will be different for both platforms.
At its core, optimising the cloud is about maintaining a balancing act between workload performance, costs and compliance. The goal is to balance workload against infrastructure in real-time to attain efficiency. The challenge for optimising the cloud for SAS analytics is that no single strategy is the same. However, there are some things you can do to optimise cloud storage.
A significant chunk of cloud optimisation is analysing patterns in the workload, including past use and operational costs, in a process called workload modelling. Current use is then compared against the recommended configurations that would deliver the ideal workload for the platform.
When cloud platforms grow, they become more complex, compromising transparency. When transparency is compromised, it becomes much harder to maintain an efficient cloud platform. So, it’s important to improve oversight and transparency across the board to make the sharing of data much easier. Furthermore, it improves efficiency in cloud storage methods.
It’s important to understand that optimising cloud storage is an ongoing process, so its best to invest in tools to make the process easier. Workflow automation can help significantly with the process because it helps SAS professionals identify any unused or partially idle resources and can either use these resources or shut them down.
As many of you know, optimising cloud storage comes with several benefits that could help your clients reduce costs and improve operational efficiency. However, this is easier said than done because SAS Analytics uses different cloud databases, like Azure and AWS, to get the job done. However, by using the right tools, optimising cloud storage becomes a more efficient process, which is crucial for organisations working with large volumes of data.
The digital era gave people the means to express themselves. Whether it be Tweets, Facebooks posts or customer reviews, there is a lot of user-generated content that businesses can look into for more information. Furthermore, this user-generated content can be very valuable if used properly. In fact, studies show that customer reviews are twelve times more trustworthy than company produced marketing material. However, analysing the sentiment behind the content remains a huge challenge. Organisations often struggle to not only grasp the volume but the real sentiment behind the content as well, which is why SAS sentiment analysis tools are a huge business asset. In this blog post, we take a look at what sentiment analysis can do for organisations.
SAS sentiment analysis allows businesses to get a better understanding of the feelings behind user-generated content. It uses statistical and linguistic conditions to identify negative, positive, neutral and even unclassified opinions from the content. The analytics platform can be used in many areas, particularly in market research.
Sentiment analysis tools can be essential for brand or reputation monitoring. No matter the industry they are in, every organisation can use sophisticated tools to monitor people’s feelings about the brand. SAS sentiment analysis tools can be useful in this regard because they can analyze different samples of user-generated content like customer reviews. This is useful in different functions like assessing customer response to new products, assessing brand perception and even monitoring content from influencers. Sentiment analysis tools are great for monitoring brand reception.
SAS sentiment analysis tools are an integral part of strategy formation. For marketers, the ability to see how customers perceive their brand makes sentiment analysis an invaluable part of forming marketing strategies. The tools can be used to assess mentions on different media platforms, identify the most relevant platforms to the company’s brand and even automate media monitoring processes. Hence, sentiment analysis tools can be used for evaluating marketing efforts to see the results and inform strategies.
SAS sentiment analysis can do more than just analyse customer opinions, they can analyse how well the product is performing on the market. While there are some similarities to brand monitoring, there is one key difference: Brand monitoring is keeping an eye on how customers perceive the whole brand, while product analytics focuses on response to a specific product. Thanks to analysis tools, organisations have a better understanding of how people will respond to new and old products. They can use SAS sentiment tools to analyse every review and a bit of feedback to make adjustments accordingly. Sentiment analysis gives marketers a better understanding of how customers respond to a new product.
With the ability to analyse how people are interacting with a brand, organisations are in a much better position to improve the customer’s experience. SAS sentiment analysis allows organisations to collect feedback from each customer interacting with the brand for analysis using powerful algorithms. Once the overall trend is found, the organisations will have a better understanding of what is affecting customers and how many feel this way. A better understanding of customers leads to several benefits for the end consumer, like better response times to complaints and meaningful feedback. For example, Google is using sentiment analytics to monitor feedback to Chrome and make updates accordingly. Thanks to sentiment analysis, organisations have a better understanding of how their customer’s feel on any subject.
SAS sentiment analysis tools are crucial for the future because they provide a competitive advantage for organisations. No matter the industry, organisations face stiff competition, but a better understanding of the target consumer market helps them finetune and refine their marketing strategies so they can successfully compete in their respective industries. Sentiment analysis tools will also be crucial as knowing what people say about your brand across different mediums is vital. Of course, to use SAS sentiment analysis, organisations need to invest in the right platform that would bring data analysis capabilities to those who don’t have a technical background. A platform like SAS Viya can be useful in making the analytical capabilities of sentiment analysis more accessible.
Data lakes are the key to streamlining the SAS analytics pipeline. The volume of data industries collect has grown exponentially, but along with that growth comes several challenges, in regards to processing in the analytics pipeline. It hinders performance and slows down production cycles, in turn, hindering the rate of innovation. While SAS platforms are more than capable of processing large volumes of data, management of data can always be optimised to improve the analytics process. Processing large volumes of data presents a huge challenge for organisations, especially in an age where data is more valuable than oil. How do analytics experts streamline the analytics pipeline to speed up the rate of innovation? By using data lakes.
The best way to explain how a traditional data analytics pipeline works is by using an analogy of a stream. Raw data comes into the pipeline and is stored in a data warehouse to be cleaned and filtered. Once the data is ready, it will be streamed into the SAS analytics platform when needed through AI and visual pipelines. Furthermore, when it comes to developing new analytics models, data engineers have to build new sandboxes different from the production environment. To build and test analytics model, the sandboxes are built with synthesised data.
There are some disadvantages to the traditional method. The process of cleaning and filtering raw data as and when is needed takes up a lot of time, slowing down the rate of production from the SAS model. Furthermore, the process of developing and testing new SAS analytical models takes up a considerable amount of time, time that could have been spent in more productive areas. Moreover, the current method requires SAS analytics engineers to move data around quite frequently.
For example, when data needs to be processed, it needs to be shifted from the source to the tools, slowing down the analytics process. Even worse, embedding data into the analytics pipeline makes it tough to update the tools. Finally, there is the issue of data governance – data security, resiliency, audit, metadata and lineage are much tougher to carry out because data is stored across different sources, forcing the SAS analytics specialists to divert their efforts, amplifying work across the board.
Data lakes capture a broad range of data types on a large scale, making it perfectly suited for taking in raw data and quick processing.
Data lakes bring several benefits that simplify the processing of data.
Data lakes remove the movement of data from source to SAS analytics platform. Removing the need to transfer data streamlines the analytics pipeline. All data is stored in a common source and can be processed by different tools. A common source for all tools means there no longer needs to be different sources for different tools. All SAS analytics tools can draw their data from a single source, making data movement more efficient than before.
Anyone who works in the world of tech and data knows that analytics platforms are never stagnant. Technology is evolving and analytics platforms should either be updated or changed completely, SAS analytics platforms are no exception. Data lakes make the change easier to accomplish because the data is not stored on the analytics platform. It can streamline the entire pipeline because it is much easier to shift over to the new platform.
Data lakes not only simplify the development of SAS analytics models but can also lead to more accurate models. Under the traditional method, analytics models were only developed using synthetic data. However, synthetic data is not always accurate, which often compromises the quality of the model. Data lakes remove this hurdle by providing secure, read-only access of production data that does not compromise SLAs.
The tasks that fall under data governance become more streamlined and easier to accomplish with data lakes. The entire process becomes much easier to accomplish because data is brought from different sources into a unified source. With data being drawn from a single location, it becomes easier to protect data.
Streamlining the entire process
As SAS data analysts, we must always look for ways to make our jobs more efficient and data lakes are one of the best ways to streamline our work in SAS analytics. By streamlining our analytics pipeline, it allows us to become more productive and spend more time on innovating rather than routine work. Streamlining the analytics pipeline with data lakes also provides tremendous value to our clients because it reduces operational costs while improving productivity.