System Administrators have been dealing with this scenario for decades now, and established methods of making a personalised, secure home directory available to users now usually rely on NFS or CIFS/SMB.
Viya provides the ability to make home directories served by NFS available to applications that use the Programming Run-Time Servers (such as SAS Studio). You do this by specifying the NFS server details during deployment. If your NFS server and Identity Provider are already used to serve home directories to other applications then the documented defaults will work great – but what if that isn’t the case?
Behind the Scenes
Under the covers, Viya is running in a Linux environment (within containers, within Kubernetes). Each user in Linux is assigned a unique User ID (uid). This uid is what allows a user to access their own personal home directory, among other things. Viya on its own has no idea what uid is assigned to what user, unless that information is provided by the Identity Provider. If you are leveraging an existing NFS Server backed by an Identity Provider that is already in use with that NFS Server, then there is a good chance (but not guaranteed) that your Identity Provider already has the required posix attributes to provide the uid to Viya.
If you are using Active Directory as your Identity Provider then there is a good chance that you don’t have these attributes. In this case Viya will generate a uid (and Group ID, or gid) for each user and store it internally. This allows Viya to kick off compute sessions using the uid it has generated, but for obvious reasons nothing outside Viya knows about this uid – which means when it comes to accessing a user’s home directory on NFS, the uid will most likely not match the uid on the home directory.
Home Directories Solved?
But there is a problem even before we get to the matching uid problem – how does the system know it even needs to create a home directory on NFS for the user? In a traditional Linux environment this is taken care of by PAM, leveraging methods such as pam_mkhomedir. This in turn relies on the Identity Provider of the operating system. In fact this is what the earliest solution to this problem used. In Viya 3.4 a more integrated solution was provided directly in the deployment process, followed by further updates in Viya 3.5. Unfortunately starting with Viya 2020.x these methods no longer work.
This script works great, but the “how to” of getting it running and integrated into your Viya Kubernetes environment is left to the user.
Home Directory Solution from Selerity
To make implementing this a bit easier, Selerity have created a Helm Chart that will deploy a Cron Job into Kubernetes to maintain home directories in NFS using the uid internally generated by Viya.
To install this solution you should be familiar with Kubernetes and Helm, as well as have the details of the NFS Server used during your Viya Deployment. Here is all that is needed to get this deployed:
This will create a Kubernetes Cron Job that must be triggered manually, and when you do trigger it will only report on what it will do (it won’t create or update anything). This will let you view the logs to see what it would do if it was enabled. The parameters above are:
VIYA_NAMESPACE – the namespace you have deployed Viya to
RELEASE_NAME – any string you want to use as the name of this deployment
VIYA_BASE_URL – the URL to your Viya deployment
NFS_SERVER_NAME – the hostname/IP of the NFS Server you specified in your Viya deployment
After a successful install you will be presented with instructions on how to view/trigger/etc. the Cron Job. If you are happy that the process will work correctly in your environment (after reviewing the logs of a sample run) you can enable it to create/update home directories by adding the --set dry_run=0 option on the Helm command, and if you want to enable it to run on a schedule also add the --set suspend=false option. Further details are available in the Helm Chart.
Helm Chart on ArtifactHUB
viya4-home-dir-builder: Create home directories for SAS Viya 4 Users
SAS Analytics Pro Cloud Native brings the ability to run SAS within a containerized environment which brings exciting possibilities for CI/CD and integrating SAS into other applications.
SASPy has become a popular choice for Data Scientists and integration developers to bring the power of SAS procedures and data step to Python software development chains. This post seeks to outline the steps required to configure SAS Analytics Pro cloud native to accept SSH connections which are required by SASPy and augment the current documentation for using SASpy with SAS Analytics Pro.
It is also important to note that the following steps can also be used to natively call SAS in STDIO mode from your host machine to the container to perform tasks.
Some Notes on Terminology
For people without a lot of experience in using Docker, SSH, Python or networking, the terminology in web articles can be a bit confusing and overwhelming. The below table outlines the meanings of terms used in this article. For further information as well on how SAS Analytics Pro works in Docker, please see our previous article which outlines Docker concepts and another article on the differences of SAS Analytics Pro and SAS9.
Refers to SAS Analytics Pro Cloud Native which is a product offering from SAS for running a SAS Programming environment within Docker.
Docker is a technology company that provides a runtime and development tools for interacting with Containers and Images.
An image is a set of compressed software libraries and binaries that can be executed as a container inside the Docker runtime environment. Images operate against a common OS kernel. SAS provide an Apro image which can be run as a container.
Containers are an instance of a Docker image. Containers contain additional configuration information such as network settings, volume and port mappings.
Your local machine where you are starting the Apro container from. This may be your laptop, PC, or a server.
This is a communication method for connecting from one machine to another in a network. In this instance we are performing SSH connections between your host and the Apro container.
These are a set of cryptographically generated keys used to identify and authenticate you when using passwordless connections over SSH.
SASPY is a Python package developed by SAS and open-source contributors. It provides an interface to the SAS language including the submission of SAS code, procedures and data interaction. It provides a number of connection methods depending on the type of SAS platform you want to connect to. This includes:
IOM based connections for SAS9 / Metadata server platforms.
HTTP/S for Viya
STDIO over SSH for Linux based servers
STDIO for local connections on Linux where SAS is installed on the machine you are working from.
We will be using STDIO over SSH in this scenario. The STDIO over SSH method enforces passwordless SSH connections so we will need to set this up.
Configuring Passwordless SSH
The first thing you need to set up passwordless SSH is a public and private key pair. To generate, you need some software on your host. On Linux based operating systems OpenSSH is already installed. On Windows you may need to install it or if you use Git, the Git bash client has it installed already.
To check if you have an existing key pair, first check your %USERPROFILE%\.ssh directory on Windows or ~/.ssh directory on OSX/Linux.
ls -al .ssh/
Or in Powershell.
If you see a group of files in the listing starting with id_xxxx and one with an extension of .pub and the other without, you already have a public / private key pair. For example if you have an rsa encrypted key pair you would see two files:
If you have existing keys, ideally they are configured without passphrases. Passphrases are great for interactive usage as they add an additional layer of security but they hinder things when using keys in automation scripts. For SASPY, keys without passphrases work best.
Creating a Key Pair
If you don’t want to use an existing key pair or do not have a set you can generate them using the following commands.
ssh-keygen -t rsa -b 4096 -C "sasdemo"
Let’s break this down:
The command ssh-keygen creates the public and private key pair.
The -t rsa is telling ssh-keygen what type of key to generate. In this case it is the RSA encryption algorithm.
The -b 4096 is telling ssh-keygen the bitness to use in the algorithm
The -C “sasdemo” is a comment to help identify what the key is for. It is appended to the key.
After hitting enter you will be prompted for a few values. You just need to press enter for each one without adding anything different. The exception is If you already have a key named id_rsa and you don’t want to overwrite it. Specify a new name in the same file path it chooses (will default to $home/.ssh/<name>. The below illustration shows this. I have named my keypair as id_rsa_apro
Take note of the name of the key you generated as we will need this later. Next we need to configure the Apro container to allow SSH connections.
Configuring SSH in APRO
Containers by default are generally built to be as lightweight as possible and as such, generally do not have all the same libraries and packages as a full operating system. In fact, it is one of the 12 principals of docker image development.
The Apro container will allow SSH without much configuration. The SAS instructions for this are fairly clear and are transcribed below.
In your /sasinside directory, create a folder called sasosconfig and in that new folder place an empty file called sshd.conf
In your container startup definition you need to add some system capabilities with –CAP_ADD arguments. These capabilities are a linux concept. To read more about their specifics see this guide. The capabilities we are adding are:
We also need to expose a port for ssh communication. We will use the same port used by SAS in their example. Add the following to your invocation command. This is telling docker to expose port 22 in the container and forward that through to port 8222 on your host.
Once you have done the following restart your container for the changes to take effect. If you have followed the steps correctly, you should see your container running in your docker client.
Now we have to create a new directory and set some permissions in the apro container to let us copy your generated key from earlier. From a command line:
Create the .ssh folder under the path you specified for the /data directory.
Run docker exec -u sasdemo sas-analytics-pro chmod -R 755 /data/.ssh to set the permission level for the folder. SSH expects your ssh folder to have restrictive permissions. 755 is the most permissive allowed.
Next we want to copy your public key you created earlier into the newly created .ssh folder.
To do this, we can use the docker cp command. SSH looks for a file called authorized_keys which contains a list of public keys that the server will accept connections from:
Now if all has gone successfully, we can now test our connection!
Testing the SSH connection
If you are on Windows, the docker container IP address may not be usable from outside of the container. On windows we simply need to use localhost or 127.0.0.1 for our server address.
Secondly, SSH by default enforces Strict Host Key Checking. You will receive an error in your attempted connection when the IP address of your Docker container changes which is whenever you restart it. To get around this, you can do one of the following techniques:
Under your .ssh folder you created earlier, add an additional file called config and add the following:
Host * StrictHostKeyChecking no
This is quite permissive. It is telling SSH to ignore host key checking from every host that attempts to connect. To be more stringent and just limit to your SASPy connection you can place connection arguments in your SASPy sascfg_personal.py file which we cover later in this article.
Test via Command Line
To test we have SSH configured let’s see if we can get an interactive terminal to SAS working.
While this is a long command let’s break it down to see what is happening:
We are using the ssh program and forcing a pseudo terminal with the -t command. This is useful when using interactive command line programs.
The -v flag is giving us verbose logging. It’s always a good idea to use -v when testing so you can see additional information about what’s going on. Less v’s give less verbose logging detail.
The -p 8222 argument is telling ssh to connect on port 8222. This is the port you specified in your Apro setup for ssh. Replace the number with the one you used.
The -i argument is telling ssh to use following identity file. This is only needed when your key pair does not use standard names. This file is the private key part of the key pair. You don’t need this if you accepted the defaults in the earlier step of generating the key pair.
Next we have the server address we are communicating to. This is in the form of <username>@<server address>. If you have started Apro with a different username, then replace sasdemo with the login name you use for SAS Studio. For the second part we specify either localhost or 127.0.0.1. The SAS documentation for this part is incorrect. If you are on windows, the docker container IP address will not be usable from outside of the container. You will need to use either localhost or 127.0.0.1.
The next part of the command is invocating SAS in stdio mode. This is the command that SASPY generates when starting a connection. If we successfully connect to SAS then we are almost assured that SASPY will also connect.
If all is successful, you will get an interactive window to SAS.
To exit, hit enter and type endsas;;;; Alternatively, stop the tutorial here and join the ranks of SAS demi gods by using SAS in the original form!
Once we have confirmed that SSH is working correctly to our container next we need to update our saspy configuration. This article won’t go into the details of installing saspy as there are plenty of articles and detailed help in the saspy user documentation at saspy.readthedocs.io
Open your sascfg_personal.py file and add the following:
Add a new entry in the SAS_config_names variable list to label the new connection. Here I will use ‘apro’
Next create a new dictionary variable with the same name you added to SAS_config_names.
Replace the luser value with your SAS Studio login userid, the port you opened in your docker config and any additional SAS options in the options key (not shown).
This configuration is slightly different to a standard SSH STDIO connection with the addition of the localhost and dasho parameters. These are optional and depend largely on whether or not you are running SAS Analytics Pro via Docker Desktop on Windows or via the Docker runtime.
The upload and download methods in SASPy use the socket filename engine in SAS to transfer files between client and server. Docker adds an entry to the windows hosts file which directs your IPv4 address to host.docker.internal. This is then used by Docker to communicate to the external host when required. For a longer discussion on this please see the following Github issue between myself and SAS on the topic. As mentioned previously, you may also receive errors from SSH when your container IP address changes as a result of restarting your container. The dasho parameter is passed to the ssh command to disable this host key checking for SASPy.
Once complete you can test your SASPY connection.
try: import saspy except ImportError: raise ImportError('saspy was not found in the specified path') sas = saspy.SASsession(cfgname='apro') res = sas.submit(code='%put NOTE: Success;',printto=True) print(res.get('LOG'))
If you have your sascfg_personal.py file somewhere other than the default paths, add cfgfile=’/path/to/your/file.py’ to the SASsession method and cfgname='name' if not using default.
Check that you see NOTE: Success in the returned log and if so, you now have SASPY connected to SAS Analytics Pro!
Hopefully this tutorial has been a help to you and assists you in setting up SASPy with Analytics Pro.
We’ve also created the Selerity Desktop (Personal) tool to help make deployments easier if you are uncomfortable with the above concepts. The Selerity Desktop configures SASPy connectivity for you. The personal edition allows you to deploy container environments with a series of additional options such as Python, Clinical Standards Toolkit and SAS OQ testing without needing to know any of the technical details. If you are interested in further information please see our product page for further information or reach out to us to discuss more complex deployments or licensing requirements.
Also stay tuned for future posts where we delve deeper into use cases that SAS Analytics Pro Cloud Native can support and other, more advanced, deployment options.
Customers who are opting for the new SAS® Analytics Pro offering may experience some confusion when they receive the license for the product. When viewing the order information, customer’s will see that the product is licensed to run on the Linux operating system however they may be using Microsoft Windows on their desktop. Further to this, when running the Analytics Pro container, some customers may wonder why their folders and paths to files are different. This article aims to help explain why this is and help you understand these differences so you can fully utilise the numerous advantages of running SAS inside containers.
The SAS Analytics Pro Cloud Native product runs as one or more Docker Containers either via the Docker runtime or packaged as a Kubernetes deployment or service in a k8s cluster. This is a smart approach:
Containers are highly portable and remove many of the quirks of building software for multiple operating systems.
They are fast and scalable and more lightweight than servers.
They are disposable. You can say good bye to the traditional dev / production environment approach.
The underlying root cause of the differences you see is due to how Docker works. While your desktop or server host may be running Windows, the container image is running Linux. Programs and files inside the Analytics Pro container act and look like they would on Linux.
What is a Container?
Containers were launched in 2013 as part of the open-source Docker Engine. Containers were built on existing concepts used in operating systems like Solaris and other unix based operating systems. Over the years, containers have become a reference standard. They are not tied to the Docker Inc. company and numerous vendors implement container technology.
Containers abstract application and deployment dependencies and run against a common OS kernel but run within an isolated user space.
This typically consumes less resources than using Virtual Machines. A Virtual Machine approach packages an entire operating system by abstracting physical hardware processes. A virtual machine also uses a Hypervisor which manages the capability to run virtual machines within a single physical host. Virtual machines consume more space and resources as they normally require a full copy of the target operating system and binaries. This also results in slower boot times to containers.
Docker formed a partnership with Microsoft to bring containers to the Windows operating system to allow Docker to run natively within Windows. On modern Windows hosts, the common kernel is the Windows Subsystem for Linux. When Docker Desktop was released in 2013, WSL did not exist. As a result, older versions of Docker Desktop would run within a Virtual Machine on Windows. This Virtual Machine would house a common Linux Kernel and required docker binaries.
Already it is easy to see the advantages of a technology like Docker for application development.
Standardise the delivery of applications to run on either Windows or Linux with a single code base.
Low-cost testing of applications on a variety of operating systems and their variants easily.
Throw everything away when you don’t need it.
Containers, Images, Networks, Volumes, Ports
Containers are an instance of an Image which is a compressed file of binaries and libraries containing all the required components to execute within the Docker runtime. Images are created using a Dockerfile which is a file format developed by Docker.
As mentioned previously, containers are isolated. That means they typically need to be given references to files and folders to use from your operating system and also be told what ports they can communicate on.
A container will typically contain one or more volume mappings, port mappings and instructions for networking.
Volume mappings are given to a container to expose files and folders on your host to the container so they can be used. This might include things like your source code, configuration files, data etc.
As well as this, an application hosted in a container may need to be told what port to let your host communicate on. Let’s take a web application as an example that runs on port 8080 within docker. On your host you want to access it on the standard web address port of 80. You will tell your Docker container to expose port 8080 in the container to port 80 on your host.
Docker containers can also run within completely self-contained networks and have multiple containers running. A simple version of this is using Docker Compose which comes with most Docker installations. This allows you to define container configurations using a yaml based file format and each container is managed from a created network object and can talk to each other easily.
SAS in the New World
Using containers means that you can also create multiple container instances easily with specific configuration tailored to the purpose you are trying to achieve. In many cases, this means you no longer need to maintain separate non production / production environments. Your user’s can have development environments locally, push their code to version control including the container configuration required and then have your cloud or hosted infrastructure run as isolated production containers. SAS can now sit easily alongside modern CI/CD approaches.
How the Analytics Pro Container uses Linux on Windows
The SAS Analytics Pro for Cloud Native container contains all the required binaries and libraries to execute Base SAS, SAS/STAT, SAS/GRAPH and over 20 SAS/ACCESS engines as well as the Basic deployment of SAS Studio. For further information on the inclusions of SAS Analytics Pro Cloud Native, please see our previous post. If your current SAS usage is focused on the SAS programming interface as opposed to the newer visual interfaces, then it’s a smart modernization choice to make.
As a user, you need to supply the image the following information at a minimum:
A SAS License (as a .jwt file) and deployment certificates (.zip file).
Configuration for mapping files and folders
Environment variables to configure port mappings
Driver and configuration files for whichever access engines you want to use.
The SAS Analytics Pro Container
The current Analytics Pro container comes in at just under 10GB in size. This is opposed to the older SAS9 Depot which would be anywhere up to 30GB in size.
The image is downloadable from SAS using the mirror manager command line tool. You can then also use another command line tool, the SAS Order manager (or manually at my.sas.com) to download licenses and certificate files required. There is also an API available to automate the download of certificates and licenses.
The container runs using the Red Hat Linux base container image. This means that from the application’s perspective, SAS is running with a Linux operating system even though the machine you are running the container from may be Microsoft Windows. This is why when you receive your SAS licensing paperwork, it will say that it is licensed for the Linux operating system.
Analytics Pro can be configured for personal or shared use. The full range of configuration options are too vast to completely cover here. Watch this space as we will be providing deep dives into the various configuration approaches that can be taken. What we will cover here are the most visible differences you will see when using SAS through a container. The most visible are mount volumes and mappings.
Making directories/files available in the Container
The Analytics Pro container needs at a minimum two Volume mappings:
A Data folder mapped to /data in the container. This acts like your Linux home directory. It is typically used to store your SAS programs and any permanent data, etc.
A folder mapped to /sasinside. This stores your SAS licence, deployment certificates and any configuration overrides such as a custom autoexec_usermods.sas and sasv9_usermods.cfg files.
You can also mount any other additional folders from your host machine to the container. Some typical scenarios include:
Creating a mapping for /saswork and /sasutil for SAS temporary datasets and util files.
Creating a mapping for /var/log for system logs
One or more volumes for permanent SAS Datasets.
A python runtime to support the usage of PROC PYTHON
Volumes inside the Analytics Pro container act like Linux mount points. For those who come from a Windows background, this may be confusing at first.
Linux volumes don’t have the same concept of Drive names like C:\, D:\, etc.
Linux volumes do not use back slashes. They use forward slashes.
Linux mounts can be either a directory or a single file.
Say for example, you decide you want to have the following mappings in your Analytics Pro container.
When you use Analytics Pro, your programs will need to reference files and folders by their container path. So a libname statement like libname myenv "/sasdata"; would point to the directory C:\shared\data on your host.
It is important to note that the SAS analytics pro container will also apply permissions to some configuration files. Typically you won’t notice this except in cases such as mapping public/private keys for ssh which require restrictive permissions.
The 2nd most visible difference is that by default, your host username and password won’t be used. In SAS9, SAS will use the authentication provided by your local machine or server you are connecting to.
The Docker container does not have access to this by default due to the isolation principals mentioned previously.
By default, Analytics Pro will create a user account within the container which it uses to allow logging into SAS Studio. It will look for a .authinfo.txt file within your /data mount point. This is a simple text file which has the following contents:
default user myusername password mypassword!
If this file does not exist, SAS will create a default account called sasdemo and generate a random password for you and create this authinfo file in the /data folder.
If you choose to run Analytics Pro in a multi user scenario, then you may need to configure the container to utilise PAM authentication. This is done the same as you would normally within a Linux server.
There are also methods for tying user accounts inside a Docker container to the host’s authentication provider. We’ll cover these approaches in separate articles to give them justice.
Selerity Desktop to Make This Easier
We’ve created the Selerity Desktop (Personal) tool to help make deployments easier if you are uncomfortable with the above concepts. This personal edition allows you to deploy container environments with a series of additional options such as Python, Clinical Standards Toolkit and SAS OQ testing without needing to know any of the technical details. If you are interested in further information please see our product page for further information or reach out to us to discuss more complex deployments or licensing requirements.
Also stay tuned for future posts where we delve deeper into use cases that SAS Analytics Pro Cloud Native can support and other, more advanced, deployment options.
SAS Analytics Pro has been used by analysts, data scientists and programmers for decades, making enterprise grade analytics available on a personal desktop. This solution was first released on the SAS 9 platform and was typically installed on a Windows PCs for use by a single user. With the latest release of SAS Analytics Pro now on the SAS Viya platform, we have prepared a quick comparison of what is included as standard compared to the previous version on SAS 9.x.
Since it first became mainstream, advanced analytics has always been a gamechanger for businesses.
In the last few years, the analytics market has seen considerable growth as brands relied on advanced analytics to ensure the continuity of businesses amidst the fluctuating market changes by capitalising on new market opportunities.
Thanks to this, today, advanced analytics platforms are nothing new to the business world.
That said, with SAS software services, you get an analytics platform like no other.
With its integrated suite built for AI-powered analytics, data management and predictive analytics, it brings you a snapshot of the future changes in the market.
Analytics help you move quickly and decisively amidst these changes, adapting to the fluctuating trends during unpredictable times while navigating the competition simultaneously.
In this post, we take a deep dive into how advanced analytics powered by SAS software services can help you future proof your business.
Uncover insights that would otherwise go unnoticed
With SAS software services, you can access data in any format, including SAS tables, Excel spreadsheets and database files, enabling analysts to manage and manipulate them to get the valuable insights that drive business decisions.
Once the data is prepared and organised, you can then employ AI-driven analytics tools to look at variables and trends relevant to your industry and predict the growth, drawbacks, or anomalies in them.
In addition, AI-enabled analytics offered by SAS solutions makes data-retrieval and pattern-identification faster than human operations.
As the insight generation picks up speed, it also accelerates the designing of insight-driven strategies. This way, analysts and business masterminds can come with targeted decisions faster.
Identify refined customer preferences
Today, with analytics-enabled segmentation, you can manipulate data to identify more insightful customer information.
Going beyond the traditional demographic-based segmentation, now you also get segmentation based on data like consumer lifestyle, social values and relationships.
This information not only reveals current customer preferences but also delivers forecasts about trends in your target audience. The refined preference-based segmentation also lets you perceive your target audience better.
As a result, businesses can shape and tailor their customer experience by offering what they seek, including one-to-one personalisation. According to recent reports, using AI and advanced analytics to optimise the customer experience has enhanced shopping by 49% and spending by 34%.
What is more is that companies that optimise the customer experience through analytics outperform their competitors on key performance metrics, including profit, sales, sales or ROI.
Prepare to face and overcome future challenges
Strategists and decision-makers often come up with business ideas and initiatives to expand the business but deciding if these ideas will work or bring the expected results is a challenging process.
With advanced analytics tools such as SAS, you can enhance your risk sensing capabilities, which can help you evaluate the risk/reward ratio of ideas for new ventures.
Moreover, AI-driven analytics can even help you extrapolate data to formulate strategies to mitigate risks of these business opportunities, helping you capitalise on opportunities with a workable contingency plan to tackle potential risks.
Prepare for the future with SAS software services
SAS software services provide an integrated platform with easily accessible tools to predict future market trends, understand consumer behaviour and make decisions that allow you to reach your future goals.
At Selerity, our advanced analytics desktop can also improve your competitive advantage, maximise the ROI and enable the continuity of your organisation.
Advanced analytics models have been driving critical decisions in organisations since the dawn of the century.
With 2020 bringing significant changes to market conditions and companies having to navigate trade and supply chain disruptions, sudden fluctuations in demand, and new risks, the role of analytics in business decision making is more critical than ever in this new normal.
In fact, according to industry analysts, the compound annual growth rate of the advanced analytics market is expected to hit 21.9% by 2027.
With analytics models, businesses today can formulate informed decisions based on data-driven forecasting.
These data analytics models deliver insights into trends and patterns regarding employees, buyers, and competitors using multiple data sources like emails, instant messages, CRM applications, and social media.
Will AI replace advanced analytics models or, is it more efficient to use AI to enhance the performance of analytics?
Understanding the role of artificial intelligence in data management
Artificial intelligence technologies can perform tasks like reviewing records, running tests and providing insights based on the data.
Today these technologies are taking over many business processes, with approximately 15% of enterprises using AI technologies in their daily operations.
Artificial intelligence also allows you to leverage virtual assistants or bots, machine learning and machine vision, test analysis, deep learning and natural language processing, to get more nuanced insights.
Integrating these AI-powered technologies with analytics tools can bring you quality insights faster, enhancing your overall enhanced data management experience.
How can artificial intelligence enhance your analytics model?
Using AI-powered technologies like machine learning to enhance business analytics can deliver a more streamlined data collection process, as they have the potential to make the data acquisition and preparing process more effective, accurate and convenient.
When applied to business operations, AI-driven analytics can deliver micro-targeted insights like customer-product matches and upcoming purchases, allowing you to design and implement highly targeted campaigns and maximise your marketing ROI.
Here are some of the ways how AI-powered analytics can enhance your processes;
Handling labour shortages
Switching to AI or integrating AI with analytics can help you meet the labour shortages you may experience in your organisation.
While you focus on bridging the labour gap, your AI-driven analytics model can cover for you, meet the market demand faster and execute fool-proof campaigns without bottlenecks.
AI-driven classification models can categorise data and make it easier to access, retrieve and analyse data to get predictions.
Increased digital activity within organisations has created an influx of data that can get difficult to manage if you don’t have the right resources. When classified, data is easier to store and backup.
With clustering, data management is even more convenient and efficient.
Clustering models sort data into different groups based on similar properties making it easier to retrieve historical data and make a decision based on insights they provide.
With forecasting models applied to data, you can predict future events, including how many customers will convert, how many visit your store in a given time or how much sales to be expected.
AI is likely to enhance the performance of advanced analytics tools
Artificial intelligence integrated with advanced analytics makes most of your operations—like identifying market trends and testing assumptions—autonomous. What you get by integrating artificial intelligence in your analytics tools are enhanced analytics capabilities.
You can access tools and resources to assimilate data and make strategic, data-driven decisions that ensure financial security, increased sales and improved productivity with AI-driven analytics.
If you want to know more about AI-enhanced analytics, don’t hesitate to give us a call. Our team at Selerity is ready to help you optimise your SAS Analytics experience.
Business data is a valuable resource for every contemporary company.
This information holds insight into factors like patterns in customer behaviour, avenues for cost savings, and offers a chance to accelerate and optimise business progress.
Today, 83% of organisations see data as an integral part of their business strategy. 69% of organisations, however, have noted that inaccurate data has reduced the quality of their work.
Mitigating the risks posed by inaccurate data is the reason why companies need optimised data exploration and analysis that allows them to leverage reliable data for operational enhancement.
When you operate with analytics software like SAS, you get more than just data analysis tools. It also helps you access and organise your data more strategically, which means this data can also be leveraged more effectively.
Keep reading to find out how SAS data management solutions offer one of the best platforms for data exploration and analysis.
Access data across multiple platforms
Storing data in one accessible and centralised location (the cloud or Hadoop) makes it easier to create more seamless operations.
With SAS, you can access your data from wherever it is stored, without having to change the data location. This means that business analytics and data scientists will have access to more data across multiple sources, formats, and structures.
This software allows you to integrate your work with other data flows, all while scheduling and monitoring the process using SAS technologies.
Owing to its integrated system, it also shortens the time taken to perform key processes. With database technologies, for example, you can analyse your data within the database itself.
As a result of easy data access and monitoring, your authorised users can perform data exploration and analysis without relying on your IT team for data provisioning. With tools like the built-in business glossary, users will also have a more comprehensive understanding of the processes they handle—improving productivity and ensuring smooth operations across the board.
Benefit from straightforward data governance
When you have to move data from the location where it is stored to another for management purposes, it disconnects the sourced and managed data. This makes it difficult to govern your data, especially big data.
When data movement is minimised, it’s easier to initiate data governance processes and policies. It will also let you maintain the quality, privacy, and security of your data without disruptions.
The integrated, end-to-end event designer in the SAS data management platform helps you build and edit data processes with ease. This will enhance your efficiency when it comes to the governance of metadata related to administrative and business operations.
Easy data governance also lets you reuse data management techniques. Your company can deploy these flexible rules and maintain consistent standards for your data management.
Strategise data-driven plans with reliable insights
40% of business strategies fail due to inaccurate data. To lower this risk, it is essential to have accurate data and insights.
SAS data management brings you a platform with built-in auditing tools to monitor and process data, ensure high-quality data, and maintain transparency. As a result, you can seamlessly extract reliable data that is ready for visualisation, analysis, and operational use.
The integrated tools further optimise your analytics by cleansing your data, removing invalid data, and giving you reliable data for more accurate strategising. Features like SAS Visual Analytics let you explore data visually, find new patterns, and publish reports on both web and mobile devices.
Rolling out a new business strategy needs careful planning that SAS solutions can support by making it easier to extract and explore data, and lower inaccuracies and inconsistencies so you can make swifter, data-driven decisions.
SAS data management solutions enhance your operations
Effective data exploration and analysis are vital for any business to gain a sustainable competitive edge. It also reduces security risks, increases productivity, improves responsiveness, lowers data loss and heightens cost-efficiency.
By partnering with Selerity, you can access the right resources for an optimised experience with SAS Analytics tools. A SAS ecosystem that’s managed by Selerity will promote data quality and extract accurate, reliable insights so you can handle your data with ease on one platform.
Don’t hesitate to contact our team to learn more about how you can enhance your data management through SAS data management solutions.
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.
It facilitates collaborative decision making
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.
It delivers easy access and data management
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.
It helps make informed decisions with reliable data
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.
Ensure faster and easy analytics with SAS analytics business intelligence and data management
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.
Today, many businesses are operating in cyberspace, which has led to an increased demand for novel and effective ways to protect their assets and customer information from malicious actors.
In many instances, security threats lurk within businesses and can cause significant financial and reputational damage.
According to studies, the majority of the security breaches are caused by rogue employees accessing restricted areas information or simply a security oversight from an employee.
These internal threats are difficult to detect, especially when a company has a large number of employees.
While basic security measures such as passwords can help protect accounts and valuable business information, criminals find sophisticated ways to exploit these systems.
What this means for businesses is that they need an alternative approach to ensure the security of their data. Biometric analytics has emerged as the most comprehensive tool for this job.
Today, biometrics has become a part of life, as most of the devices we interact with leverage biometric authentication to protect our sensitive and personal information. An increasing number of modern businesses are also using biometrics to protect their data.
Studies reveal that the biometrics industry will be worth over $59 billion by 2025.
To truly understand the value of biometrics and data analytics in the business world, we need to explore what biometric data analytics can do for businesses.
Monitoring employee behaviour
Among all the biometric scanning technologies, fingerprint scanners are the most common.
As fingerprints are unique to each individual, these scanners are effective at identifying different individuals and denying access to intruders.
Today, many businesses use fingerprint scanners to monitor the attendance of employees. Fingerprint scanner data is used to calculate the working hours, the number of workdays and clocked in/clocked out times.
Biometric data analytics on fingerprint data can also be used to monitor employee movements during working hours.
For instance, certain parts of the company, which hold sensitive information or assets can be protected with fingerprint scanners, allowing only authorised personnel to access these areas.
Biometric analytics alert businesses if any unauthorised fingerprints are detected at these high-security areas, allowing the company to take the necessary safety measures quickly.
Protecting customers by keeping out suspicious individuals
Facial recognition technology is a more recent form of biometric authentication but has developed considerably over the past few years.
While facial recognition is not accessible to many small businesses, established businesses leverage this technology to protect their assets and customers.
American Airlines used facial recognition technology to streamline their customer’s trips through the airport and prevent unauthorised border crossings.
The company used facial recognition systems to analyse the facial features of every passenger and compare it with the border protection database—which includes information about individuals who are not allowed to cross borders—to confirm the identity of a traveller and alert the authorities if they are matched with the border protection database.
These systems are effective at keeping dangerous or wanted individuals off flights and protecting the airline, their passengers and employees.
Making recruiting safer
Businesses need to be aware of who they are recruiting to fill important roles because, as mentioned before, many security threats come from within the organisation.
By analysing biometric data from fingerprint and facial recognition technology, businesses can run background checks on their prospective candidates to see if they have a criminal record and determine if they are suitable to recruit.
Biometric data analytics is a valuable tool for any business
When biometrics and data analytics go hand in hand and play a major role in how modern businesses protect themselves and their customers against threat actors.
As seen in this post, biometric data analysis can be valuable for all businesses across industries.
If you’re looking to leverage biometric data analytics, give our Selerity analytics desktops a try. This is a SAS ecosystem designed to enhance your SAS experience.
Accurate data is an important resource when it comes to doing business in the modern age. Having swift access to this data allows you to stay competitive in the industry.
As a statistical software used for data analytics, SAS is one of the best platforms that can help you improve and evolve your business. With features like innovative analytics, data management tools, and business intelligence software, SAS is a platform that is trusted around the world.
Whether it’s navigating through challenges in the market or deploying new strategies that guarantee optimised results, SAS provides useful insights for faster and better decision-making.
What are the benefits of using SAS?
SAS Analytics comes with many tools such as dashboards, predictive analytics, and real-time analytics that let business owners explore and utilise data that is relevant to their businesses.
With the help of SAS, companies can extract useful and accurate data and leverage information to improve their business strategically. SAS also allows you to gain insight into business operations by making it easier to analyse and comprehend big data.
With detailed and accurate data, you can ensure better decision-making and better business performance overall.
How can SAS provide faster insights?
With its many, integrated tools, automated analytical functions, and intuitive navigation interface—using SAS for data analysis has become a method of extracting useful information on a swift timeline.
As a platform utilised in business operations, the speed you gain from using SAS for data analysis is not only a result of its accurate functions.
The easy navigation offered to users allows teams to solve complex issues that were too time-consuming or impossible to solve accurately before. Among the experts who use SAS for their business operations, it is also known for providing high-performance tools built for professional analytics.
Combined with its user-friendly interface, these tools make it easier to navigate and operate, fast-tracking the process of gathering useful insights.
The tech-driven analytics functions also perform faster and more accurately than traditional, manual processing. As a result, you will not only be saving time on data gathering but also cutting down on the time you would have spent rectifying errors with more manual processes.
Accurate analysis and comprehension of data
Using the Visual Analytics feature offered by SAS, you can access insights into new data sources. Users can also create visual representations of data that make data analysis faster and easier to understand.
On the other hand, SAS Contextual Analysis lets you identify emerging issues in the industry, buying patterns, and trends in the market in unstructured data without requiring prior knowledge of its contents.
The advanced analytics tools featured in SAS let you not onlymeasure your success, but also identify the threats to your business.
With accurate information provided by prescriptive and descriptive analytics on your side, you can mitigate risks and initiate strategies that can help you overcome potential challenges.
Swift automated text analysis
The automated processes introduced by SAS also support the delivery of faster insights. Processing data with the help of technology makes the data extraction and analysis process more efficient and less prone to errors.
The automated text analysis feature can assess textual data that is collected from portals such as social media, customer calls, or comments—data that cannot otherwise be accurately assessed until the development of a manual taxonomy.
This allows you to analyse the data, its characteristics, and the relationships within it faster and utilise this information to uncover relevant patterns.
SAS for data analysis—faster insights and accurate results
Every business is trying to get ahead in the industry by using every tool and resource they have at their disposal. This means that you don’t just have to be accurate and efficient with your processes but also faster.
Using SAS for data processing and analysis lets you have it all.
What makes it quicker is not just the speed of delivery, but also the advantage of having accurate information that is easy to comprehend and makes your decision-making processes easier than ever.