Quick deployment to dashboards and analytics features (using SAS Visual Statistics and Enterprise Guide). Easy to create a simple forecast and discover business insights using segmentation tools.
IT Project Manager with 5,001-10,000 employees
Easy to create a forecast, discover insights, but visualization needs work
Pros and Cons
- "Quick deployment to dashboards and analytics features (using SAS Visual Statistics and Enterprise Guide). Easy to create a simple forecast and discover business insights using segmentation tools."
- "Better connectivity with other data origins, better visualization, and the ability to create KPIs directly would all help."
- "There are scalability issues. It depends on the data volume and number of end-users. VA requires a lot of hardware resources to move volumes of data."
How has it helped my organization?
What is most valuable?
Agility in dashboard creation and evaluating statistics with simple models.
What needs improvement?
- Connectivity with other data origins
- Visualization is poor
- Create KPIs directly
For how long have I used the solution?
One to three years.
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SAS Visual Analytics
October 2024
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What do I think about the stability of the solution?
Normally it is stable, if the hardware is correct.
What do I think about the scalability of the solution?
There are scalability issues. It depends on the data volume and number of end-users. VA requires a lot of hardware resources to move volumes of data.
How are customer service and support?
Seven out of 10 for the SAS Support home page and direct support.
Which solution did I use previously and why did I switch?
QlikView and Qlik Sense, IBM Cognos suite.
How was the initial setup?
The straightforward part: Create a dashboard and calculate attributes. You don’t need programming experience using SAS Visual Analytics. You can export to Excel or PDF directly. The complex part: Creating a relationship between tables.
What's my experience with pricing, setup cost, and licensing?
Compare pricing online with other vendors; licensing type is simple.
Which other solutions did I evaluate?
Qlik, IBM Cognos, MicroStrategy, and others.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Senior Manager, Group Analytics at a financial services firm with 10,001+ employees
Empowers our citizen data scientists to answer their own business questions regarding customer trends and to forecast future trends.
What is most valuable?
- Fast interactive dashboard designer
- Visual data explorer (network analysis, sankey, model segmentation)
- Inclusion of a really good predictive modelling visual interface
How has it helped my organization?
- Exposure of full population transactional data to customer facing roles with ability to analyse trends and patterns
- Empowers our citizen data scientists to answer their own business questions regarding customer trends and to forecast future trends
What needs improvement?
- Random forest modelling in visual statistics
- Ability to select category for display in data object from control such as drop down list
For how long have I used the solution?
I've used it for 18 months.
What was my experience with deployment of the solution?
There were no issues encountered with the deployment.
What do I think about the stability of the solution?
There have been no issues with the stability.
What do I think about the scalability of the solution?
There have been no issues encountered while scaling it.
How are customer service and technical support?
Excellent service as usual from SAS.
Which solution did I use previously and why did I switch?
SAS Visual Analytics was been chosen by my employer as one of two applications for strategic data visualisation. Tableau has been adopted for general purposes, whilst SAS Visual Analytics and Visual Studio are deployed for data visualisation where analytics are the focus as opposed to regular dashboarding.
What's my experience with pricing, setup cost, and licensing?
Pricing is excellent and beats a lot of the competition, especially when scaling.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Buyer's Guide
SAS Visual Analytics
October 2024
Learn what your peers think about SAS Visual Analytics. Get advice and tips from experienced pros sharing their opinions. Updated: October 2024.
816,406 professionals have used our research since 2012.
Senior Manager at a consultancy with 201-500 employees
Easy to learn and use with good scalability potential
Pros and Cons
- "It's quite easy to learn and to progress with SAS from an end-user perspective."
- "The installation process can be a bit complex."
What is our primary use case?
We usually use SAS more from an ETL perspective.
We implement projects with these technologies, however, at the end of the day, the client is purchasing those technologies. We do not purchase these technologies, we just use them in projects.
We are all always end-users. Although we are not using it, we are implementing it for the end-users. We are using the tools.
Essentially we use the product a lot for ETL, for extracting, transforming, and loading data from one data structure to the others. We also use it a lot for reporting purposes.
What is most valuable?
The solution is very stable.
The scalability is good.
The usability is quite good. It's quite easy to learn and to progress with SAS from an end-user perspective.
What needs improvement?
Regarding performance, they have some issues. They have always had some issues there. They are better, however, still, there are some issues around performance.
The installation process can be a bit complex.
For how long have I used the solution?
I've been using the solution for more than ten years at this point. It's been over a decade. We've used it for a while.
What do I think about the stability of the solution?
The solution is very stable. However, the performance is something that needs to be looked at.
SAS is the kind of platform that if you are a power user and you know how to use, it's good and most probably you'll just bypass these performance issues. However, as SAS is used quite a lot by business users, at the end of the day, they don't do it as it should be done from a technical perspective, and then they just have some performance issues there.
I would say most probably the problem is more about the end-user who is using the platform, however, at the end of the day, there are bottlenecks.
What do I think about the scalability of the solution?
The scalability of the product is very good.
In our organization, we have at least ten to 20 consultants that have some knowledge of SAS. We have more than ten customers that use SAS.
How are customer service and support?
The technical support is good enough. I don't think it's excellent, however, it is good enough.
Which solution did I use previously and why did I switch?
SAS has a lot of clients here in Portugal. Oracle does as well. Those are two good platforms here.
How was the initial setup?
In terms of deployment, it is not quite a straightforward installation. It's still a little bit complex to install SAS.
That said, from a support perspective, at least, we don't have complaints. They are there and they support us throughout the process if we need assistance.
I'm not so technical that I could comment in detail about the deployment itself. It depends on the solution that you are using from SAS. If you are just installing, for instance, the SAS Enterprise Guide or Data Integration Studio, it does not take more than one week and you are good to go. If you are installing other packages, more complex packages like Data Management and so on, it could be longer. SAS is quite a big landscape. It's difficult to comment on SAS as they are quite a generic provider. They have everything from data to campaign management. We used them a lot in the past. It's quite a big portfolio that you can install from this platform. Therefore, to properly calculate deployment times you need to find out which package you are trying to use from SAS and if there are different challenges within those implementations based on your business.
How big of a team you need for deployment also depends on the project, as we always look at it from, if we are a service provider once again, the size of the project. In a very big utility company, we need a lot of technical team members. If we are implementing SAS just for doing some ETL stuff in a small insurance company, we just need one guy, probably. It's difficult to calculate without parameters o base an estimate on.
What's my experience with pricing, setup cost, and licensing?
Customers do have to pay for a license, as well as for Enterprise Guide, Data Integrations Studio, and so on. I do not recall any project without paying for some sort of SAS solution unless you are just implementing a small POC. That can happen. Once the POC is over and the company goes into the production environment, they need to pay.
What other advice do I have?
Usually, we use the on-premises deployment, however, it can also be deployed in the cloud as far as I know.
I would recommend the solution to others. It's a platform that I really enjoy. We, from a company perspective, always recommend SAS. It has quite an interesting portfolio.
I'd rate the solution at an eight out of ten.
Which deployment model are you using for this solution?
On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Project Manager - Business Intelligence at www.datademy.es
When it comes to statistical analysis tools, SAS and R lead the way, but are different in important ways such that the pro's and con's of each should be considered.
Originally posted in Spanish at https://blog.mbitschool.com/2015/05/data-science-tools-sas-vs-r.html and https://sasybi.blogspot.com.es/2015/05/data-science-tools-sas-vs-r.html
Some of the disciplines that have experienced the most development in recent years have all related to data science. The techniques and tools used in this discipline are gaining more weight in the business environment. If we take a look at the tools most commonly used, we see that there have been changes in recent years. The next (www.datasciencecentral.com) chart shows the major tools currently used by data scientists.
They are the most sought after today and represent the long trend led by R, and if we focus on existing posts, the leader has been SAS. We could add to those listed in the chart Phython, which although is a general-purpose language, its use in data analysis is increasingly widespread. We can also add to previous tools such as SCALA, RapidMiner, Weka or KNIME.
In this post, we will try to compare two of the most-used tools: SAS and R. Besides being the most-used tools, they also represent different architectures, different orientations and from the point of view of costs: paid vs free. Probably much more interesting to compare a Ferrari with a Lamborghini, to compare R with SAS, but although we also purchased speed, and cost usability, in the business-analytics context we focus on SAS and R.
Do not lose sight that considering the hectic pace leading the IT industry, if we make the same comparison within two years, tools will have evolved and certainly the criteria to assess them, there will be the need to integrate new data types into the analysis.
We begin with a brief introduction of both tools:
SAS: data analysis tool with tradition. It takes many years to lead the market and present in large accounts. It has several tools for data analysis: SAS / BASE, SAS / Enterprise Guide and SAS / Enterprise Miner. Annual term licenses at a cost affordable by only large accounts.
R: data analysis tool, unless you're a SAS veteran, but with a remarkable presence in the market. Widespread in universities and research centers, it is entering with force in the business landscape. Open source license. Extensive community and active users, the amount of available libraries is growing by the day.
The comparison criteria to consider are:
- Ease of use / learning curve.
- Management and data management
- Graphic and visualization capabilities
- Software updates
- Support services and communities
- Workflow capabilities
- Ecosystems
- Integration with other languages and tools
- Licenses and costs
Ease of use / learning curve:
In this aspect SAS may be a simpler language for non-programmers, and there are many business analysts who must use such tools without prior technical background programming. SAS data steps are easy to learn for anyone even slightly acquainted with table structures, as it has a design type DML (Data Manipulation Language). Moreover SAS proc SQL allows the option to write SQL code directly in R may demand a more solid base of knowledge in programming and data structures. If SAS is similar to SQL, R would have its equivalent in C++. In structuring, R is an object-oriented language, while SAS responds to a type of structured, sequential language. R can do the same thing in many different ways, for example, if SAS aggregations, we'll go to a proc SQL aggregation or a PROC MEANS. But in R, there are multiple ways to do this (aggregate, summarize, apply Functions, Doby, etc.). This can be confusing to the novice who is learning R. As for training resources, it's easy to find useful resources on the web. SAS has certifications, but this formal training is also expensive.
Management and Data Management:
The key difference in data management is that R works in memory and SAS disk. Working mostly in RAM has its advantages and disadvantages, facing processes with high-volume datasets R records should be taken into account. There are libraries that allow R disc also work. SAS processes has traditionally been a problem footprint and libraries as the work must be well managed. Both work well paralleling processes.
Graphical and visualization capabilities:
The graphics capabilities of SAS focus on SAS / BASE and SAS / Enterprise Guide and, without considering SAS / Visual Analytics is licensed part, they are pretty fair. SAS in this area covers the essentials, at least in their own modules of data mining. Besides it is not limited in its use intricate. R, however, has very potent display capabilities and numerous packages with advanced functionality.
Software updates:
Due to the nature of open source, R has new algorithms and techniques readily available as individual packages are updated. To date R has about 15000 packets in CRAN (Comprehensive R Archive Network). SAS's policy of regular releases of commercial software, so that R can have more flexibility to incorporate new functionalities, although it may do SAS tested in a controlled environment.
Support services and communities:
R has a widespread and community but has no support, even if you have SAS support. In everyday practice, the broad user community for R (forums, questions, resources), supplies more than the lack of support. That said, some people are more relaxed having support on the other side of the line or you resolve the problem or you can "push" to an alternative solution.
Workflow capabilities:
SAS module features the Enterprise Guide an intuitive interface for developing process flows analytic. There are different tools based on R which also allow the development of workflows (an example is Rattle), but the have not been finally imposed, nor are they optimized. Experience shows that many analytic processes are not supported by the components of these tools and, for example, in the case of SAS, most code is purely SAS / BASE and of little use to the predefined components of which Enterprise Guide is made.
Ecosystems:
SAS provides a range of tools in fields near the Science Data as Business Intelligence, Dashboarding, Data Visualization, Data Warehouse, ETL and Data Quality, which can be integrated with data science processes (end-to-end), while R is a language focused exclusively on data science.
Integration with other languages and tools:
With regard to integration with other tools and languages, it is possible that R will take the lead from SAS. There are many open-source community tools that are integrated with R and rarely does commercial software not offer integration with R. Logically, SAS also has integrations and partnerships, analytical environments, but perhaps stay one step behind.
Licensing and costs:
There is little to say on this: as we know R is open source and SAS is commercial software with high cost. It would be interesting to see what happens in terms of trends of use if SAS lowered prices. So far it has already released one version for free training (SAS OnDemand for Academics). There are approaches in line to use both, something perfectly acceptable since R is free. There are facilities that use SAS for all data management (extraction of sources, merging, cleaning, application of business rules, consolidation, etc.) and allows the final dataset R prepared to apply the statistical model and perform the final presentation. Not a bad approach, especially considering that we can save the license SAS / Enterprise Miner (models) which is the most expensive .Equally useful is to know some equivalences between code level tools: SAS and R Equivalents
Finally an interesting study in which SAS or R preference based on years of experience is analyzed.
In this brief summary we have tried the aspects we consider most critical, this post serves as a home to possible ways to provide comments or considerations not listed in this compendium and that may also have relevance in the selection of the data analysis tool. We Also welcome contributions about other tools (Python, Matlab, SPSS, SCALA, etc.).
Interesting training services about SAS and R, ask at: cursos_a_medida_r@yahoo.es
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Has an easy initial setup process with good scalability features
Pros and Cons
- "The technical support services are good."
- "SAS Visual Analytics could be more user-friendly."
What is most valuable?
The product has all the essential features.
What needs improvement?
SAS Visual Analytics could be more user-friendly. There could be more integration with different computer languages. Additionally, there needs to be more API features.
For how long have I used the solution?
I have been using SAS Visual Analytics for more than five years.
What do I think about the stability of the solution?
I rate the platform's stability an eight out of ten.
What do I think about the scalability of the solution?
It is easier to scale the product. We have small businesses as our customers for it.
How are customer service and support?
The technical support services are good.
How was the initial setup?
The initial setup process is quite easy.
What's my experience with pricing, setup cost, and licensing?
The product is expensive.
What other advice do I have?
I rate SAS Visual Analytics a nine out of ten.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer:
Project Manager - Business Intelligence at www.datademy.es
Data preparation, exploratory analysis, and Report Designer make this visual BI tool an effective solution, but there are competitors that also provide similar features.
Originally posted in Spanish at https://sasybi.blogspot.com.es/2015/07/sas-visual-a...
SAS Visual Analytics is a business analytics solution that allows you to visually explore all data in an easy-to-use platform that's accessible to users of all levels without statistical, technical, or design skills.
Visual Analytics within BI solutions that are available on the market are positioned within the analytical displays solutions group. In this group, we have solutions such as QlikView, Tableau, and TIBCO Spotfire, amongst others. In summary the proposed solutions have:
- Analytical visualization tools that allow interactive analysis, relying on visualization capabilities and agile data management, allowing them to perform a free analysis of the data model imported into the tool.
- The orientation of these tools is usually self-BI, facilitating the integration and analysis of data with little IT intervention.
- The visualization capabilities likewise allow you to make clear and effective presentations that aid decision-making.
- Agility and speed data management technologies rely on in-memory.
- These tools are supported by an intuitive interface that facilitates data exploration aimed at both IT and business analysts profiles.
SAS Visual Analytics offers a complete analytical platform for displaying information, allowing you to identify patterns and relationships in data that were not previously apparent. The interactive capabilities of self-service BI and reporting combine with advanced analytics for all to help discover knowledge of data of any size and type.
Let us now look at the features of the tool, analyzing each of the main modules, and its technical architecture:
- Data Preparation importation and preparation of data for later viewing and analysis.
- Exploratory analysis is a module to explore, analyze and visualize data in order to identify patterns, trends and knowledge in the data.
- Report Designer is the reporting module for report design and dashboards.
Data Preparation
SAS Visual Analytics has a module for importing data and other data preparation based on SQL which allows adapting imported data to the optimal structure for its exploitation. For most potential analyses, the recommended tool works on a table that consolidates aggregate information from multiple tables and starting file. This is the classic board obtained as N junction fact tables and dimensions. The tool also enables the option of working with a model in which star felling facts and dimensions would be separate tables.
The tool has a module for data preparation that allows data transformation on imported data for performance analysis based on a SQL query builder. This module may, thus, stop a little when transformations to be performed are fairly complex. In this case, I propose using SAS Enterprise Guide, offering the choice of Visual Analytic Pro (Visual Analytics + Enterprise Guide).
With the fields of the imported tables, it is relatively easy to derive the calculated fields using elements in a simple way, giving access to a powerful expression editor.
Exploratory analysis
One of the main differences of SAS over other analytical tools are its display analytic capabilities (predictive techniques, time series, associations, etc.) based on the long experience of SAS tools such as SAS Enterprise Miner. The algorithms apply predictive analytics for automatic detection, and you can get detailed info on the selected algorithm. You can easily create decision trees for groups or classifications in the data, as well as box-plot diagrams to learn more about the distribution of data.
The ability to easily obtain time series for process Forecasts. These processes are very simple to implement, but would fall short if we think of a more industrialized forecast that would make a massive entry which would result forecast for other systems (e.g. forecast need for stocks), in these cases it is advisable to go solutions SAS Forecast Server type.
In predictive processes, we can use the functionality " underlying factors "that allows us to evaluate how other variables affect our prediction can perform scenario analysis and simulations, "what-if".
It has the ability to connect through add-in to Visual Statistics for processes that need more advanced statistical analysis.
Utilities to learn about the relationships between variables, such as correlation matrices. Descriptive statistics that provide insight into the distribution of values in the variables (minimum, maximum, average, zero, etc.)
Report Designer:
Report Designer very intuitive use (drag and drop). We can easily create reports or dashboard using the graphics and visualization objects as include indicators or classifiers select.
Ability to incorporate dashboards analysis documents obtained in the process of exploratory analysis.
Once you designed a serial graphic objects on a document we can define interactions between them, to relate the selections made some of them to other objects or to define navigation between them.
SAS Visual Analytics incorporates multiple possible visualization box plots, heat maps, animated bubble charts, network diagrams, decision trees, geolocation. Likewise, auto charting capabilities help determine the most appropriate graph to display the data according to the elements selected for analysis. A bar overview allows you to zoom on the range of data that you want, without losing the whole picture.
Dimensions and hierarchies Organization for OLAP analysis multidimensional.
Creation, display, publication and distribution of multi-device analysis and reporting. Integration with Office Outlook, SharePoint, Excel and Power Point
Technical architecture:
Response times are nimble because the data is loaded into memory based on SAS LASR (server analytical high performance memory). It also has solution oriented Cloud with an on-premise option.
In short it is a powerful analytical tool display, which is an interesting option for companies without having to make a large initial investment, want to start making analytical, with the ability to scale and grow into other tools.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Head of BI at a tech services company with 51-200 employees
Integrates well, has a good interface, simple to learn and easy to use
Pros and Cons
- "It integrates well with SAS, making it simple and quick for developers."
- "It is not as mature as competitors such as Tableau and QlikView."
What is our primary use case?
It's an interesting platform for the front-end.
It can be deployed both on-premise and on the cloud. Our customers prefer to use the on-premise version for their financial institution.
What is most valuable?
SAS Visual Analytics is an excellent platform. The user interface is good, it has a good look and feel. It is simple to use. It integrates well with SAS, making it simple and quick for developers.
It does not require a high level of skill, but rather a medium level. It is very easy, and fast to learn. It is not a problem. I have people who don't know the product but can work in a very autonomous manner after one week.
What needs improvement?
It is not as mature as competitors such as Tableau and QlikView.
It is expensive, and not really easy to install.
What do I think about the scalability of the solution?
SAS Visual Analytics is extremely scalable.
One of our clients is an Italian bank. One of their installations serves 12,000 people at the same time across Europe.
Tableau and QlikView, in my opinion, would have difficulty doing this, whereas SAS Visual Analytics can do it easily.
Which solution did I use previously and why did I switch?
Power BI by Microsoft is currently growing rapidly in Italy, but I believe this is due to the fact that it is much less expensive than its main competitor. It's inexpensive, which is the primary reason, it is rapidly expanding in the Italian market at the moment, and we are working with it.
How was the initial setup?
SAS Visual Analytics is not easy to install.
This is a platform issue, but from what I understand with the SAS competitor, other on-premises installations have the same problems. These problems may be linked when we use a cloud solution. This could be the difference. This is yet another reason why Power BI over Azure is gaining popularity and is growing quickly.
What's my experience with pricing, setup cost, and licensing?
SAS Visual Analytics is expensive, as is the rest of the platform.
What other advice do I have?
I would rate SAS Visual Analytics a nine out of ten.
Which deployment model are you using for this solution?
On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Project Manager - Business Intelligence at www.datademy.es
It enables forecasting without a background in statistics, but data preparation and management need work.
What is most valuable?
- Visual analytics
- Reporting
- Predictions features
How has it helped my organization?
We have detected a high margin group of customers with very little work.
What needs improvement?
Data preparation, and data management need work, as without Enterprise Guide, if you use SAS/VA alone (not SAS/VA pro), it will be hard to do the data preparation.
Forecasting is a very easy tool to use, and you don't need a great background on statistics. However, if you need to do forecasting with many groups of data in an industrialized way, then SAS/VA is not a suitable solution, because forecasting in SAS/VA is easy, but it needs a lot of manual work.
For how long have I used the solution?
I've used it for one year, alongside other SAP products such as SAS/Enterprise Guide.
What was my experience with deployment of the solution?
Data preparation problems, as SAS/VA needs a big, aggregated (all columns) table to work well. We didn't know the importance of data preparation.
What do I think about the scalability of the solution?
We work in the cloud, and therefore it was quick and easy to implement.
How are customer service and technical support?
Customer Service:
I would rate them high as they're good and quick.
Technical Support:I would rate them high as they're good and quick.
Which solution did I use previously and why did I switch?
I knew Business Objects and QlikView. I started with SAS/VA because the client needed prediction and forecasting features.
What about the implementation team?
I used a vendor team whose expertise was high. There was also a third party consulting team, with high-medium expertise
What's my experience with pricing, setup cost, and licensing?
The initial cost is just for the licenses, and the day-to-day cost is the consulting services.
Which other solutions did I evaluate?
We also looked at Qlikview. It was good at visualization, but poor about prediction and forecasting features, so we chose SAS Visual Analytics.
What other advice do I have?
It's important to have a data preparation tool like SAS/Enterprise Guide if your data model is complex or your volume of data high.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Updated: October 2024
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Great to hear feedback from a long term real-world situation!