Wednesday, April 12, 2017

Data Analytics tool - SAS



Some people see data as facts and figures. But it’s more than that. It’s the lifeblood of your business. It contains your organization's history. And it’s trying to tell you something”            

SAS is a business analytics tool that helps businesses transform a vast amount of data into valuable insights that companies can use to make smart business decisions and improve profitability. SAS helps companies to get insights about what they’re doing right, what is working and what isn’t working.
SAS is utilized by a lot of Industries such as: retail, banking, health care, government, manufacturing, hotels, insurance, defense and security, media, utilities, travel and transportation, sports, capital markets, automotive, communications, small and midsize businesses and many others.
Some of the main uses of SAS are:
ü  data entry, retrieval and management
ü  statistical and mathematical analysis
ü  business planning, forecasting and decision support
ü  report writing and graphics
ü  operation research and product management
ü  quality improvement
ü  applications development 
By utilizing SAS, companies can gain competitive advantages by:
·      Integrating large amount of a high quality data
·      Performing sophisticated analysis and make a faster decisions
·      Improving information management and governance
·      Making faster recommendations
SAS software consists of more than 200 components, some of the most widely used are:
·      Base SAS - basic procedures and data management.
·      SAS Enterprise Miner - allows performing data mining and based on analysis conducted on vast amount of data helps to create highly accurate predictions.
·      SAS Studio - allows to access data files, libraries and create new programs.
·      SAS Visual Analytics - enables to create a variety of graphs, dashboards and reports that helps to gain a better understanding of a business performance.  
·      SAS Enterprise Guide – enables to analyze data and publish results in fast in a time efficient way. 
·      SAS Data Management- provides efficient way to clean the data. Enables to easily remove the outliers that can mess up the outcome, Input a missing data values or drop the missing variables of data so the system could deliver more sophisticated and accurate results and predictions.
·      SAS STAT – by providing the latest statistical technics it enables to conduct a variety of statistical analysis such as: market research, regression, predictive modeling, multivariate analysis, cluster analysis, distribution analysis and many others.  
·      SAS Enterprise BI Server - provides an easily associable data and enables business users to get an access to SAS Analytics by using Microsoft Office applications. 

An example of how we can utilize SAS Enterprise Miner in practice: A grocery store is planning to arrange products by placing items that are usually purchased together on different aisles of the store. The plan is to make people walk through the entire store in order to find the products they are looking for. The idea is to expose people to other products that they may like while they are searching for a specific item. By doing so, customers are exposed to certain brands for a longer period of time and therefore there is higher level of brand exposure.  In addition, customers will spend more time in the store thus improving the chance that customers will purchase more products. SAS Enterprise Miner has a function that is called Association analysis (it finds products that are bought together or are somehow related to each other). After uploading large data sets of item purchase history and running the associations analysis node, the SAS software will illustrate the results of the products from the store that are usually bought together. See the examples provided below in pictures A and B. Picture A is output form the model that illustrates the likelihood that customers will purchase certain items together. Picture B Illustrates the way we would allocate the products on different aisles of the store based on the data that we received from the SAS results.       
     Picture A

      Picture B





Find more information about SAS and other data analytics tools on Kayli Tarcy's blog.





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