Enterprise Business Intelligence

Running head: ENTERPRISE BUSINESS INTELLIGENCE
ENTERPRISE BUSINESS INTELLIGENCE
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1ENTERPRISE BUSINESS INTELLIGENCE
Introduction
The SAS enterprise miner or SAS EM is an advanced level data analytics software
that is designed to help users of the software develop predictive and descriptive models
quickly in a short period of time using a more streamlined process of data mining. The
enterprise miner’s interface is more graphical which allows the users to move through the
five step of SAS SEMMA approach more logically. These five steps are: exploitation,
sampling, modelling, modification and assessment. The users are allowed to select their
process flow by selecting the suitable tab from the software’s toolbar and then use the drag
and drop method to bring the needed nodes into the pallet. The SAS enterprise miner
software supports many different types of techniques and algorithms which includes time
series, market basket analysis, decision trees, link analysis, linear and logistic regression,
neural networks and web path analysis. The SAS enterprise miner’s architecture of the client
server helps data analysts and business users to join together, collaborate and share their work
and other models. The SAS EM client software is designed to run on windows 8, 7 and 10.
There are multiple options that can be used as hosts for this software which includes IBM
AIX R64, Solaris on Sparc version 10, HP-UX on Itanium 11i version 3 and IBM z/OS
V1R12 or higher.
Data mining process for SAS EM
The main steps which are followed in the SAS enterprise miner’s data mining process are:
1. Sample of the data is created by diving in to multiple datasets. The sample should be of a
size big enough to have all the needed information but compact enough to process. The data
preparation tools are included in this step. These tools are used for append, data import, filter
and merge.
2. The data set is then explored and searched for trends, relationships and anomalies to gain
better understanding of the dataset. This step uses tools that are required for graphical
exploration, variable selection methods and statistical reporting.
3. The data can then be modified by selecting, transforming and creating the variables to put
focus on the process of selecting the model. The step uses tools that are needed for value
recording, interactive binning and missing value handling.