A vital but rare skill among technology management teams is the ability to prepare and apply tools that help an organization adapt to the present and future and not just respond to the recent past.
BA (business analytics) and BI tools enhance an organization’s ability to survive by facilitating a full range of defensive strategies, such as reducing risk or lowering inventory, and aggressive moves, such as finding new customers or higher-margin markets. With that in mind, SAS Institute recently updated its data-mining dynamo, Enterprise Miner 5.1 for SAS9. The improved model-design options make a strong argument for bringing data mining into the enterprise.
SAS’ sophisticated view of applying BA to data mining and related tasks shows in Enterprise Miner 5.1. The product sits on the distributed client/server architecture of SAS’ Enterprise Miner Server and can access the company’s add-ons to SAS9. The new version uses multithreaded execution to distribute its work over multiple processors and to perform simultaneous calculations on multiple models in the client.
Although Enterprise Miner is a data-mining application, it includes both standard BI tools and an expanding set of BA tools. Enterprise Miner aids analysts in modelling business processes from existing data and predicts what might happen based on particular questions.
The software supports the data-mining cycle in a thorough and, for the most part, elegant style. It starts with problem ana-lysis, asking the right questions and knowing what kinds of answers can be acted on.
The process continues with a work cycle SAS calls SEMMA (sample, explore, modify, model, assess), in which an analyst or statistician works directly with Enterprise Miner to deliver tested models that can be used as-is on the spot, be saved for reuse, or be exported for integration with other programs.
The Enterprise Miner client contains a set of tools for attaching to data sources. Through the client, analysts can tap external tools available in SAS9 or other existing mechanisms in order to prepare and clean data from data warehouses, data marts, or other significant databases.
SAS9 comes equipped with many native intrinsics and low-level programming methods for accessing and refining data.
Performing data analysis with Enterprise Miner is a straightforward process. After attaching to data sources, the analyst sets up a sampling routine, selects variables to target, and shapes data import by deciding how to deal with exceptions such as records that are missing values in specific fields.
I liked the way Enterprise Miner’s intelligence extended through the workflow and how smoothly the client supported changes.
The next phase is to build a set of tasks to design a model that answers questions by selecting component “nodes.” Nodes are dragged and dropped onto a diagram workspace, where the analyst connects them in a processing sequence.
When results have been obtained, the analyst can examine them graphically or as tables (or both) in order to gain insight into the processes.
Its program architecture enhances performance by speeding iterative work and expanding the questions and size of data that can be analyzed.
Angus is a contributing editor at InfoWorld.
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