Sep 20, 2013
As database professionals, we typically work in a field of exact science. For example, a common practice in business intelligence (BI) solutions is creating duplicate copies of data sets, then comparing the results from the different sources to make sure they’re the same. If you extract five years’ worth of data from an application’s database and put it into a data mart, the results in the data mart must be the same as the results in the application’s database, even if the table structures were changed and older records were archived. You might build a cube or semantic model and again check to make sure the results are exactly the same as the source system. If the numbers don’t add up, the results are rejected because you know that something is wrong and must be corrected. I have to confess that not getting a conclusive result when working on a tough data problem sometimes keeps me up at night. Data mining is a database technology that, by its very nature, might not yield conclusive, reliable results. I’m going to show you that taking the time to analyse the anomalies can be extremely useful and valuable in business applications. By putting aside the prerequisite of always needing to have a conclusive, correct answer, you might find some golden nuggets amid those uncertain predictions. That outcome is the nature of data mining. The below article will introduce you to the Data Mining Model Designer in SSDT and the data mining add-ins for Excel 2013, and will explain the six steps to getting started with data mining: Step 1: Create a New Data Mining Project Step 2: Prepare the Data Step 3: Add the Data Source to the Project Step 4: Add the Views Step 5: Create a Data Mining Structure Step 6: Process and Explore the Mining Structure Read on… Turley, P 2013, ‘Getting Started with Data Mining in SQL Server’, SQL Server Pro, weblog post, 17 Sep, accessed 20 Sep 2013, < http://sqlmag.com/sql-server/sql-server-data-tools-data-mining-model-designer>.How do your Excel skills stack up?
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