BI and Data Management
Jun 05 2013
I hold a strong prejudice that IT paradigms are useful for about 30 years. The PC was dominant from 1980 to 2010, “online” mainframe systems from 1970 to 2000, and so on. If that’s the case then time’s up for Bill Inmon’s data warehousing framework.
May 22 2013
After deciding which mining model is best, you need details to make predictions actionable. Microsoft enables a couple of ways to access those details.
SQL Server Integration Services (SSIS) includes the Data Mining Query transformation which uses input data with an existing mining model to return a prediction. You can automate predictions by including a Data Mining Query transformation in a SSIS package and returning predictions in a regular data flow.
To make the prediction exporting process easier to understand, I will use SQL Server Data Tools to manually export a batch of selected prediction details.
May 21 2013
Organizations are aware of what big data has in store, but are not sure if this new technology adds any value to their specific organizational scenarios. Most are thinking "we do not do much with the social media data or web logs or have any real need to analyze such information within our business context". However, the cost effective storage and data discovery platforms that can give new insights are some things people are liking, but they are assuming these are something they may use in the future (and not now).
So, to really understand what could be the best place to start with big data is a great idea to explore.
May 19 2013
Once you have successfully processed your mining structure and mining models, it is time to view your results. To begin viewing those results, simply click the Mining Model Viewer tab.
Within the Mining Model Viewer tab, select the mining model you wish to review. Ideally you gave your mining models meaningful names so you can easily distinguish mining models from the names in the pull down list. The actual screen shown depends on the algorithm used by your selected mining model. For example, the screen print below shows the results of a Decision Tree mining model.
May 15 2013
Most people segregate the regular data away from big data in their thinking process. The structured relational or non-relational data is often thought of as the regular data and the data in web logs or files is thought of as big data. Traditionally, managers who are successful in last few decades embraced such thinking and reaped great career benefits. However, the new breed of managers have begun to look at the data assets quite differently as they evolve into formulating new strategic drives.
Conceptually, it may be true that big data assets have quite a few differences with the traditional data assets. However strategically, this isolated thinking is leading to segregating the data organizations into two silos. This can make long-term information strategy costly, ineffective and suboptimal for the organizations.