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In many scenarios, the Data Profiling task will be used at the beginning of a data warehousing project to evaluate the source data prior to implementing ETL (or data integration) processes for it When it is used this way, it can help identify problematic data in the sources For example, perhaps the source database contains a Customer table with a State column, which should represent the US state in which the customer resides After profiling that table, you may discover that there are more than 50 distinct values in the column, indicating that you have either some incorrect states, some states have been entered in both their abbreviated form and spelled out, or possibly both.

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Once the problem data has been identified, you can design and incorporate the appropriate business rules in the ETL process to exclude or cleanse that data as it moves to the data warehouse, ensuring a higher quality of information in the warehouse For the preceding example, this might be implemented by setting up a list of allowed values for the State column and matching incoming data against this list to validate it This upfront exploration of data and identification of problems is valuable, as it allows you to establish the rules that define what data can enter the data warehouse, and what data can be excluded, due to quality issues Allowing bad data to enter the data warehouse can have a number of effects, all of them bad.

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long-term investors On the other hand, sharp traders can still find opportunities no matter what the environment Although preferred by short-term traders, RSI can also be used to monitor overbought or oversold market conditions, moving between 0 and 100 RSI is an oscillator, a special type of indicator that fluctuates above and below a centerline Beneath the surface, RSI is based on complicated formulas, but it appears easy to use, primarily because it has only one signal line When RSI rises above 70, it s a signal the market or underlying stock is overbought The tricky part, as you remember, is that the market can remain overbought for extended time periods Some traders use an overbought reading as a potential danger signal Conversely, when the RSI drops below 30, it s a signal the market or the underlying stock is oversold It takes some experience to use RSI because it tends to generate a lot of signals If you want to master RSI (or its cousin, Stochastics), you ll need to do the following: 1 Experiment with the default settings 2 Observe RSI in different market conditions 3 Practice trading before committing real money based on its signals Once again, oscillators such as RSI tend to be used more with individual stocks for a short-term time frame, although it is adaptable to longer-term time periods For some market watchers, the long-term RSI signals are perhaps the most valuable Therefore, some traders change the default time period setting in RSI from daily to weekly to get a longer-term view of the market

For example, poor quality data in the warehouse will cause the users of the warehouse data to not trust it, or to potentially make bad decisions based on incorrect information These rules are typically set up front, and may not be updated frequently (if at all) due to the complexities of changing production ETL processes As data sources and business conditions change, these rules may become outdated A better solution is to base some of the business rules on the current profile of the data, so that they remain dynamic and adjustable, even as the source data changes..

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