Consolidating data multiple sources books on on line dating
When loading data into SQL Server you have the option of using SQL Server Integration Services to handle more complex loading and data transforms then just doing a straight load such as using BCP.One problem that you may be faced with is that data is given to you in multiple files such as sales and sales orders, but the loading process requires you to join these flat files during the load instead of doing a preload and then later merging the data. SQL Server Integration Services (SSIS) offers a lot more features and options then DTS offered. With this task you can merge multiple input files into one process and handle this source data as if it was from one source.With this load process, let's assume the source data is not sorted first, so we need to use the SORT task to sort the data prior to using the MERGE JOIN task.The following shows our Flat File sources and then a SORT task after each one of these and then lastly our MERGE JOIN task.In converting to new EMR, the process involves taking data from a legacy system and doing the best you can to map and “fit” the data into a new system.It may sound simple, but it’s not as easy as connecting the pipes, turning on the water, and getting drinking water out the other end. What makes data harmonization in healthcare difficult is not the volume, variation, or other “V’s” of Big Data (although they do present their own unique challenges).
Recall any recent article about how healthcare prices are determined — the seeming lack of rhyme or reason — and you can easily see where inconsistency in practices leads to ambiguity of the concepts themselves.
But with M&A comes real-world data sharing and EHR conversion problems as it pertains to information systems and electronic health records.
Successfully merging data from multiple sources is, perhaps, one of the most misunderstood and consistently underestimated problems in health IT today.
Data harmonization in healthcare is difficult because of the high level of ambiguity and complexity in the data concepts themselves.
For example, patient demographic information can be merged fairly easily from one system to another.