As the data is gathered the students normally spread their sampling population far and wide to get statistics on as many units as possible. They are thus found grappling with a large volume, velocity and complexity of data in their records.
Researchers have a habit of collecting data from blogs, public sources and 3rd party aggregators along with other traditional sources. Not all the records that they gather will be of use to them. In fact some of them might throw the analysis off course.
Time series analysis is a good method of getting the information that is most essential. This is a useful method of analytical work when it comes to detecting breaking trends, market changes and issues related to performance.
It is essential the researchers learn to identify unique trends among their records. How do they know if a trend is problematic or just part of the usual trends? If there is a drop in sales numbers for a particular region, should that be taken as unique or is there a drop in sales numbers everywhere. Is the drop in sales numbers only for a particular period, or is there a drop in sales numbers throughout the year.
One way of filtering out unwanted character sets is to identify the reasons for the unusual breaks in trends. Once the reasons have been identified then it is easy to analyse if the records should be kept as part of the data or should they be removed. This way the data that the students will be left with will be perfect for accurate analysis.