When one is presented with any type of data, the use of filtering is a common practice for turning their data into information. Data itself is simply a collection of letters and numbers, but when data is manipulated, or in this case filtered, it can become a useful piece of information.To determine the appropriate way to filter your data you must first define what it is that you are looking for. The best way to do this is to form a question such as, “I want to know `X` that meets condition `Y` “. Once a question is formed you have essentially created the components that will make up your filter criteria. You have established what conditions you are searching for and which conditions need to be excluded from your final results. At this point you can now begin the manipulation process that will filter the data and provide you with your information.
Let’s look at an example to see how we can apply the steps we have discussed. Suppose you are presented with a dataset that lists all the members of a local gym and the membership data corresponding to each member.
You would first form your question, “I would like to know who has been a member of this gym for over a year”.
Instead of drudging through every member record manually, your most efficient way to answer the question is to use the process of filtering your data. You could easily create a conditional statement on the field stating the member’s join date and filter out all the records of members who have joined within the last year (as these members would have membership tenures of less than one year).
Now you are left with a useful piece of information that has answered your question and gotten rid of all member records who didn’t fall within your
question’s parameters.
While this is a very simple example and an overall general description of data filtering, the core principle should remain whenever one needs to filter data. This principle can be identified by two main parts:
1)
Clearly define your question by determining the characteristics you want to find in your data and excluding the characteristics you do not want
2)
Create conditional statements on your data that will answer your question and provide you with the information needed
Whether your data happens to come in a spreadsheet, database, or some other tabular format, sticking to this principle will provide you with a guide to make the most of your data.
