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Data Profiling is about understanding your data. It is an essential first step for any data project: validating your data at the analysis stage  beats by dr dre monster solo before it causes real problems for your project.
In this article, we're only going to look at one particular aspect of Data Profiling, namely the analysis of attribute patterns, or format(s) of the data contained in a particular attribute. For example, a phone number attribute might contain data which could be represented by 999-9999-9999. Of course, this is unlikely as there are several formats for area code, mobile and international numbers.
Other examples include attributes like account numbers, social security numbers, zip codes etc.
The questions you're looking to answer include:
How much data do we have? How many rows of data are in the dataset?
How many Nulls or missing values are there for the attribute in question? If there are very few Nulls is this an error? If there are a high number of Nulls is there some implied value; does a Null  big beats dr dre mean something?
What are the patterns and their volumes? This is best represented as a frequency distribution of patterns.
If there are a very large number of patterns which represent  monster beats by dre white an attribute then may be there is little meaningful analysis that can be applied - we might assume these are memo, or note, fields.
Do the patterns conform to any expected standards (e.g. for ZIP codes, phone numbers or sort codes)?
Just because the values conform to the expected pattern, does not mean they are valid however. Classic cases include phone numbers and email addresses, which may appear valid, but cannot be verified without some external checks.
It is also worth pointing out that many systems built for one country may have been pressed in to service to support international businesses. Address formats, social security numbers and such like will all prove more challenging in such scenarios.
As already mentioned, even within the same country, phone numbers may have multiple formats depending on how area codes are entered or if international dialling codes are allowed.
Are there any outliers; patterns which  beats solo dr dre appear only a handful of times?
Are there any  beats dr dre solo white patterns used infrequently which look very similar to popular patterns? A classic case may be a numeric code, which appears to contain alpha characters. Often this is down to zeros and "O"s or ones and "l"'s being incorrectly input.
Data Profiling is easy and cost-effective. No matter what your data source, take a look at it today. I guarantee that you will find something worthy of further investigation.
Citrus Technology provide Data Profiling and Data Quality tools to help you understand your data; to find patterns, issues and opportunities. Visit our website for a free white paper  dr beats dr dre on Data Profiling and a free trial of our software.
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