What is data profiling and how does big data make it easier?


Why do you need data profiling?

Data profiling helps you find, understand and organize your data. It must be an essential part of how your organization handles its data for several reasons.

First, data profiling helps to cover the basics of your data by making sure the information in your tables matches the descriptions. Then it can help you better understand your data by revealing the conditions that span different databases, source applications or tables.

In addition to simply revealing hidden nuggets of information buried in your own data, data profiling helps you ensure your data is up to standard statistical goals as well as business rules specific to your business. For example, a state column might use a combination of both two-letter codes and the spelled (sometimes incorrect) name of the state. Profiling would uncover this inconsistency and inform the creation of a standardization rule that could make them all consecutive two-letter codes.

What are the different types of data profiling?

Many of the data profiling techniques or processes used today fall into three main categories: discovery of structure, discovery of content, and discovery of relationships.

Discovery of structure, also known as structural analysis, validates that the data you have is consistent and formatted correctly. There are several different processes that you can use for this, such as pattern matching. For example, if you have a dataset with phone numbers, pattern matching helps you find the valid format sets in the dataset. Pattern matching also helps you understand whether a field is text or number based along with other format specific information.

Structure discovery also examines simple basic statistics in the data. By using statistics such as minimum and maximum values, mean, medians, conditions and standard deviations, you can gain insight into the validity of the data.

content Discovery is the process of looking more closely at the individual elements of the database. This can help you find areas that contain zero values ​​or values ​​that are incorrect or ambiguous.

Many data management tasks start with posting all the inconsistent and ambiguous records in your data sets. The standardization process in content discovery plays an important role in solving these small problems. For example, finding and correcting your data to fit street addresses in the right format is an important part of this step. The potential problems that can arise from non-standard data, such as being unable to reach customers via mail, because the data set includes incorrectly formatted addresses, are expensive and can be addressed early in the data management process.

Finally relationship discovery It involves finding out what data is in use and trying to get a better understanding of the connections between the data sets. This process starts with metadata analysis to determine key relationships between the data and narrow the links between specific fields, especially where the data overlaps. This process can help cut down on some of the issues that arise when data sets are not adjusted.



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