Data Profiling: What You Should Know

 

Data profiling involves investigating data available from information sources like file and database and gathering informative summaries or statistics about it. Something like big data profiling can help you find out whether the already present data can be used for other uses. It also improves the capability to search information or data by tagging it with descriptions, keywords, or simply placing it in a given category.

Data profiling is actually a visual assessment, which applies a toolbox of organization’s rules as well as algorithms to understand, identify, and potentially reveal irregularities in your data. The know-how can help improve available datasets as well as systems as a perfect section of monitoring as well as enhancing the general condition of the bigger data sets.

The future of data profiling is bright. Companies must relate with increasingly diverse as well as intimidatingly big sets of information from sources like social media, blogs, and even emerging data technologies such as Hadoop. Data profiling can help you identify anomalies, assess data quality, register, discover, and also check on enterprise metadata. It takes place in different procedures.

How Data Profiling Is Performed

Data profiling utilizes techniques of descriptive statistics like maximum, mode, minimum, standard deviation, percentile, aggregates, frequency, and variation like count as well as the sum. It also makes use of additional metadata information acquired in the course of data profiling like data type, discrete values, length, typical string patterns, appearance of null values, and also abstract type recognition.

Metadata can be helpful when it comes to discovering problems like misspellings, varying value representation, duplicates, and missing values. Different analysis can be done for different structural levels. For instance, single columns can be profiled to get the know-how of the frequency distribution of varying level types, values, as well as the use of every column.

The already embedded value dependencies might be exposed within a cross-column analysis. Overlapping value sets representing important relationships among entities can be studied during inter-table analysis. In most cases, purpose-built tools are applied for data profiling to make the entire process easy. The complexity of the computation increases as you move from a single column to the cross-table structural profiling.

When Is Data Profiling Performed?

Data profiling is done several times. This is done with varying intensity in the course of the entire data warehouse developing procedure. A light profiling assessment ought to be undertaken after the targeted source systems have just been recognized and the DW/BI company requirements have actually been met. The importance of this early analysis is to find out whether accurate data is present at the correct detail status and that irregularities can be dealt with immediately.

In-depth profiling can be done before the dimensional modeling procedure to assess what is needed to change data into an appropriate dimensional model. Profiling can also move into the famous ETL system design procedure to know which data to get and the type of filters to apply to the available data set.

Data profiling can be performed within the warehouse development procedure immediately after the data or information has been put into staging and the data marts. Conducting data during these stages makes it clear that data cleaning, as well as transformations, have actually been done in the right manner and in accordance with the requirements.

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