Home Technology A Simple Guide To Data Profiling For Businesses

A Simple Guide To Data Profiling For Businesses

Data is the new currency and businesses are constantly looking to extract more value out of their information assets. A comprehensive analysis cannot be conducted without an effective data profiling program. Since organizations are generating varied and large volumes of data at great speed, they need profiling to ascertain the quality of different elements and recognize their potential for fulfilling business requirements.

Organizations depend upon information management to understand their present state and make future projections. You can hire the best data management services to design your program but it will not be productive unless you ensure the supply of consistent and standardized elements. Profiling has become necessary for finding answers to key questions surrounding your information’s quality. Let’s dive into the topic to understand this practice and its importance for organizations.

What Is Data Profiling?

Data profiling involves the assessment of the source data and the collection of all additional information about the element. This practice is helpful in not only creating accurate metadata of all elements but also in assessing their quality. Profiling is helpful in understanding the structure of an information element apart from knowing its exact content. The exercise helps in establishing relationships between various elements and ascertaining their usability in different data-related projects. It also plays a key role in identifying anomalies and inconsistencies and is helpful in resolving data quality issues.

Why Is Data Profiling Necessary?

A question that now arises is why is the practice needed? Most entrepreneurs feel that their information management systems will be capable of handling data quality issues. Spotting an error after an item has been a part of data-related processes is not efficient. You need a mechanism that establishes the veracity of an element right at the beginning. Profiling is necessary because of the following reasons:

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1. Assess The Completeness Of Data

Organizations need information which is complete, in order to extract value from it. Profiling helps in making a thorough assessment of the completeness of an element. It helps in spotting blank or null values in the items. In case, you are able to spot any gaps right at the outset, you can take the necessary steps to fill them.

2. Establish The Uniqueness Of Data Elements

As mentioned before, organizations are generating information at a great pace. This can lead to a situation where the same element is entering their digital ecosystem from different points. Data profiling can be helpful in establishing the uniqueness of various items. It can find out the number of distinct values in an element and identify its duplicate nature.

3. Discover Relationships Between Various Elements

Most of the information assets in an organization are linked to each other. Discovering the relationships between various assets enables the enterprise to use its information in an optimized manner. Profiling gives you a complete idea of the composition of an item along with its anomalies. It also helps in uncovering the inter-relationships between various elements.

4. Spot Inconsistencies And Maintain Data Quality

Since profiling involves getting a complete insight into your information assets, it provides you with an effective way to spot inconsistencies. Businesses can use the practice to identify errors in their elements and take the necessary steps to eliminate them. This is helpful in maintaining Successful Data Management Plan.

Where Can Data Profiling Be Applied?

The practice can be applied to various existing projects to improve their efficiency. Following are some of the situations where profiling can prove to be helpful:

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1. Business Intelligence Or Data Warehousing Programs

Business intelligence or data warehousing programs involve the collection of data from various diverse sources. Applying profiling techniques can be helpful in spotting errors and removing them at the source. This will be helpful in improving data quality.

2. Source Data Quality Projects

The most important use of the technique is in maintaining data quality at various sources. This helps in not only resolving any existing issues but also in making sure that the same anomalies do not appear in the future.

3. Data Migration Initiatives

Another important situation where the application of the technique pays rich dividends is data migration projects. When your information elements are being moved from legacy systems to a new system. It can help in spotting issues that can be rectified by making coding modifications during the transfer.

What Are The Different Types Of Data Profiling?

Following are the two main profiling types:

1. Structure Discovery

This involves performing mathematical validations to make sure information is formatted correctly. The most common technique used to achieve the goal is pattern matching. It helps in unearthing anomalies in a given data set.

2. Content Discovery

This kind of profiling involves scrutinizing each individual element to find errors. It is usually conducted after structure discovery and helps in knowing the exact location in a data set where the anomaly is occurring.


Data profiling is an integral part of all data quality initiatives. The practice helps businesses understand and enrich their information assets. It also plays a key role in standardizing information elements as well as their sources.

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About Author:

Sophia Dicosta is a renowned Data Manager by profession with hobbies of innovative. She works with leading Data Management Consulting Companies – EWSolutions Ltd. Feel free to get in touch with her.


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