What is data profiling?

Data profiling, or data archeology, is the process of reviewing and cleansing data to better understand how it’s structured and maintain data quality standards within an organization.

The main purpose is to gain insight into the quality of the data by using methods to review and summarize it, and then evaluating its condition. The work is typically performed by data engineers who will use a range of business rules and analytical algorithms.

Data profiling evaluates data based on factors such as accuracy, consistency, and timeliness to show if the data is lacking consistency or accuracy or has null values. A result could be something as simple as statistics, such as numbers or values in the form of a column, depending on the data set. Data profiling can be used for projects that involve data warehousing or business intelligence and is even more beneficial for big data. Data profiling can be an important precursor to data processing and data analytics.

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How does data profiling work?

Companies integrate software or applications to ensure data sets are prepared appropriately and can be used to the best of their advantage to remove bad data. Specifically, you can determine what sources have or are creating data quality issues, which ultimately affects your overall business operational and financial success. This process will also perform a necessary data quality assessment.

The first step of data profiling is gathering data sources and associated metadata for analysis, which can often lead to the discovery of foreign key relationships. The next steps that follow are meant to clean the data to ensure a unified structure and to eliminate duplication, among other things. Once the data has been cleaned, the data profiling software will return statistics to describe the data set and can include things such as the mean, minimum/maximum value, and frequency. Below, we will outline for you proper data profiling techniques.

Data profiling vs. data mining

While there is overlap with data mining , data profiling has a different goal in mind. What is the difference?

• Data profiling helps in the understanding of data and its characteristics, whereas data mining is the process of discovering patterns or trends by analyzing the data.

• Data profiling focuses on the collection of metadata and then using methods to analyze it to support data management .

• Data profiling, unlikely data mining, produces a summary of the data’s characteristics and enables use of the data.

In other words, data profiling is the first of the tools you use to ensure the data is accurate and there are no inaccuracies.

Types of data profiling

Data profiling should be an essential part of how an organization handles its data and companies should look at it as a key component of data cleaning. It not only can help you understand your data, it can also verify that your data is up to standard statistical measure. A team of analysts can approach data profiling in many different ways, but typically falls into three major categories with the same goal in mind which is to improve the quality of your data and gain a better understanding.

Here are the approaches analysts may use to profile your data:

• Structure discovery: This approach focuses on the format of the data and ensuring it is consistent all throughout the database. There are a number of different processes analysts might use for this type when examining the database. One is pattern matching, which can help you to understand format-specific information. An example of this is if you’re lining up phone numbers and one has a missing value. This is something that could be caught in structure discovery.
• Content discovery: This type is when you analyze data rows for errors or systemic issues. This process is a closer look at the individual elements of the database and can help you find incorrect values.
• Relationship discovery: This type entails finding out what data is in use and trying to find the connection between each set. In order to do this, analysts will begin with metadata analysis to figure out what the relationships are between data and then narrow down the connections between specific fields.

Benefits and challenges of data profiling

Generally speaking, there are little to no downfalls when profiling your data. It is one thing when you have a good amount of data, but the quality matters and that’s when data profiling comes into play. When you have standardized data that is precisely formatted it leaves little to no chance for there to be unhappy clients or miscommunication.

The challenges are mostly systemic in nature because if, for instance, your data is not all in one place it makes it very difficult to locate. But with the installment of certain data tools and applications it shouldn’t be an issue and can only benefit a company when it comes to decision-making. Let’s take a closer look at other key benefits and challenges.

Benefits

Data profiling can offer a high-level overview of data unlike any other tool. More specifically, you can expect:

• More Accurate Analytics: A complete data profiling will ensure better quality and more credible data. Properly profiling your data can help make better sense of the relationship between different data sets and sources, and help support data governance procedures.

• Keeps Information Centralized: By examining and analyzing your data through data profiling you can expect your data quality to be much higher and well-organized. The review of source data will eliminate errors and highlight the areas with the most issues. It will then produce insight and organization that centralizes your data in the best way possible.

Challenges

Data profiling challenges typically stem from the complexity of the work involved. More specifically, you can expect:

• Expensive and time-consuming: Data profiling can become very complex when trying to implement a successful program due in part because of the sheer volume of data being collected by a typical organization. This can become very expensive and a time-consuming task to hire trained experts to analyze the results and then make decisions without the correct tools.

• Inadequate resources: In order to start the data profiling process a company needs its data all in one place, which is often not the case. If the data lives across different departments and there isn’t a trained data professional in place it can become very difficult to data profile a company as a whole.