Data Profiling: Definition, Techniques, Process, and Examples
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What is data profiling?
Data profiling is the systematic process of determining and recording the characteristics of data sets. We can also think of it as building a metadata catalog that summarizes the essential characteristics.
According to Gartner, this involves analyzing data sources and collecting metadata on the condition of data, so that the data steward can investigate the origin of data errors. As a result, you can discover and investigate data quality issues, such as duplication, lack of consistency, and lack of accuracy and completeness.
Data profiling, Kirk Borne from DataPrime says, is like “going on a first date with your data.”
You sit across from your computer screen, gazing at your data tooling, wondering if this new dataset is compatible with your project goals and whether there will be any red flags.
That’s where data profiling can help. We start our relationship with a new dataset by profiling it — dedicating some time to get to know it.
There are many tools available to profile data. However, it’s important to remember the end goal — build a useful overview of your dataset with the information you need to understand its origins and trajectory through your systems.
Data profiling is often confused with data mining or data cleansing. So, before we proceed, let’s explore the differences.
Data profiling vs. data mining
Although data profiling has some overlaps with data mining, the end goals are different.
Gartner defines data mining as the process of discovering meaningful correlations, patterns and trends by analyzing data. Meanwhile, data profiling helps in the understanding of data and its characteristics to ensure its completeness.
An MIT survey on data profiling highlights the difference between data profiling and data mining as follows:
Data profiling produces a summary of data characteristics, whereas data mining aims to discover useful but non-obvious insights from the data. So, while data profiling supports or enables the use of the data, data mining is the use of the data.
Data profiling vs. data cleansing
Data cleansing is the process of finding and dealing with problematic data points within a data set. It can include:
- Revisiting the original data sources for clarification
- Removing dubious records
- Deciding how to handle missing values
However, data cleansing is useful when you know which data must be checked.
According to Experian, bad data lurks in our databases undetected and unaddressed until we shine a spotlight on that data using a specific SQL query. That’s where data profiling comes in handy, as it can analyze vast quantities of data for completeness, inconsistencies, errors, anomalies, and more.
So, a thorough data profiling process usually reveals some aspects of our data that need fixing. That’s why you can think of data cleansing as a use case of data profiling.
Data profiling: Benefits & use cases
According to a SIGMOD research paper, in addition to data cleansing, data profiling has several use cases, such as:
- Query optimization
- Data integration
- Scientific data management
- Data analytics
- Project management
- Data discovery
Let’s explore each use case.
Data profiling provides information on the characteristics of a database, such as rows, columns, average values, and more. Statistics about each database can help you estimate the query design, considerations, and implementation plan. As a result, you can optimize your queries for better performance.
To integrate multiple datasets, we first need to understand the datasets and their relationships. This is crucial to understand how to link datasets, what’s the best way to link them, do you need to take into account different conventions such as name or unit of measurement, and so on.
Scientific data management
Before importing raw data into your databases, it’s important to understand the nature of that data. That’s where data profiling can help. After profiling these datasets, you can develop a plan to extract that data and adopt the appropriate schema.
Any analysis or data mining starts with data profiling. Data profiling gives an initial high-level understanding of the dataset and its characteristics so that you can choose the right algorithms.
For example, clustering algorithms might need values in numerical columns to fall in similar number ranges. You can use the statistical summary from profiling to check which columns must be transformed.
Taking data-driven decisions requires a solid understanding of the data you have, and the information you need for the project. With data profiling, you can take stock of your data, its quality, completeness, and credibility. You can also determine whether you have all the data you need to make your project work.
Having data available to be used broadly across an organization requires that data be easily accessible, searchable, and understandable. Data profiling can help by enabling you to compile the metadata needed, along with descriptive summaries and metrics for better context.
Data profiling in action: An example from the healthcare industry
Data profiling plays a critical role in ensuring data quality. In healthcare, data profiling can help improve the quality and accuracy of patient data. Bad data can lead to inefficiencies, patient frustration, and even patient mistreatment.
According to The Office of the National Coordinator for Health IT, healthcare organizations should profile their data to:
- Finalize the design of a master data store
- Assess the state of data
- Develop critical metrics and standards for data accuracy, credibility, and use
- Set up a new system for EHR, patient registration, billing, and more
- Connect to a health information exchange
How can you do data profiling: Techniques & approach
According to Felix Naumann’s “An Introduction to Data Profiling”, data profiling can be done using single or multiple fields.
Single field profiling is the most basic form of profiling that assumes all fields are of the same type and share common properties. This type of profiling helps you discover:
- Summary statistics: This includes count of data and mathematical aggregations such as maximum, minimum, and mean values.
- Data types: This involves determining whether the data is categorical, continuous, and exhibits any patterns. Simple data types include strings, numbers, and timestamps, whereas more complex types include XML and JSON.
- Data values: This means identifying the characteristics and patterns in data values. Examples include address fields, cities, ID strings, and more. Profiling data values also helps you assess your data against known business rules. For instance, if you know that only students from South America can apply for a specific category of funding, you can check for that condition in your data.
- Distributions: Visualizing data distribution is useful in spotting outliers. For categorical data, you can see counts per category. Meanwhile, for numerical data, you can plot histograms and note characteristics like skewness, the number of modes, and presence of outliers.
Meanwhile, multi-field profiling explores the relationship between fields to discover:
- Inclusion dependencies, keys, and functional dependencies: With profiling, you can find out if the values in one field are a subset of values in other fields. This helps you add dimensions to your data set.
- Visualize numerical relationships: Profiling helps explore the relationships between numerical fields using pair plots, cross-correlation heat maps, or tables of correlations between fields. These visualizations provide a quick overview of the relationships each data set has with other assets.
When should data profiling happen?
Ralph Kimball argues that data profiling should happen right at the beginning of a data project. This can help you evaluate whether the project is viable. You get an early view of the data and a taste of the problems that may occur further into a project.
It also helps you find dirty data and analyze data quality so that you set up proper processes at the beginning. Catching problems early can lead to significant savings in time and more robust data projects.
Here are some of the other benefits of profiling data at the beginning:
- Spot and map data transformations and quality improvement
- Identify unexpected behavior that needs addressing at a business process or ETL system design level
- Predict potential ETL issues and set up contingencies if needed
Kimball also highlights how “data profiling makes the implementation team look like they know what they’re doing. By correctly anticipating the difficult data quality issues of a project upfront, the team avoids the embarrassing surprise of discovering big problems near the end of the project.”
Data profiling is a critically important step in any data management or analytics project. So, it should come at the beginning so that you can provide an accurate project timeline estimate, ensure the availability of high-quality data, and enable data-driven decisions.
As Kirk Borne highlights, data profiling is the best path to “knowing thy data”. To know more about data management, check out this article on the four key things to grasp.
Data profiling: Related reads
- What is data observability: definition, importance, framework & benefits
- Data Catalog: Does Your Business Really Need One?
- Data observability vs. data monitoring: how are they different?
- What is data integrity? Definition, importance, and best practices
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