What Is Data Profiling?: Making Sure Your Data is Definitive

The data profiling process is your secret weapon in your fight for good data. It is the process of evaluating the content and quality of data. The goal of data profiling is to determine how accurate, complete, and valid the dataset is. A data wrangler’s understanding of an overall dataset is informed by the statistical analysis of values for logic and consistency, otherwise known as data profiling. Data profiling tools evaluate quality by exploring frequency distributions of different values both within and across tables or columns. With good data profiling, the implementation cycle for a project is made shorter and discovering business intelligence embedded deep within the data is possible. Learn about the types and benefits of data profiling and how to improve your data profiling abilities. 

Types of Data Profiling Processes

There are three main types of data profiling that most analysts use. These different types of data profiling influence the data profiling tools used and the outcomes of the process. 

  1. Structure profiling. This type of data profiling focuses on discovering the structure of the dataset and determining if the data is validly and consistently organized. When doing structural discovery, analysts will also be able to focus on any missing values and solving similar problems. 
  2. Content profiling. This type of data profiling focuses on the data itself. The analyst will look at individual data records and determine if the data contains errors or other systematic issues.
  3. Relationship profiling. This type of data profiling focuses on the relationships between data. For example, an analyst can look at the relationship between all of the tables in a dataset. This process helps make it possible to reuse data because the relationships are clearly established.



The Importance of Data Profiling

The process of data profiling is important in the analysis process because it answers key questions about the status of the data. Analysts need to answer these questions to determine if a dataset is ready to be analyzed and used. Here are some of the key questions that data profiling can help answer about a dataset: 

  1. Is the dataset complete? Are there null values or blank rows? 
  2. Is each record unique, or are there duplicates? 
  3. Are there patterns in the data? Are the patterns the ones anticipated? 
  4. What is the range of the values? 
  5. Are the minimums, maximums, and averages what was expected? 

Once analysts have answered these questions, they are more prepared to begin analyzing datasets. The key is finding good data profiling tools to help the process be more efficient and effective. But not all data profiling tools are created equally.

Up Your Data Profiling Game with Trifacta

Trifacta’s interactive interface was built with powerful data profiling features in mind: data is presented in the most visually compelling representations based on the inferred data type. In fact, every profile in Trifacta is completely interactive—users simply select certain elements of the profile to explore hands-on. When it comes to data profiling tools, these interactive profiles and powerful features make Trifacta an effective and efficient choice. For ease of data profiling, Trifacta automatically identifies dataset formats, schemas, specific attributes and relationships across attributes and datasets, along with associated metadata for each dataset. These are crucial features for data profiling tools to be able to do to perform each of the three types of data profiling

These visual representations of data enable quick surfacing of patterns or problems, as well as actionable insights throughout the life of your data project. Beyond just identification, pattern profiling alerts users as to common and anomalous formatting patterns within each data type. Trifacta will also suggest script transforms for irregular or incongruent data, eventually automating that process.

What Trifacta Gives the Data Analyst

Additionally, the Trifacta data quality bar is a valuable asset to your data profiling efforts. When you pull your subset of data to be profiled, Trifacta’s Results Summary page and data quality bar make profiling data easier by giving data analysts the information they need:

  1. Core statistics such as the dataset’s size, distribution, quality, distinct values, median, mean, quartiles, average, and standard deviation, to name a few.
  2. Percentage of valid, mismatched, and empty values in your results file.
  3. The size of the results file, separated by file format.
  4. Number of columns in your results file.
  5. Number of rows in your results file.
  6. The ID of your result file.
  7. The data source that was used to generate your results file.

A data analyst’s workload is dramatically lessened by Trifacta’s uniquely interactive and predictive profiling functions. Now, analysts can not only easily spot empty or mismatched values, but with just a few clicks, they can build a transform script to clean their entire dataset. No programming needed. And when the script is executed, Trifacta generates a visual profile of the entire dataset as part of the job. In short, data cleansing and normalizing data with Trifacta has never been easier.

To learn more about Trifacta’s data profiling capabilities schedule a demo