What Is Data Profiling? Definition, Uses and Best Practices

There are certain data quality metrics to pay extra attention to in your data profiling process. They include:

• Completeness: Does your existing data have any blank or null values? Is there any missing or unknown data?
• Format: Does data you gathered conform to your requirements? Is it formatted correctly?
• Consistency: Can you make reliable conclusions using your data set?
• Duplication: Does your data set contain too many duplicates?
• Accuracy: Is your audience data factual and up-to-date? Is there any poorly structured data?
• Integrity: Is your data linked to relevant information? Is it gathered in a timely manner?

Data profiling allows you to understand and organize your data. It tells you whether the data you have is suitable for further investigation and prepares it for data processing and data reporting and analytics. All in all, data profiling minimizes the risks and inaccuracies in your data projects and helps your business make critical decisions that can impact its success.

Advantages of data profiling

No matter how big or small your business is, growth starts with the data you gather. Here are the 4 main benefits of data profiling:

High-quality data

Data profiling eliminates the bad data from your data warehouse, whether it is duplicated or simply irrelevant. No matter how many data sources you gather from, data profiling helps you select the right information to draw conclusions from so you can be confident in your decisions.

Organized and easily searchable data sets

Tagging and categorizing data is a crucial component of data profiling that aids in the process of data management. It gives your data engineers a clear overview of your data sets, so they have an easier time searching for quality data with keywords, discovering patterns, and developing a data strategy.

Error prevention

Data profiling allows you to identify issues early and correct them before they become a bigger problem down the line. Any missing data and poorly structured data values are discarded before they become a part of your data analytics, stopping them from skewing your results. It also helps you improve and streamline your data warehousing process.

Informed, data-driven decisions

Improving data quality through data profiling allows you to make judgments based on empirical evidence. High data quality, especially in terms of well-formatted and consistent data, also gives you the option of employing machine learning and artificial intelligence analytical algorithms to make predictive decisions.

Challenges of data profiling

While a data quality assessment is a key part of any data initiative, there are a few considerations to keep in mind, including:

Computational logistics

In addition to ample time and a proficient data profiler, your business’ data profiling capabilities also relies on the performance of your computing system. A lot of memory and disk space is needed to undertake a large-scale profiling project, which can be costly.

Difficulty of dynamic data profiling

Datasets change from time to time and need to be reexamined in order to be useful again. Is it possible to update the results and improve data quality without looking over entire data sets over and over again?

Statistical anomalies

If a piece of qualitative or numerical data shows up 3 or 4 times, it may be a duplicate, but if it appears more than 10 or 15 times, is it statistically significant? How do you determine whether it should be included in your analysis? How do you keep your data integrity?

Types of data profiling

Structure discovery

Structure discovery is all about consistency and format. For example, for a group of phone number inputs, you may want to check if any of them contain symbols or letters rather than just numbers.

Structure discovery also employs basic statistical analysis to gather information such as standard deviation, mean, and mode. This can help you notice patterns and correct issues.

Content discovery

Content discovery looks for errors in individual data records. This type of data profiling catches data quality issues such as missing values and ambiguous information.

Content discovery is an important check to pass when you manage your data, especially when you're dealing with data fields that require accuracy. An address, for example, is not complete unless it has a corresponding zip code. Abbreviations, such as using "St." for "Street" and "WI" for "Wisconsin," may also affect mail carrier systems. While these issues may seem innocuous, they make all the difference.

Relationship discovery

Relationship discovery catalogs the connections between different data sets, including similarities and differences. Sometimes, two data sets need to be combined to create value. For example, a customer's name needs to be matched with their correct address in order to ensure product delivery. Relationship discovery is also integral for sampling, duplicating, and transferring data so data integrity can be maintained.

Data profiling tools and techniques

There are a variety of data profiling tools and techniques available to enhance data quality and solve data quality problems. Here are some data profiling techniques to get you started:

• Column profiling: First and foremost, column profiling conducts frequency analysis. It counts the number of times a value appears in a single column. Then, it uses this information to uncover patterns and produce statistics. For numerical columns, minimum value, mean, and standard deviation are typically calculated.
• Cross-column profiling: Cross-column profiling deals with key analysis and dependency analysis. Key analysis looks for primary keys that identify each data set, such as customer name, product number, or license plate number. Dependency analysis searches for connections within a data set.
• Cross-table profiling: Cross-table profiling is more complex. It analyzes multiple columns across different tables in order to locate broader relationships and dependencies. Stray data and discrepancies are often discovered in this process.
• Data rule validation: Data rule validation notes where data quality can be improved by checking the data you collected against certain established standards.

Now that you understand the basic techniques for profiling, let's take a look at a few data profiling tools:

• Informatica Data Quality: A data profiling tool that allows you to automate your data quality assessment, Informatica Data Quality finds relationships and flags problems within your data and supports the transformation of data with standardization, validation, enrichment, and more.
• Aggregate Profiler: Aggregate Profiler is an open source data quality and data profiling tool. It is an open source tool that supports data generation, data preparation and data masking. It also features real time alerts for data issues and changes.
• Oracle Enterprise Data Quality: This tool is integrated with Oracle Master Data Management and provides data profiling, auditing, cleansing, and matching for a range of data types such as customer, product, financial, and operational data.

Get the most out of your data

Get the most out of your data with data profiling. Ensure the best data quality, so you can make data-driven decisions that take your business to the next level.

Working with data can be daunting, but Mailchimp is here to help. Check out our Marketing Library for more resources on how to use data to forecast, plan, and track your company’s performance and success, including Google Analytics tutorials and how to protect your customers’ data.

Need more ways to format and simplify your raw data? Take a look at our data reporting best practices. Let Mailchimp guide you in improving every step of your customer journey, from prospect to purchase.