What is Data Profiling?
Data profiling is a process that identifies and describes the statistical distribution of data in an organization’s databases. It can be used to do things like determine the quality of data, find changes in business rules, and improve the performance of applications that access databases.
Data Profiling is analyzing a data asset to provide statistical results about the data. It is a summary snapshot of the shape of the data including information about completeness, distribution, patterns, type, and duplication of the data. Organizations use this summary view to better understand the data structure, describe its value and confirm that it is good to use, or identify any anomalies and issues.
Analyzing a column in a table is a fast way to answer questions like:
- How many rows are in this column?
- Is the data type text or numeric?
- What is the average length of the entries in the column?
- What is the min / max value found in the column?
- What percentage of the rows in this column are empty?
Data profiling can be a challenge because different technologies require different tools or methods to get summary views. Analysts may need to write SQL queries to find the stats and properties they need to understand their data. An automated solution will provide a quick way to get the insight that data stakeholders need to govern and use data.
Types of Data Profiling
Data profiling is the process of examining data in a database to get an idea of what the data looks like to determine if it is fit for use. The two main types of data profiling are structure profiling and content profiling.
Structure profiling examines values contained in fields, while content profiling examines the contents of files that contain unstructured data such as documents, images and videos.
Structure profiling is used to determine the structure of a database, including whether it’s in the right format and has the right attributes (such as names, addresses, and phone numbers). Content profiling is used to determine what information is actually stored in each field within a database table, such as “John Smith” or “123 Main St.”
Three Categories of Data Quality Profiling
The three main categories of data quality metrics are validity, uniqueness and completeness.
- Validity is the degree to which data conforms to a standard. For example, it may be required that all dates be stored as YYYY-MM-DD format; if some records have dates like “08/02/94”, then this would be an example of invalid data.
- Uniqueness refers to the characteristic of data that makes it distinct or different from other data. It is a property of data that ensures that each data point in a dataset is unique and has a distinguishable value.
- Completeness refers specifically to whether or not all relevant attributes for each record exist within its corresponding table(s). This metric helps ensure that there aren’t any missing values within your organization’s datasets so that all key pieces of information needed for analysis will always be available when needed!
Benefits of Data Profiling
Data profiling is a way to catch errors before they happen. As more work is automated by data lakes, more errors will be caught automatically in many cases. But this means that you need to do ongoing data profiling so that you can identify what’s working and what isn’t.
Data profiling can be done on demand or scheduled, in real time or on a batch basis (for example, once per month). It can also be done manually or automatically–the latter approach being preferable because it’s faster and more accurate than human-driven analysis alone.
- Regularly profiling your enterprise data enables data stewards to catch anomalies in real time as business rules change. This prevents errors that would otherwise have to be fixed later in the analytics process.
- Determine whether you have enough data available for your particular purpose, which is especially useful when you’re trying to figure out which sources will provide good information about certain topics or people (like consumers).
- Analysts can confirm that the data that they find is what they want to use in their analysis.
- Create a single definition for each piece of information so everyone on a team uses the same definition for it every time. This reduces confusion and increases productivity for everyone who works with that information.
- IT teams can understand if they are managing duplicate copies of data that could be deleted
Data Profiling Examples
It’s important to understand that data profiling is not just about creating definitions for your tables, columns and fields; it’s also about creating definitions for the information that you store in those tables, columns and fields (aka “data”). When you do this properly, you can use these definitions later when you need them–for example:
- When someone needs to know what kind of data they should enter into a particular field on a form or report (e.g., “Is this email address valid?”)
- When someone needs to know which reports should be run against certain datasets because they contain interesting pieces of information (e.g., “Which customers bought product X last month?”
Data Profiling in Cloud Environments
Organizations are adopting cloud technologies for increased analysis and collaboration, so it is critical to enable the analysts with high quality data since more stakeholders will use, share, and make business decisions from the data. Data profiling can surface anomalies that need to be addressed for data quality. Managing data quality to communicate preferred data sources is essential.
A cloud platform administrator can use data profiling to help determine what datasets to upload to a cloud environment. Once data is in the cloud, data analysts use it to choose which datasets to use for analysis and collaboration, while owners and data stewards will use it to select which datasets to certify and which datasets to archive.
Data Profiling Best Practices for Data Quality
Decision making based on poor-quality data creates significant risk and carries high financial, productivity, and reputational costs. Organizations are defining new data quality policies to specify the required levels of validity, completeness, currency and accuracy for information to maximize value and minimize risk to the enterprise.
A best practice is to surface anomalies that need to be addressed for data quality. Automated data profiling enables organizations to keep a current view of their data and proactively address any data quality issues before they create significant negative business impact. The best way to make sure you’re using your company’s data correctly is by taking an inventory of what you have and making sure everyone understands definitions for each piece of information
Reduce Risk and Increase Data Trust with BigID
Data profiling is a critical part of any organization’s data management strategy. It gives you a way to make sure everyone understands how your company uses its data, which helps reduce confusion and increase productivity for everyone who works with that information. Data profiling is also useful for finding anomalies in real time as business rules change, which prevents errors from being fixed later on in the analytics process.
BigID provides automated data profiling that eliminates the need to write manual queries. Included in BigID’s data intelligence platform, the data catalog can profile columns in tables across all data sources. With a single click, data teams can profile data by column and take action with BigID apps to address data quality or contact the data owner.
Capabilities for Data Profiling with BigID:
- Profile high-value, sensitive, personal, and regulated data to protect critical data
- Automate manual tasks to gain a summary profile view of data
- Operationalize data governance strategies by focusing on data anomalies
- Provide insight for stakeholders to trust the data
- Identify duplicate and redundant data
- Find and remediate inaccurate data
Schedule a demo to learn more about how BigID can help you with your data profiling challenges.