An often overlooked first step in maximizing your data quality is data profiling. Data profiling is valuable in improving your ability to use and analyze your data to guide decision-making.
You can’t cook a recipe if your ingredients are all scattered in different areas, difficult to find, and not prepared. Your first step to cooking should always be to have your ingredients in order. Similarly, data profiling is the first step to using your data for the right purpose and simplifying your data strategy down the line.
What is data profiling?
Data profiling is the process of sorting, cleansing, and analyzing data to obtain a clear and accurate overview of your data. Before the data profiling process, data is harder to analyze and use appropriately. The data profiling process involves:
- Monitoring data
- Identifying errors
- Properly formatting information
- Sorting data
Ultimately, data profiling helps your organization cleanse and organize your data to ensure that it can be easily understood and used to guide decision-making. Profiling your data helps to highlight quality issues, group-related data, and identify trends.
Data profiling is often confused with data mining. However, there are some key considerations to keep in mind when comparing data profiling vs data mining. Profiling focuses more on maintaining the quality and consistency of the data and preparing it for use. Meanwhile, data mining focuses more on extracting valuable information from the dataset, like mathematical values and trends.
What are the benefits of data profiling?
Data profiling plays an important part in organizing, understanding, and interpreting your organization’s data. Better organization of your data is extremely valuable for achieving your business goals. Benefits of data profiling include:
- Improved data quality
- Better data accuracy
- Better insight for business decisions
- Saved time
- Easier-to-find data
- Fewer errors
When your data quality management strategy incorporates data profiling on a regular basis, you can maximize the usability of your data. Data profiling helps to highlight and erase data quality issues to ensure that your data is consistent and easy to interpret.
By addressing errors before they become a problem, you can avoid spending unnecessary money by preventing situations like returned mail and bounced emails. In doing so, you also preserve valuable time and resources by ensuring resources are allocated appropriately and that staff is not spending energy on sending communications to the wrong email or address.
Altogether, your organization sees improved efficiency and saves valuable time. By highlighting errors early, data profiling saves your staff time correcting them down the road. By grouping data together, data profiling makes it easier to find data and identify trends, saving your team the time doing so. When done properly, data profiling is highly valuable.
What are the types of data profiling?
Data profiling is not a one-size fits all process. In fact, there are different approaches to data profiling depending on the needs of your organization. There are three important categories of data profiling.
Structure discovery
Structure discovery focuses primarily on validating your data and ensuring that it is formatted correctly and consistently. An example of structure discovery in action includes ensuring that a list of email addresses are formatted properly with the domain properly entered.
This process may also be called structure analysis as it provides valuable information about your data, depending on the type of data you are working with. Analysis data may include basic statistical values like mean, median, mode, and standard deviation.
Content discovery
Content discovery is targeted toward ensuring data quality and focusing on the accuracy of the data in your database. This process targets individual pieces of data and identifies issues. Errors highlighted can include an address with no zip code or a phone number that is missing an area code.
With content discovery, you are able to minimize the chances of issues that can arise from poor data quality, like sending information to an incorrect address or receiving bounced emails due to incorrect email addresses.
Relationship discovery
Just as one may guess based on the name, relationship discovery focuses on finding relationships and connections between your data sets. This process may highlight relationships between data elements or cells in a single database or highlight connections across multiple databases.
Relationship discovery is valuable as related data should remain together to highlight trends and identify patterns. These patterns help to improve analysis and direct further business moves and communications. For example, customer contact information and order history are closely related and can guide future communication with that customer.
Best practices for implementing data profiling?
Properly implementing data profiling into your data quality management strategy requires a consistent commitment to the process. One of the first data profiling steps is cleansing your data to reduce errors and erase inconsistencies. It’s important to note that data profiling and data cleansing are not one-time processes and should be carried out on a regular basis.
In order to carry out the process effectively, there are several data profiling techniques that you may use. You can carry out data profiling using one of three methods:
- Column profiling- This method highlights how often each value appears in a table, to identify frequency distribution and potential patterns.
- Cross-column profiling- Cross-column profiling involves two important processes. These are key analysis and dependency analysis. Key analysis scans through an entire collection of values in a table to identify a primary key. Dependency analysis, on the other hand, finds dependent relationships within a dataset.
- Cross-table profiling- Through this method, one can identify foreign keys that link data. This approach helps sort data based on similarities and differences while also highlighting potential redundancies.
To maximize the effectiveness of your data profiling, you should incorporate data profiling tools into your data management strategy. Performing data profiling without tools is tedious and time-consuming. The right data quality tools can help simplify the data profiling process and maximize its success by regularly cleansing your data and ensuring accuracy.
Incorporate data profiling tools in your management strategy
Altogether, data profiling is a vital part of a strong data quality management strategy, ensuring accuracy and maximizing efficiency in your organization’s data management. With an entire catalog of tools geared toward improving data quality, Experian is a valuable partner along the way.
At Experian, we support your data management strategy from data cleansing to data enrichment. To learn more about data profiling tools and how they can benefit your organization, contact Experian today.
