Data Profiling: Step One to Ensuring Big Data Quality
“Data is valuable!” headlines scream. “Your information can be a goldmine!” Yet, that’s only true if you’ve got high data quality.
How can you achieve high levels of data quality? The answer lies in data profiling. Read on to learn about what data profiling is, how you do it, and why it matters so much.
What Is Data Profiling?
Data profiling refers to the process of examining, analyzing, and reviewing data to collect statistics about the data set’s quality and hygiene. You can also refer to this procedure by other terms: “data archaeology,” “data assessment,” “data discovery,” or “data quality analysis.”
There are three types of data profiling:
- Structure discovery – this focuses on data formatting to ensure information is uniform and consistent
- Content discovery – this assesses the data quality of individual pieces of information
- Relationship discovery – this detects connections, similarities, differences, and associations between data sources
The How and When of Data Profiling
Now that we’ve established a definition for data profiling, let’s explore the when, how, and why of the concept.
We’ll start off with the “when.” You perform data profiling with an ETL process, which takes place when information is being moved from one database to another.
Now, the “how.” There are three ways to profile data:
- Column profiling, which counts the number of times a value appears within each column in a table
- Cross-column profiling, which assesses values across columns to perform key and dependency analysis
- Cross-table profiling, which looks at values across tables to identify potential foreign keys
The Why of Data Profiling
Having established the when and how, let’s look at the “why” of data profiling. In a recent webinar, Harald Smith, Director of Product Marketing at Syncsort, explained why data profiling is crucial for data quality as well as data management best practices. You’re encouraged to watch this webinar to learn more about data profiling.
When you perform data profiling, you’re asking yourself whether there’s any incorrect or incomplete information in your data sets, or if there’s any data that hasn’t been formatted properly. There might also be missing context or duplicated information.
Good data is crucial if you want to take advantage of such technologies as AI and machine learning (ML). ML thrives on good data. The conclusions it reaches are only as reliable as the information that comes into the system. When you skip the step of data profiling, you can’t be confident you have high data quality.
Data profiling gives you greater confidence in your information because you know the data quality level is high. You can take steps to fix the problems that plague your information, then make more informed decisions that drive your business forward.
When you adopt data management standards such as data profiling, you can trust that your information is of high quality. High data quality leads to more positive outcomes. Additionally, with higher quality data you’ll get more out of new technologies such as ML, and you can make business decisions based on a more solid informational foundation. To learn more about the importance of data quality, read our eBook: 4 Ways to Measure Data Quality.