Businesses want to collect data about their audience and customers are willing to share their names, addresses, phone numbers and so on. That said, there’s always the possibility of typographic errors or customers may intentionally give out wrong information. Reports show that by 2025, the volume of data generated and consumed across the world is expected to cross 181 zettabytes.
With this in mind, it’s becoming more important than ever before to work on maintaining high data quality standards. It’s one of the reasons why businesses are paying more attention to data profiling as a part of their data management strategy.
What is data profiling?
Data profiling refers to assessing data with a combination of business rules, tools and algorithms to create a report on the condition of your data. Data can be profiled by comparing all records within a single column, comparing columns in the same table, comparing columns across different tables and validating data against pre-defined rules.
This exercise is aimed at discovering data types, recurring patterns, inconsistencies, inaccuracies and gaps in the records as well as uncovering the structure and relationships between data sources. It also builds profiles on the data with metadata such as data type, functional dependencies and length between tables, etc. and tags the data set with relevant keywords and categories to make it searchable. The reports are usually in the form of graphs and tables that help visualize the data condition so that data engineers can find the source of the issue and correct it.
Let’s look at an example. When two companies merge, information from both databases is brought together. With data profiling, you can get a high-level overview of the new data available and how it may be connected to your existing database. It identifies duplicate data, data that follows different formatting standards, etc. so that the data quality team can work on standardizing the dataset, deduplicating, appending and merging records to create a single source of truth.
Types of Data Profiling
There are three main types of data profiling.
Structure Discovery
Structure discovery or structure analysis as it is also known is the process of validating formats and matching patterns. For example, a column of email addresses could be scanned to ensure they all contain a single “@” and end with “.com”.
Structure discovery also involves calculating basic statistics such as standard deviation, mean and mode for numerical data.
Content Discovery
This involves looking for obvious gaps in the data and missing values as well as ambiguous or incorrect data. For example, it could highlight an address that is missing a pin code or abbreviated states that should be written in full.
Relationship Discovery
As the name suggests, this is the process of cataloging links between tables such as instances where cell values are created on the basis of calculations involving other cell values and data sets with references between primary and foreign keys. It helps identify overlapping data and instances where the data sets are not aligned.
How does Data Profiling Help?
Improved data quality is the most obvious benefit of data profiling. In turn, this helps organizations in many ways.
Increases confidence in your data
Though the importance of data is well-known, a recent survey found that unfortunately, 75% of business executives do not have a high level of trust in their company data. Without trust, data cannot be used to its full potential.
Data profiling helps analysts identify issues and correct them to improve the overall quality of the data sets. It also makes it easier to understand why and how these issues came about so that proactive measures can be taken during the data collection stage to keep them from recurring. This gives your team more confidence in the data and encourages them to use it for decision making.
Makes data easier to search for
Let’s say your brand wants to send out a marketing campaign targeting customers living in New York. If you have records with the state abbreviated as “NY” in the address field, they will not be discovered when searching for “New York”. As a result, you miss out on connecting with potential customers.
Data profiling standardizes such details and hence makes data easier to find. It also tags records with keywords and metadata and categorizes them in a way to make information easier to filter and access.
Identifies issues in their nascent stages
Left unaddressed, data quality issues can quickly snowball into bigger problems. Let’s say you have an incomplete, incorrectly formatted address. Last mile delivery agents may not be able to deliver orders to this address resulting in returns, extra shipping and disgruntled customers.
With data profiling, such issues can be identified and corrected before they affect other aspects of your operations. Calculating standard deviations and other statistics around numerical data also helps identify outliers that may otherwise have been missed out on.
Strengthens AI and Machine Learning Outcomes
When it comes to using AI and ML models for decision making, the relevancy of outcomes is directly linked to the quality of data being fed into the model. Data profiling ensures that all data being used by such models meets quality standards and is available in standardized formats. Something as simple as ensuring all dates are written in the same DD/MM/YY format can dramatically impact the results of AI algorithms and minimize the risk of having them draw erroneous conclusions.
Summing It Up
As the volume of data being worked with increases, so does the need for data profiling. Data profiling helps you get ahead of issues that could cause problems and delays leading to poor decisions, missed opportunities and unhappy customers. The good news is there are many data profiling tools available in the market today. Look for tools that automate data profiling as part of your overall data quality checks, are easy to use, scalable and can be integrated with your existing systems and software. Get started and the results will soon be evident.