Key takeaways on data profiling:

• Data profiling is an essential process that reviews data sets to ensure alignment with business standards and goals, addressing issues such as inaccuracy and inconsistency to improve overall data quality.
• The data profiling process is categorised into three types: structure discovery (validating data formatting and consistency), content discovery (checking data quality and standardisation), and relationship discovery (uncovering connections between datasets).
• Effective data profiling can inform strategic decisions in data migration, warehousing, and cleanse operations, enhance customer relationship management, facilitate smoother mergers and acquisitions, and boost data security.

What is data profiling?

Data profiling is a meticulous process that uncovers the underlying structure and quality of data sets.

Utilising a suite of analytical algorithms and metadata analysis, data profiling examines, analyses, and summarises data, providing a high-level overview of data characteristics. Think of it like of putting your data under a microscope, revealing the finer details that can inform strategic business decisions.

It’s worth remembering that this process is not a one-size-fits-all approach. Instead, it tailors to the unique needs of each dataset, focusing on characteristics such as accuracy, consistency, and timeliness.

It’s much more than just counting non-null values or collecting basic descriptive statistics. It delves into the metadata, revealing frequency distributions, key relationships, and functional dependencies.

The ultimate goal of data profiling is to align data characteristics with business standards and goals, ensuring data quality and utility. By identifying and addressing common data issues, such as null values, invalid entries, and values that deviate from expected norms, data profiling ensures that the data is not just voluminous, but also valuable.

Types of data profiling

Data profiling isn’t a monolithic process. It comprises three primary data types: structure discovery, content discovery, and relationship discovery, each honing in on a different aspect of data quality.

Structure discovery is the detective of data profiling. It verifies the consistency and correct formatting of data, employing basic statistics to assess data validity. Think of it as checking the skeleton of your data, ensuring it is sturdy and well-structured.

Next comes content discovery, the meticulous examiner of the data profiling process. It delves into the quality of data, processing it for formatting and standardisation, and integrating it with existing data efficiently. Data exploration plays a crucial role in inspecting the flesh of the data, making sure it’s healthy and properly formed. A data quality assessment is essential in this stage to ensure accuracy and reliability.

Finally, there is relationship discovery, the sociologist of data profiling. It identifies the connections between different datasets, uncovering the intricate web of relationships that exist within your data universe. Together, these three types of data profiling offer a comprehensive understanding of your data.

Read more about how data can be used to grow your business:

• How to build an effective data strategy?
• Data-driven leadership: empowering managers to make informed decisions
• Data automation for business growth: everything you need to know

The importance of data profiling: where and when is it used?

Given its incredible importance, data profiling is not just a nice-to-have; it’s obligatory in various scenarios, such as data migration, data warehousing, and business intelligence projects.

Let’s consider a data migration project. Data profiling is the torch that illuminates the path, revealing the actual quality of data in legacy systems, which is often overestimated by business users.

By profiling data early in a project, it can prevent delays and additional costs by revealing data issues before they become problematic during testing or production phases. Understanding data lineage is essential in these scenarios to ensure data accuracy and consistency throughout the process.

Data profiling also plays a crucial role in data cleaning, with data profiling tasks being important to the process. It’s like a broom that sweeps away missing or incorrectly formatted data, like incomplete addresses or misplaced data in fields. This ensures that the data is not just vast but also clean and ready for further processing and analytics.

Benefits of data profiling process

The benefits of data profiling are manifold, impacting various aspects of business operations. At its core, data profiling improves data quality, making data-driven processes like data analysis or machine learning more efficient.

This, in turn, saves time that could have been lost due to data errors or inaccurate results from poor data quality. Additionally, data profiling can enhance data security by enabling organisations to pinpoint data vulnerabilities and implement necessary security measures.

Data profiling plays a crucial role in customer relationship management by:

• providing insights into customer behavior,
• helping businesses comprehend their customer data,
• informing more effective marketing and customer service strategies.

Moreover, during mergers and acquisitions (M&A), data profiling can identify and resolve compatibility issues in data systems and structures to ensure a smoother integration and transition.

Techniques for data profiling

Data profiling employs a range of data profiling techniques, from basic methods like column profiling to advanced techniques like cross-column and cross-table profiling.

The structure discovery technique validates that data is correctly formatted and consistent by employing processes like pattern matching and statistical analysis. Content discovery, on the other hand, involves a detailed examination of database elements to check data quality, which includes identifying null or incorrect values and standardising data formats.

More advanced techniques like cross-column profiling include key analysis, which looks for potential primary keys, and dependency analysis, determining relationships within a dataset. Cross-table profiling utilises foreign key analysis to examine inter-table relationships and identify orphaned records or value sets that could be mapped together.

These techniques, when automated using tools, can significantly streamline the data profiling process.

Common challenges in data profiling and how to overcome them

Despite its many benefits, data profiling is not without challenges. Some common hurdles that professionals often face include:

• handling large volumes of data,
• dealing with diverse data sources,
• ensuring data privacy,
• managing data quality issues.

One of the key challenges in data profiling is the manual inspection of datasets, which can be time-consuming, error-prone, and unsuitable for large datasets. Another challenge is data sprawl and data drift, which complicate data management, necessitating extensive data preparation before integrating into a data warehouse.

To overcome these challenges, it is recommended to take an iterative approach to profiling, rather than attempting to profile all source systems in entirety.

Also, it is important to profile the most error-prone data sources first, such as customer data entered manually, and ensure a data dictionary is included in the project plan.

Data profiling tools: open source and commercial

A plethora of popular data profiling tools are available to businesses, both open source and commercial, each offering unique features and capabilities.

Open source data integration tools like OpenRefine, and Apache Griffin offer a range of features for data governance, real-time alerting, and support for big data handling. These tools provide an affordable option for businesses looking to embark on their data profiling journey.

On the commercial front, tools like Talend, IBM InfoSphere, and Informatica offer robust features and comprehensive support for businesses with more complex data profiling needs. These tools often come with a higher price tag but offer extensive capabilities and support to justify the investment.

Selecting the right tool depends on your specific needs and resources. Regardless of the choice, the critical point is to leverage these tools to enhance your data profiling efforts.

Examples of data profiling

To truly appreciate the power of data profiling, let’s look at some real-world examples.

• Domino’s, the global pizza chain, used data profiling to manage data from its AnyWare ordering system, enhance customer insights, improve fraud detection, increase operational efficiency, and drive higher sales.
• Office Depot, the office supply retailing company, applies data profiling for quality checks before integrating data into the company’s data lake. This approach helps maintain a comprehensive view of customers and supports back-office functions.
• Globe Telecom, a major provider of telecommunications services in the Philippines, leveraged data profiling to score, profile, and cleanse data. This improved their data quality scores frequency, increased trusted email addresses by 400%, and enhanced ROI on marketing campaigns.

Leverage the power of data profiling for business excellence and data quality

Leveraging the power of data profiling and data integration can significantly enhance data quality, leading to better business decisions, improved customer experiences, and overall business excellence.

Implementing data profiling allows businesses to:

• sort through massive datasets,
• extract valuable insights,
• improve the quality of their business intelligence,
• identify and eliminate redundant or corrupt data,
• ensure that only accurate and relevant information is utilised for business decisions.

Beyond improving data quality, data quality reports can enhance data quality rules in the following areas:

• decision-making,
• compliance,
• customer service,
• profitability,

all of that achieved by identifying and mitigating data anomalies.

It’s no wonder data profiling is a powerful tool that businesses can leverage to enhance data quality, drive better business decisions, and achieve overall business excellence.

Through various techniques and tools, organisations can uncover the hidden treasures in their data, revealing insights that can propel them forward. While there are some challenges connected to it, they can all be overcome with efficient tools and best practices.

Embracing data profiling is not just an option; it’s a necessity in today’s data-driven world. If you are keen to make the most of it, consider talking to one of our experts. We will be happy to help you apply innovative data solutions and take your organisation to the next level!