What Is Data Profiling and How Is It Used?

Data is one of businesses' most valuable pieces of information to make decisions and create effective strategies. Without data, business leaders would risk their company’s growth in decision-making closer to uninformed guesses than informed strategy. Data is a key piece of evidence-based planning, and businesses from different industries choose to leverage data to ensure the choices they make have robust information driving them. 

However, data is only effective when it’s accurate. That’s why businesses use data profiling, or cleaning data, to ensure its quality and safeguard themselves against the risk of making decisions based on incorrect data. Read on to learn about the advantages of data profiling, its uses, and the risks of big data profiling.

What is data profiling?

Data profiling is when someone or something, whether an automated program or a data professional, examines collected data sets for errors, continuity issues, or other signs of low quality. The process typically begins by applying a framework of what the examiner expects to see within the data, such as a set number of participants or a specific demographic, and if the data accurately mirrors those expectations.

Data profiling helps data professionals identify the legitimacy and quality of the data, influencing the effectiveness and accuracy of any insights derived from it.

Types of data profiling

Data professionals use a variety of methods to explore the quality of a data set, but these methods typically fall into three general categories:

• Content discovery allows a data professional to identify systemic issues or any errors with the data.

• Structure discovery is useful for finding inconsistencies or formatting issues, such as missing content or numbers. 

• Relationship discovery allows a data professional to identify the specific type of data and how it relates to each other.

Applications of data profiling

Professionals in various industries use data profiling when they need to rely on collected data to inform decision-making. Some examples of these industries include health care, law enforcement, marketing, and government.

Health care

In health care, professionals use data to help improve patient outcomes, diagnose diseases, allocate funds for programming and hospitals, and monitor health risks. Big data allows information that would typically be siloed in separate locations or practices to come together for higher-level analysis and use.

Law enforcement

Law enforcement uses big data to help predict the location and likelihood of criminal activities. Departments then assign officers to specific patrols in the hopes that their presence will deter crime and quickly address any issues.

Marketing

Marketers use big data to learn more about customers’ behaviors and needs. With accurate data, marketers can create new strategies and campaigns to increase customer loyalty and retention and optimize their own actions for better customer engagement.

Government

Governments use data profiling to help cleanse the data collected from communications between employees, constituents, politicians, and national leaders. Big data is also an important piece of informing decisions to create new public policy or government services since it helps officials create programs that better reflect their constituents’ needs.

Advantages of data profiling

Data profiling has several key advantages that make it an important part of the processing of big data. In general, data profiling helps to ensure the data you are working with is accurate and high-quality. It helps professionals identify where issues or inaccuracies might occur and allows them to address them before implementing the data. Data profiling increases the efficacy of your data and allows you to store it all in one place for better analysis.

Risks of data profiling

The risks of big data profiling tend to fall into the concern that the profiling is incorrect, which introduces inaccuracy into the data sets. For example, the sheer volume and complexity of the collected data might mean it’s time-consuming and expensive to profile. If the data finds itself siloed, or the company doesn’t have a data professional on staff, then the cleansing might be incorrect. Plus, data collection means there’s a risk of someone’s private or personal information facing exposure or unethical use.

Roles that use data profiling

Some careers that use data profiling as part of their expected tasks or responsibilities include data architect, data analyst, and profiling analyst.

Data architect

Average annual US salary (Glassdoor): $141,473 [1]

Data architects are professionals responsible for creating the databases companies use to collect, store, and analyze data. In this role, you work with employees to identify their goals for the data and create tailored databases for their use. You are typically the one responsible for creating the processes required to successfully profile and utilize data. You also update databases with new technology as it develops. Data architects typically have a bachelor’s degree in a subject such as computer science. This is a senior-level role, so you’ll generally need at least eight years of experience in related careers first. 

Data analyst

Average annual US salary (Glassdoor): $85,950 [2]

Data analysts use collected data to solve problems and improve business practices and decisions. You ensure the data is of high quality through profiling and then use the results to make recommendations that help your employer reach their goals. Data analysts typically need a bachelor’s degree in a subject such as computer science or finance. 

Profiling analyst

Average annual US salary (Glassdoor): $98,804 [3]

Profiling analysts use data to report on trends. As a profiling analyst, you use your insights to create actionable plans or strategies. You spend time handling, cleaning, and analyzing data, then creating robust reports used by various professionals. Profiling analysts typically require a bachelor’s degree in mathematics or computer science. Some employers might prefer you to have a master’s degree. 

Learn more with Coursera.

Data profiling is a key piece of data analysis, particularly because it helps ensure the data is accurate, efficient, and of high quality. With courses on Coursera, you’ll have the opportunity to explore the exciting field of data profiling. Options such as IBM’s Introduction to Data Analytics can help you learn about data basics and see if a career in this field is right for you.