The purpose of any data project is to transform available data into valuable assets that will put your company on the path to excellence. To achieve this, data must be easy to discover and catalog. The objective is to make it not only accessible but above all understandable and exploitable for your employees who use it on a daily basis. One of the levers to achieve this is Data Profiling. Here are some explanations.
The very principle of a data strategy is to give your teams the means to rely on tangible, representative, and quality information to fulfill their missions. But raw data is not enough. Like a precious mineral, data must be methodically refined. One of the essential phases to make data speak is called Data Profiling. It is a process that relies on analyzing and exploring the available data to understand:
- How they are structured,
- The information it contains,
- The relationships between different datasets,
- How they could be associated, combined, and used more efficiently.
What are the different types of Data Profiling?
When you launch a data profiling process, you examine and analyze all of your data assets to determine their structure, nature, and possible combinations. In this way, you can clearly identify the interdependencies between datasets to better make them talk. According to data experts, there are three types of Data Profiling: structure profiling, content profiling, and relationship profiling.
One of the key elements of data exploitation is its optimal organization. To do this, you need to look at the structures of the data. Structure profiling is the type of Data Profiling that ensures that the data is correctly formatted and consistent within a database. Structure Discovery or “structure profiling”, refers to a process of validating the format and consistency between datasets.
Content discovery, or content profiling, is based on the analysis of rows of data to identify errors and systemic problems. For example, the most common use is to examine a list of customers to identify those with invalid email addresses. The goal is to highlight null or erroneous values so that they can be corrected as soon as possible.
The third type of data profiling, called relationship discovery, is used to analyze and identify the relationships of data used between spreadsheets or database tables. To do this, you will need to perform a metadata analysis to detect possible connections between different data sources and identify overlaps.
The benefits of Data Profiling
There are three main benefits of Data Profiling. The first is that it saves time before launching a data project. You can take an exploratory approach to determine whether the data you have will really enable you to gain the knowledge you need. Then, and only then, can you implement your project.
The second benefit of Data Profiling is that it improves data quality. Data Profiling ensures that your data is clean, accurate, and ready to be distributed throughout the organization.
Finally, Data Profiling allows you to expand the scope of what is possible. Your employees need to quickly and easily find specific types of data that can help them launch new projects or capture new markets. When data is not searchable, it can be difficult to locate it in a longer chain. With Data Profiling, data is better identified, categorized, and sorted. Your teams can then easily manipulate it and assemble it into databases using specific keywords.
By engaging in Data Profiling, you create the conditions for optimized exploitation of your data. Done methodically, Data Profiling is a promise of efficiency, relevance, and cost optimization, as it will allow your teams to save precious time and rationalize the exploitation of your data.