According to Statista, the data analytics global market has been forecast to reach $70 billion and more by the year 2025. It is shown in another report that the data volume consumed, copied, captured, and generated all over the world is expected to be 182 zettabytes in the same year. When you consider the rise in data volume, it’s imperative for businesses to ensure data quality and leverage that data to drive their decision-making processes. A poll by Statista found that 45% of organizations surveyed in Europe and the United States have listed that their employees don’t have analytical skills, which is proving to be the main challenge in using data for driving business value in 2022. To stay competitive in the marketplace and achieve greater confidence for their data sets, organizations are turning towards data profiling as the core ingredient for their overall data management strategy. What Is Data Profiling? The assessment of data is known as data profiling, using a combination of business rules, tools, and algorithms to create high-level reports of the condition of the data. The main goal of data profiling is to uncover missing data, inconsistencies, and inaccuracies so that data engineers can do their investigation and correct the sources. The reports of data profiling are generally graphs and visualizations with tables that display the relevant metrics, like the duplication degree in the set of data. Furthermore, data profiling isn’t only meant to be used to troubleshoot risks to data integrity and data quality. It can also become a core discovery process used by analysts for uncovering the relationships, structure, and content between different sources of data. To sum it up, data profiling helps create a profile of the quality and state of the data. The collected information for building this profile involves metadata like data length and type, generated statistics related to the data set, and dependencies between the tables. Data profiling also involves tagging the set of data like assigning categories and keywords for making the data searchable and speeding up future analysis. Now that you know what data profiling is, let’s share a couple of examples to define data profiling in a more expansive manner. Examples of Data Profiling There are many use cases of data profiling within organizations that seek to better maintain and understand their data. Here are some examples to better paint the picture: Data Warehousing When a data warehouse is created by a business, its goal is to collect data from different sources and store it in standardized formats, where it can be easily accessed to be analyzed. However, if the data quality is poor, then gathering all of it in one location doesn’t solve the bigger issues, as you get bad decision-making from bad data. When you incorporate data profiling in the workflow of the data warehouse, it offers a check against data of low quality. As the collection of information is done via ETL processes, you can use data profiling to validate the integrity of the data and comply with data rules during or before the intake processes. Now your business has centralized data, which you are confident can be reliable for making informed decisions. Mergers and Acquisitions Let’s assume that your business is merging with your competitor. You’ll then have access to a wealth of data that is entirely new for discovering new customers and finding new insights. However, you’ll first have to integrate all that data with the data that already exists. Data profiling offers a higher-level overview of the newer data assets made available, along with their dependencies. Here, data profiling can be used to identify data that has been duplicated between the two systems and can show where the format of the newer data is different so that your data teams can work on standardizing it. Now your data is ready to be merged and cleansed into one place. Data Profiling Benefits Apart from offering an improvement in data...