Data profiling allows organizations to examine and analyze data from various sources to understand its structure, quality, completeness, and other key characteristics. It’s essential for ensuring that data is fit for purpose, and can help identify any issues like inconsistencies, missing values, duplicates, and anomalies that could cause problems for downstream processes such as data integration, analytics, and reporting.
Data profiling is a technique used to assess the quality, structure, and content of data. It involves analyzing available information to identify patterns, relationships, and anomalies. The process helps organizations make informed decisions and find areas for improvement.
Data profiling typically involves the following steps:
There are many reasons why organizations should invest in data profiling. The top benefits are:
Common data profiling examples include analyzing customer data to identify duplicates, ensuring the accuracy and completeness of financial records, and detecting inconsistencies in supply chain data.
Intelligent automation automates non-routine processes and tasks that require some level of thought. Businesses that have embraced digital transformation initiatives that include intelligent automation enjoy increased efficiency, and accelerated time to market.
Customer demand better experiences from the brands they interact with. To improve your CX, you will need to build an effective strategy. Create long-term relationships with customers by addressing their specific needs throughout their purchase journey.
Knowing the customers’ intent gives marketing and sales teams power over their journey through the sales funnel and allows them to predict future behavior and create campaigns that will address different personas. Understand what your customers want.