![]() ![]() Data deduplication helps provide the customer success team with a holistic view of their customers and provides the best customer experience possible. Duplicate data can cause companies to focus on the wrong targets, and even worse, to contact the same person multiple times. Data deduplication helps provide the team with the most accurate data, and eventually improves analytics performance. In general, duplicate data really distorts a company’s visibility into its customer base and can derail analytics efforts. In the example mentioned above, we were unsure which customer is our highest paying customer. Data analysts no longer need to spend 80% of their time on tasks such as data wrangling and transformation and can instead focus on more valuable data analysis. Then, it helps save on data preparation and correction costs. First of all, it reduces data storage costs. ![]() This is the most obvious and direct benefit. For example, improved data quality can lead to more cost savings, more effective marketing campaigns, improved return on investment, improved customer experience, and more. It also overlaps with entity resolution, where the task is to identify the same entity across different data sources and data formats.ĭata deduplication can benefit your business in a myriad of ways. From an operational perspective, you can’t answer questions like which account is the right one to contact?įrom an analytics perspective, it’s hard to answer questions like who are my top paying customers by revenue?ĭata deduplication has a lot of overlap with data unification, where the task is to ingest data from multiple systems and clean it. Redundant or duplicate data can harm your business and your strategy in many ways, both in operational use cases and analytical use cases. It is an ongoing process to ensure no excess data is in your database, and that you’re using only a single copy of truth, or the golden record, for analytics or operations. At the most basic level, data deduplication refers to the deletion and removal of redundant or duplicate data. ![]()
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