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The shift from traditional entity resolution processes to automated methods marks a pivotal evolution in data management and analysis practices. This blog explores the fundamental differences between these approaches, highlighting the enhanced efficiency, and the significant time and cost savings automation brings.
Traditionally, entity resolution has been heavily reliant on manual processes with inhouse developed tools and off-the-shelf solutions. These processes necessitate extensive human involvement in data entry, data preparation, algorithm development, data verification, and reconciliation processes. Such methods are not only labor-intensive but also prone to a high frequency of errors. As the volume and complexity of data increase, the scalability of manual methods faces significant challenges. This limitation results in proportional increases in costs, ability to scale and the time required for effective data management, delaying the generation of actionable insights and hampering prompt decision-making.
Automated Data Inc. (ADI) revolutionizes entity resolution by harnessing the power of automation to simplify the matching process. The platform leverages artificial intelligence to replicate what a data scientist does when resolving entities and matching data. Under the hood, we use large language models, machine learning and proprietary algorithms to semantically profile data, and then optimize matching based on semantic labels.
In a side-by-side comparison, the manual process for entity resolution was cumbersome, and involved a large number of steps. Users had to peruse documentation, examine data and structures, comprehend how data should be linked, write code (including normalizers and matching algorithms), troubleshoot issues, execute the process, and evaluate the outcomes.
In contrast, ADI simplified this workflow to a few simple steps. Users simply connect to their data sources, initiate the process with a click, and then evaluate the matched data. In addition, the platform includes all the required human-in-the-loop workflows for exception management for times when changes are required. One shot benchmark tests have demonstrated that ADI achieves better than 90% precision with zero fine tuning.
The cost benefits of using ADI are substantial. Compared to existing practices and competing platforms, ADI reduces the TCO by more than 70% between license fees, labor costs and other overhead. In a test with ADI, versus a group of data professionals who independently completed an entity resolution project across 15 data sources, ADI achieved an 85% reduction in time spent onboarding, profiling and matching data. Furthermore, the accuracy and consistency of ADI's processing minimized error rates by up to 50% which are typically caused by manual processes, cutting down the expenses and time required for data corrections and mitigating their operational repercussions. By freeing up data teams from manual entity matching tasks, they can devote their efforts to tackling larger, more complex problems and extracting valuable insights from their data.
The current transition in entity resolution marks a significant turning point from labor-intensive methods to streamlined automation. This shift represents more than just efficiency gains; it signifies a fundamental revolution in data handling, promising substantial savings in both time and costs.
Gain access to accurate and user-friendly solutions for handling large datasets and generating actionable insights. See how ADI is revolutionizing entity resolution, through automated matching pipelines, semantic linking and enhancements, and streamlined data management.
Contact info@automated-data.io or request a demo to learn more.
