Frank Sentner

Messy, inconsistent data makes it hard to gain insights and make informed decisions. There are some normalization techniques and solutions you may already be considering. Software can help you standardize your data, correct inaccuracies, and add missing information, all while integrating data from multiple sources. But choosing and implementing the right solution requires understanding both technical

Larger businesses handle data across multiple systems, each built to serve distinct business functions. While basic data preprocessing focuses on cleaning and standardization, core business systems reality reveal deeper challenges that technical solutions alone cannot solve. Key takeaways: Using data preprocessing as a downstream replacement for data quality and standardization is asking for problems. Cleaning

Data mapping, understood as mapping records and fields between systems is not an easy task. It’s way more than that. This process accompanies post-M&A integration in acquisition-heavy industries as well as reconciliation of data between regional business entities (e.g. independently functioning plants), or third-party sources of data (vendors, suppliers, etc.). It will also be a common