This is the third and final blog post in my “Charting a Course Through the Data Mapping Maze.” If you’ve been following the previous posts, thanks for joining me on this journey. Part one defined data mapping and outlined key components and why it’s essential. Part two explored how data mapping works, the common techniques used, and the top challenges to be overcome.
A few years ago, Harvard Business Review reported france rcs data on a study showing a paltry “3% of companies’ data meets basic quality standards.” Bad data is bad business, and countless examples show the economic and reputational impact. Unity Software disclosed on a Q1’22 earnings call that it took a $110 million hit to its bottom line after “ingesting bad data from a large customer.”
Selecting the right data mapping tool is an important decision to help maintain and improve an organization’s data quality.
Mapping Data Mapping Solutions
Data mapping is performed in three main ways: manual mapping (people meticulously match data elements based on their understanding), automated mapping (algorithms and tools match data elements, which is useful for large-scale projects), and semi-automated mapping (algorithms suggest the best matches based on preset rules and people fine-tune to the details).