Data Standardization: Define, Test, and Transform

While organizations shift towards establishing a data culture across the enterprise, many are still struggling to get their data right. Pulling data from disparate sources and getting varying formats and representations of what is supposed to be the same information – causes serious roadblocks in your data journey. Teams experience delays and mistakes while carrying out their routine operations or extracting insights from datasets. Such problems force businesses to introduce a data standardization mechanism –

How To Merge Purge Large Databases

An average enterprise uses 464 custom applications to digitize its business processes. But when it comes to generating useful insights, the data residing at disparate sources must be combined and merged together. Depending on the number of sources involved and the structure of data stored in these databases, this can be quite a complex task. For this reason, it is imperative that companies understand the challenges and process of merging large databases.   In this article,

Address Standardization 101: Benefits, Methods, and Tips

When was the last time you found all addresses in your list follow the same format and were error-free? Never, right? Despite all the steps your company may take to minimize data errors, address data quality issues – such as misspellings, missing fields, or leading spaces – due to manual data entry – are inevitable. In fact, Professor Raymond R. Panko in his published paper highlighted that spreadsheet data errors especially of small datasets can