Data cleaning is an essential part of the data aggregation and transformation process Global Markets undertakes in its research methodology. The massive amount of data aggregated through primary and secondary research over the years were cleaned prior to taking shape as figures and sentences in Global Markets reports and applications. The purpose of the data cleaning service is to enable Global Markets’ clients in taking advantage of its expertise and long historic knowledge of data cleaning to transform into meaning information. The data cleaning steps followed by Global Markets are:
Standardization and Normalization
Global Markets’ Arabic and English data standards are built on local data aggregated from public domain sources. The standards developed are unified across individual names, company types, company names, transaltions, measurement units, and more.
The normalization of data follows by tranforming the standarized data into unified common scale in order to develop the proper utilization of the data. This is important since each one of the GCC countries have their own definitions, bylaws, data standanrds that require a “common scale” for them to communicate with each other.
Flagging Errors & Duplicates
Data errors and duplicates can cause chaos by generating wrong reports and analysis of data no matter how beautiful/interactive the data dashboards are. The process flagging data errors and duplication is driven by a strong data understanding the core of how data is originally aggregated by private and public sources. Such understanding that is backed by strong data structures and standards cleanses data of errors and duplicates.
Missing Data Enrichment
Once the data is standarized and normalized, Global Markets fills the data gaps in the different datasets it aggregates. Filling gaps of data from the public domain source into clients’ datasets improves their ability to build a reliable connection of the external data sources.
Once a dataset has gone through the data cleaning cycle, Global Markets has the ability to interconnect it with other datasets that are internal within the client’s data warehouse or external in public domain data. Imagining internal databases common standards with government datasets in municipalities, statiscal authorities, ministries is now possible with Global Markets.
The ultimate purpose of data cleaning is to enhnace the quality of data that is being used by companies in order to reach a relavant, accurate, complete, and a valid understanding of the data.