Data enrichment companies | data transformation process

Data Analytics Process

An organization's most essential and difficult data-driven task is to turn transactional data into actionable insight that leads to excellent business decisions and outcomes. The detailed data transformation stages or processes are:

Data enrichment companies | data transformation process

Data Extraction : Collection of an organization’s data from multiple sources

Data Consolidation : Putting all extracted data of different format into an integrated destination

Data Profiling : Examining, analyzing and creating useful initial summaries of source data.

Data Transformation : Also called as "data enrichment" comprises data cleaning, data clustering, and data classification or categorization into numerous buckets of information called categories or taxonomies..

Data Loading : Insertion of converted data into an operational data store, data mart, data lake, or data warehouse

Data Insights : Using reports, dashboards, and data visualizations to analyze data and draw conclusions

Actionable Data Insights : It may impact choices and drive the company effectively with a meaningful result

An organization's raw transactional data is transformed into actionable information through extracting, transforming, and loading (ETL). For example, collecting data from many data sources, integrating data from these sources with different headers, cleaning data using efficient cleansing logic, grouping and classification for single or multiple data attributes, discovering the appropriate insight to act, etc. To solve the difficulties, experienced analysts, sophisticated technology, and smart choices are required.