Transformation in ETL refers to the cleansing and aggregation of the collected data for easy interpretation. We offer data transformation both as a part of the ETL process and as a standalone service.
The transformation step implements a set of rules to convert the raw data into a structured format. You can add value to your business through this step by converting the collected source data into powerful business insights.
The data transformation phase undergoes many sub-processes with the help of AI.
Consolidates first-party data from internal sources with third-party data extracted from multiple external sources.
Deletes extra copies containing redundant and overlapping information and stores only one golden copy of the data.
Examines the data for any inaccuracies or inconsistencies once the data migration process is completed.
Combines data residing in multiple sources/locations into a single, exhaustive dataset for a unified view.
Removes and fixes any corrupt, inconsistent, inaccurate, or incomplete data within a dataset.
Compiles information from various databases and presents it in a summarized format.
Bad data refers to outdated information and can have some direct consequences on your business, including loss of revenue. For example, obsolete data can lead to inaccurate marketing assets that are not suited for the intended audience. Therefore, the extracted data must go through multiple sub-processes to complete the transformation phase.
Enriched data can transform your business by offering accurate value and knowledge about your brand, customers, and business as a whole.
Make informed decisions
Save time and reduce costs
Create better marketing plans
Learn more about your customers
Know your competitors
Predict industry trends