Implement

DG Implementation best practices

Build data audit/balancing
process


It is important to verify that correct data is pulled from the sources and loaded completely to the target location during the data integration process. The data audit ensures that by setting up a set of common tables to accommodate various control metrics related to data integration.

Daily stand-up
meeting


These meetings occur once every working day and function as a communication vehicle between the team members. The goal is to keep everyone on the same page as to the progress of a project.

Data
cleansing


Poor data quality can easily translate to vast sums in lost revenues. Investing some time in implementing data cleansing processes is a good practice for maintaining data quality.

Effective
data matching


Data duplication is a common problem that plagues a lot of organizations. Matching data across sources and repositories allows an organization to streamline data driven initiatives and save resources on redundant actions.

Data
standardization


Data standardization refers to the transformation of data into a standard format which suits the business. This is done during data profiling and should be performed in consultation with a steward.

Managing the
project lifecycle


The number of reviews during a project’s life-cycle reflect on the quality of the project. Managing, documenting and acting upon these reviews is crucial for project success.

Real-time
matching


The best way to eliminate duplicate data is to prevent them from entering the target location. Matching influxing data against existing master data in real-time allows only the new data to enter.

Understanding address
validation


Address validation refers to the validation of any postal address against the postal standard of the originating country.

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DG implementation.

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