Data Management Platform to Assure Data Consistency

Overview

Transforming businesses via Data Profiling, Masking & Migration, Data Assurance and Advanced Analytics while keeping future shifts in mind.

How is Infosys delivering value to customers in the area of Data Services?

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The Infosys Data Services Suite (iDSS), is an easy-to-use, high-performance, scalable, and cost-effective data management platform that addresses most issues in the data migration life cycle. iDSS provides seamless, zero error data services across diverse source and target databases, and addresses all data management needs for structured and unstructured data. It consists of separate modules for data quality, master data management (MDM) and migration methodology, rich artefacts, checklists and toolsets for accelerating data migration and MDM.

Infosys Data Workbench (iDW) – that specifically caters to data quality – is a highly scalable data quality solution coupled with light weight analytical MDM capabilities.

It has been developed to build Analytical MDM capabilities on data lake, using traditional and ML based techniques. IDW has a comprehensive set of tools for data profiling, data standardization, address standardization using Google and Bing APIs. It also uses supervised and unsupervised learning models to detect data anomalies, and help in missing values correction. The MDM module has different matching techniques using deterministic, fuzzy, phonetic or hybrid approaches and ML based approaches to identify duplicates and generate a golden record using survivorship rules. It has the following modules:

Data Profiling: Performs source data profiling and generates the reports that helps in drawing out DQ Rules to further implement.

Data Cleansing: Performs source data standardization and saves cleansed data in the required location.

Master Data Management: Solution to pre-fill unknown master attributes in transactional data by mapping to history/master data using Machine Learning Models.

Challenges & Solutions

iDSS uses historical data to understand pre-existing patterns. Dynamic learning is used to pre-fill unknown master attributes in transactional data by mapping to master data using machine learning models. Machine learning enhances the Master Data Management process by using automation to minimize manual efforts to pre-fill master attributes. MDM is usually applied to reference data and entities.

iDSS has a domain specific data model that facilitates the entire data lifecycle stages, reduces effort and increases productivity by up to 35%. Data Migration relates to transfer of master, transactional and historical data, across applications or computer storage types while changing the storage, database or application. iDSS is GAMP validated and adheres to all documentation and product life cycle standards as prescribed by GAMP.

Client may need to let go of or merge application services (from one database to another) or undertake application migration. All the existing functionality (onboarding of plans, maintenance of retirement plans, etc.) and underlying transactional database need to be migrated from the existing legacy application to the new application in addition to migrating the database along with stored procedures, indexes, tables and data.