Data stewardship through automated information retrieval & AI/ML-based behavior learning

Simulate Stewardship Process

AI/ML based automated search and confidence scoring

Productivity Boost

Intuitive UI helps analysis and decision making

Complements MDM

Enrichment and scoring of MDM fuzzy match outputs

How Life Science CIOs Can Build a Data-Driven Enterprise Foundation With Commercial MDM.
(Animesh Gandhi, 23 Sept. 2020)

D Cube Analytics has been mentioned as a Representative vendor in Gartner

The DDS MDM.aiTM application takes a drastically different approach to data stewardship where an Assistant application complements the MDM system. The DDS MDM.aiTM application leverages advanced information retrieval, data enrichment and AI/ML capabilities to accurately replicate or optimize the human data stewardship behavior. The application drastically reduces the outcome variability and significantly reduces the human intervention required in the stewardship process


Configurable & Streamlined Lookups

Standardized lookups from external websites used for data enrichment, configured based on the attribute and availability of lookup results.

AI/ML Powered Recommendations

AI/ML-enabled automated systems mimic the data stewards mind to generate merge/no merge recommendations with a confidence score. ML algorithms take the subjectivity away from decision making.

Confidence Scoring

Attribute match scoring engine has a defined scoring logic for each record attribute. Attribute level scoring generated during data vectorization helps provide the final confidence score to the data stewards.

Model Training on Recurring Patterns

Training based on the steward feedback would help accurately score recurring patterns. Stewards can focus on more varied scenarios during manual audits.

Intuitive UI

A unified view of the potential match records with available enrichments and merge recommendations. Easy to use interface helps making decisions based on the automated data enrichment and algorithm-driven insights.




Collect data from MDM and associated enterprise applications, industry registry databases and other sites from Google lookup, based on universal identifiers, exhaustive search criteria and rich business rules.



Generate record level scores using supervised machine learning methodologies. AI/ML-enabled automated intelligence system provides recommendations for merge/no merge with a confidence score.


UI based assistant application showcases the scores along with enriched data giving visibility into how confidence scores are calculated. This assistant application also allows data stewards to interact and actionize as needed.



Productivity Increase


Reduction in data steward on-boarding time


AI/ML Accuracy

months ROI



Case Studies

Data Wrangling for AI/ML

Data Wrangling for AI/ML

Associate Product ArchitectProduct EngineerOne of the leading providers of machine vision, AI/ML solutions provider was in need of a robust Data Wrangling solution which would be leveraged for their multiple customer deployments in order for them to scale their...

Leveraging AI in automating Data Stewardship process

Leveraging AI in automating Data Stewardship process

Technical Product ManagerPrincipal Consultant – Data ScienceA large biopharma organization is using Informatica to create Prescriber master data based on IQVIA onekey / HCOS data, matching with HCP/HCO level transaction records from various internal / vendor sources....

Leveraging Full Potential of the Data Lake

Leveraging Full Potential of the Data Lake

Associate Product ArchitectAssociate Software DeveloperData Science teams at a large biopharma organization face multiple challenges while creating data packs, which is a prerequisite for their models. The prolonged data pack creation timeline occurs due to the lack...

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