Mastering the basics: Why master data management is vital to Gen AI readiness?

According to a study, the most important reasons for an organisation to implement data preparation and management tools are higher performance (30%), better data insights (21%), data-driven end-user satisfaction (21%), and risk reduction (12%).

The statistics demonstrate a need for resilient data management for generative AI (Gen AI) to produce effective results. High-quality data from Gen AI helps in customer service improvements, product ideation and creation, and marketing content generation.

This article explains how effective master data management (MDM) is key to effective Gen AI in your business.

What is the role of AI in master data management?

Master data is a single source of truth: a company’s critical data, including customers, products, and other key business entities. Without an MDM, this data remains scattered in several systems across departments with limited access.

AI augments the capabilities of MDM by improving the speed, cost, and ease of accessing data across the organisation. Here are some important roles of AI in MDM:

Data quality 

AI cleanses and enriches incomplete, inaccurate, and inconsistent data within the MDM system. It uses machine learning to learn and identify data errors, such as inconsistent data formats and data errors.

Data governance 

This includes implementing policies and procedures to manage and secure data properly. However, many data governance steps fail during implementation. AI in data management automates policy and procedure enforcement.

Data democratisation

Traditional MDM systems heavily rely on IT teams for implementation and maintenance. AI for data management leverages natural language processing (NLP) and a low-code approach to reduce the dependency on large IT teams.

Data validation

AI acts as your MDM co-pilot. It proactively collaborates with your decisions, challenges them, and explains any decisions it takes on demand.

Data maintenance

Ensuring that the data within your MDM is up-to-date and ready to be delivered on demand is a continuous and resource-intensive task. AI uses machine learning algorithms to automate data maintenance, identify any changes in records, and update them.

The AI model trains itself on the MDM data and becomes better at maintenance with time.

How do you harness Gen AI for master data management?

Generative AI for businesses adopts rapid technological innovation to transform their operations and elevate value creation for their teams and customers.

MDM discovery

A study found that while large businesses have an average of 175 applications, smaller ones have an average of 73 applications, with the growth in data lakes at 30% CAGR. As your master data pool becomes larger, it is increasingly difficult to identify the domain type. This is where machine learning techniques such as semantic tagging, clustering, and data similarity automate data discovery and domain identification without spending hours evaluating millions of data columns.

MDM lineage

Gen AI maps and tracks how master data moves between applications across the organisation. It does the data lineage mapping by scanning technical metadata and using machine learning-based relationship discovery.

For example, the U.S. FDA tracks the end-to-end supply chain of drugs, including suppliers, ingredients, distributors, and manufacturers, using MDM lineage. This helps track problems in drug lots, trace their origin, and recall quickly (if necessary).

MDM modelling

A centralised MDM that acts as a single source of truth to amplify the operational and analytical use. This helps in application modernization, digital commerce, cloud data warehousing, and data lakes. AI helps in schema matching between individuals or a group of attributes in semantically related master data models.

MDM categorisation and acquisition

Gen AI automates the ingestion and onboarding of master data into the files. It identifies fields and field types and maps them to data models. This increases the efficiency of business processes that interact and exchange data with other applications and customers.

MDM match and merges

Automatically identify duplicate data and match and merge it into a golden record within the MDM using Gen AI. The system uses declarative and AI rules for indexing and blocking multiple fields for quick and better data de-duplication and matching.

MDM governance

Map the data owners, policies, and business glossary definitions to master data using NLP techniques, domain discovery, and data similarity. This improves the productivity and accuracy of associations and facilitates cross-functional collaboration between data owners.

MDM privacy and protection

Identify and classify sensitive data within the MDM for enhanced privacy and protection. Associate privacy policies and enforce the rules of the privacy policy. Auto-identify data such as credit card numbers, phone numbers, email addresses, and social security numbers, and enforce the privacy rules within the API.

How can Infosys BPM help?

The state-of-the-art AI platform by Infosys BPM is a suite of ready-to-use BPM-focused solutions and design frameworks to transform business operations. It leverages the power of Gen AI for business processes and insights, thus augmenting your business’s MDM.

Read more about the Gen AI for business operations service.