AI MDM Management


Master Data Management has been around for over a decade now. As we enter the new age where Data has become currency, most companies tend to spend a lot of there time bringing in data from various sources or systems. Having moved in the data to a common platform they relentlessly work from data cleansing, curating and eventually getting prepped up for master data management. The archaic archetypes of ETL, data pipelines and meta data management are getting soon getting to a point to be obsolete in some manner. Some digging on the internet are one can see a simplified view of MDM in steps

Step 1: Tie Master Data Management to a business, process improvement initiative.

Step 2: Identify all master data assets related to that process improvement initiative.

Step 3: Evaluate and profile the current status of your initiative’s data quality.

Step 4: Identify all necessary data integration for systems of record and those subscribing systems that will contribute to, or ultimately benefit from MDM’s good and consistent data.

Step 5: Determine the most efficacious MDM implementation style to support the relationships for data exchange between the MDM Hub and all participating enterprise systems.

Step 6: Select a flexible and versatile MDM solution that can govern and link any sharable enterprise data and connect any business domain, including reference data, metadata and hierarchies.

With advancement in AI and computing power Artificial Intelligence (AI) driven solutions, the demands for an underlying data management system are quite challenging: On the one hand we are talking about the typical 4 V’s of big data — volume, variety, velocity, and veracity. But on the other hand, data quality and data governance are playing a crucial role as well. This is where Master Data Management (MDM) as an enabler for innovative and service-oriented data management architectures comes into play.

Why VA?

When deriving information from data, business analysts and data scientists use 80% of their time to find, clean, and reorganize relevant data sets. Only 20% of the time can be spent on work that is actually value-generating. Additionally, those data-savvy analysts and data scientists currently become more and more a scarce resource and thereby expensive.

We expect the MDM discipline to undergo profound changes due to applications of machine learning and AI. Starting from activities such as data pull, cleansing, curating to mapping using AI based applications to the complete and autonomous definitions and execution of tasks of the MDM organization.

VA has built a framework from various project experience, lessons learned. The framework acts a building blocks and is used to address specific use cases for a customer and build on a wide range of topics spanning robotic process automation, standards mapping, predictive analytics and many more. The frameworks use’s external industry data dictionary to help in the process. The framework has state of the art deep learning models for each of process from data pulling, cleansing, curating and data mapping. Our models are constantly evolving with new data which helps us better addressing any data eventualities in the future.

Hence, we ensure to work on real value cases for organizations. This approach is realized during discovery workshops. VA experts validate the technical feasibility of desired solutions, enlist data needed for the realization of a case as well as desired capabilities.

How do we get started?

We start of with a Discovery Workshop to understand the current or to be data journey. Document the process for everyone’s understanding with the existing benchmarks. A high-level design of the use cases identified is a must. Followed with it we have a Rapid Prototyping Phase where we weave the AI MDM solution for 2-3 identified use cases. Here we test the accuracy of our existing model, we apply existing industry data dictionaries.

Post completion of the prototype we demonstrate the results and install the working prototype at the customer location. The in-depth results are shared with the stake holders.

The Next Steps

Post the rapid prototyping and presentation, based on the customer readiness we go in for a complete implementation of various phases of AI Information Management.

We would happy to talk more and walk through some of our demos do reach out to us at


Add Your Comments

Your email address will not be published.