Master Data Management (MDM) 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 time and effort bringing in data from various sources or systems. Having moved 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 metadata management are soon heading to obsolescence in a phased manner.


With advancement in AI and computing power, Artificial Intelligence (AI) based MDM solutions are now more feasible, though 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 while balancing the needs mandated for data quality and data governance. This is where Master Data Management (MDM) as an enabler for innovative and service-oriented data management architectures comes into play.


When deriving information from data, business analysts and data scientists use 60-80% 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, these data-savvy analysts and data scientists are increasingly becoming a scarce resource and thereby more expensive, a reason why a solution like AI-MDM that helps saves costs and time is gaining more importance.

The Vision Insight AI-MDM solution is the synthesis of knowledge garnered from the implementation of several projects implemented in Data Engineering and fine-tuned on the basis of valuable lessons learned and the expectations from such a framework both from the technical and business view. The framework acts a building block and is used to address specific use cases and build on a wide range of topics spanning robotic process automation, standards mapping, predictive analytics and many more. The frameworks use client specific data taxonomies to make it relevant for every client. The framework has state of the art deep learning models for each stage of the process that includes 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.


We start off with a Discovery Workshop to understand the current or to be data journey and document the process for understanding with the existing benchmarks. A high-level design of the use cases identified is a must, followed by 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 and apply existing industry data taxonomies.
Post completion of the prototype we demonstrate the results and install the working prototype at the client location. The in-depth results are subsequently shared with all stake holders.


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.

If you would like to connect and explore further or address your data woes, please reach out to us: