In any organization, as new data sources emerge from various customer touch points
, being able to leverage them to create a master customer profile in a unified repository is key towards providing better products and services, and at the same time increasing loyalty and reducing churn.
Organizations would like to leverage the wealth of data created within their enterprise and generated across their network, for operational and commercial use cases. Using this data as part of the digital transformation program enables better customer satisfaction and promotes sales of existing and emerging products through enhanced merchandising of goods and services. This type of initiative requires creating master records using a Master Data Management (MDM) approach.
It is the goal of any MDM solution to enable organizations and their partners to both identify and know their customers and products better in order to provide:
● Better customer service
● Make better bespoke decisions for customers
● Identify further opportunities for ancillary sales
● Identify customer preferred interactions and touch points
Machine learning techniques help integrate customer data silos even in the absence of unique Identifiers from various operational systems. Such systems can use probabilistic matching for record linkage, data clustering and classification techniques along with reinforcement learning for automation on scale out platforms to add significant value to how data can be leveraged as an asset.
In order to deliver MDM functionality on a big data scale, Zaloni offers data mastering as part of the Arena DataOps platform. Arena
provides a Spark-based scale out implementation for matching, linking and mastering, with support for pluggable machine learning libraries that will enable end users to master customer, product and additional data domains using a set of consistent processes and methodologies. The model is flexible based on an organization’s business requirements and does not require a specific type of data model for the data entities to be mastered. Spark-based
machine learning has several advantages over traditional data matching. It matches all types of data domains, it has “live” training that provide unlimited flexibility, and it scales to volumes that weren’t previously attainable. The end result is an agile master data management capability