Augmented Data Management can provide valuable capabilities to support MDM initiatives by leveraging advanced technologies, increasing operational efficiency, and improving decision-making.
It may sound sensationalist, but it is inarguably true that the speed of technological evolution is at an all-time high. However, this rapid pace of change is exciting enterprises on the one hand; on the other hand, it is making them nervous. With AI (Artificial Intelligence), ML(Machine Learning), and NLP (Natural Language Processing) impacting every business, no industry is ever going to be the same again. Old methods are giving way to new techniques of doing things that are faster, simpler, and a lot more effective. Master data management (MDM) is no different and is undergoing significant changes. The MDM, which existed for the past two decades and was seen as time-consuming, costly, and overly dependent on IT, is now past us. The era of augmented data management (ADM) is now upon us. Here we look at how master data management (MDM) is getting revolutionized by ADM and what advantages it offers to businesses.
Augmented data management (ADM) is an approach wherein AI, ML, Data automation, Graph, NLP are applied for data management and optimization. It complements human intelligence by aiding human capabilities and cognition to manage data to increase efficiency, decrease costs, enhance data quality, improve governance and decision-making, while doing away with repetitive tasks and creating more value in less time. It is meant to empower IT and data engineering teams as well as enable business users to take the lead in initiatives related to insights and analytics. Being cloud native, ADM offers cloud readiness and scalability, thereby offering the ability to handle a vast volume and variety of data.
Augmented data management techniques are already being used to address time and/or labor-intensive tasks, for instance, profiling data, eliminating data redundancies, applying policy rules, configuration management, and modifying performance. However, there are more reasons to implement it.
Augmented data management is brilliant at detecting outliers and anomalies and suggesting modifications, for instance, pattern recognition, automatic spotting, and concealing personally identifiable information like passport, date of birth, etc. Besides, as opposed to traditional data management, which involves collecting, storing, securing, controlling, and carrying out jobs such as filtering, copying, and tagging, ADM takes out a whole lot of manual tasks from the equation.
Some of ADM’s best practices for MDM relate to the following:
ADM fits organically with MDM by letting organizations address two of their primary goals, i.e., to ensure optimized business operations for efficiency and driving business growth. Compliance with changing regulations, risk managing ability, and data governance are certain other areas that ADM lets master data management improve. From a granular perspective, the chief benefits of augmented MDM are:
ADM’s adoption in MDM is growing at a tremendous speed. Some of the trends which have already taken root, and are here to stay, are:
Augmented data management helps identify associations among large datasets, thereby expanding the capabilities of MDM, especially when it comes to data and analytics. MDM providers have started combining augmented MDM to their solution to recognize associations among previously unknown data and have acquired the ability to tie them back to master data objects. It has unlocked a whole new world of contextual master data management where data objects are depicted within a particular context or business process.
With the backing of graph, AI/ML capabilities augmented MDM is aiding data management leaders to ensure maximum leverage can be taken from their master data and sharper contextual insights can be gleaned more efficiently and quickly.
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