Using Machine Learning to enhance legacy Data Management

Using Machine Learning to enhance legacy Data Management

Managing data has never been easier through the implementation of AI; things are getting more comfortable to handle. Through different data management consulting services, handling the very architecture of data through AI from different services is a seamless process. But is it any better than machine learning implemented in the same field? While it’s a mixed affair between the positive and negative aspects of ML implemented in data management, let’s understand as to how machine learning is helping legacy data management?

The new era in data management

Extraction of information from the pre-existing data takes about 80% of the time in managing, constructing, and cleaning up data. 20% is the mere value of data that has been extracted. The costs, in turn, significantly rises by the data scientists that carry out the work in data extraction. Machine learning is an excellent way of dealing with the current data architecture was shifting from infrastructure and hardware expenses can be reduced, and usage of existing resources and assets in data can be utilizing the best possible manner.

Implementation of master data management in the AI use case

While it might seem that machine learning can be utilized in every aspect of our lives, it’s evident that it would take up all types of jobs in the coming future. Being said, data management consultancy services often procure the assistance of a pre-defined format through which algorithms and codes can be written to follow these formats. Every step of the way, machine learning utilizes data to be evaluated to derive a specific output.

  • Business goal definition.
  • Master data and data source identification.
  • Metadata and data lifecycle analysis.
  • Infrastructure evaluation.
  • Output validation

Master data and data source identification

Through the application of machine learning in data structuring and formatting, identification of frequently used data can be analyzed quite easily. Several organizations have faced problems with the legacy system, and the usage of algorithms has deemed itself to be another route in data classification and analysis.

But then, machine learning is still not at a stage where it can seamlessly classify which data might be the master data. It’s all based on the intangible criteria of the information that is being stored. Various algorithms can be used to identify the sources for the master data once it has been identified. Providing a feature-rich machine learning algorithm that comprises of in-depth analysis of these sources is beneficial and could provide support to the data dictionary for easy data access.

Metadata and data lifecycle analysis

Implying machine learning in several aspects of identification, strength, and specification of link. It’s necessary as there might be interlinking of linkages involved that might make it difficult for the information to be extracted. Active learning is one way through which a challenge of data extraction can be neglected through labeling of data and obtaining the defined structure of the metadata.

Master data management operation

Towards the end, it’s all about the data management strategy that “wins” in the process. It should neither be a manual process, not a hard-coded entity to be followed throughout the process. But then another way through which the outcome can be positive is through a closed system where property-matching can be availed through surplus calculations of the features from two records.

The procedure might change in the coming future through referential matching, where the data can either be enriched or used to enrich proprietary/internal data. Its all about updating the database continuously based on the inputs and creating a more sustainable data pool where a better way of data matching can be availed.


The very idea of utilizing machine learning to enhance legacy data management is quite a remarkable option in handling the data and providing seamless user interaction with the data. The data management strategy is slowly thriving to be a “self-configuring” were detecting the changes being made to existing data can be analyses and verified, thorough the various algorithms for the ongoing changes.

Although machine learning is on the brink to bring something more significant to the table, master data management is the only option for enterprises to elevate their data quality. While master data management its truing out to be a crucial aspect in handling information from a single area of the company, growing to hold an entire enterprise’s data is quite the challenge that machine learning is still is not capable of bearing.

Author Bio

Sophia Wilson is a professional data management expert working with EWSoluitons, A leading world-class data management solutions company. She loves to write about different types of data solutions.

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