5 Ways To Automate Data Management With Machine Learning

Businesses, nowadays, invest significantly in managing their information and mining it to get valuable insights into their functioning. Information management has become an integral part of the operational structure of all enterprises. However, the vast amount of information needing to be processed quickly has forced them to look at ways to automate data management with machine learning and other innovative technologies. Usually, machine learning applications are seen supporting analytics program and helping the predictive analysis initiative at organizations. However, the technique can be successfully applied to optimize the performance of metadata management solutions as well. This helps in conducting a number of repetitive and time-consuming tasks quickly. Moreover, the introduction of innovative technology also helps in optimizing the performance of the existing system. Let’s see how machine learning can be a game-changer in the data management field.

1. Automatic And Versatile Cataloging Of Data Elements

Enterprises are creating huge volumes of information at breakneck speed. Their databases contain assets belonging to varied types. This makes it essential for businesses to have an organizing system which helps them in storing their elements efficiently. Data cataloging is an essential exercise that most organizations use to sort and categorize their assets. A catalog contains all the vital details about each element present in their digital ecosystem. The information helps in defining each item in a clear manner and helps users locate the desired element easily. An asset can be sorted based on a domain, source, lineage, compliance requirement, or any other metric chosen by the organization. A system will need to run through multiple data sets, columns, sources, etc. to get a clear understanding of an element and define its metadata correctly. This can be done by using tools with in-built machine learning capabilities. The solutions will use algorithms to do the cataloging process automatically while making sure that business rules are applied in the process. This will be helpful in creating well-organized catalogs providing a clear definition of the assets to interested users.

2. Establish Data Provenance And Record Lineage Of Assets

Information is constantly transformed at enterprises during various processes and it is easy to lose track of its origins. The situation gets complicated with the increasing use of analytics. In order to get more in-depth knowledge, businesses are encouraging the independent use of analytics solutions at all levels. This gives rise to a unique situation wherein small data silos are being created where individual users are storing elements required by them. These items undergo a transformation and may not be understood by another stakeholder. It becomes essential for corporations to establish the provenance of their assets and efficiently record the lineage of each item. It will be difficult for human resources and even traditional digital systems to create a consolidated view quickly by going through various versions. However, machine learning algorithms can help automate data management by running through large datasets quickly. Tools with machine learning capabilities will scan items to chart the path taken by them during their journey at the organization. This will help them in establishing the origin of assets and recording data lineage perfectly.

3. Enable Efficient Metadata Management

Organizations are sitting on a huge pile of versatile data. Even small businesses store information belonging to various aspects of their enterprise like clients, vendors, employees, sales, marketing, etc. On top of that, all the data is present in a variety of file formats not restricted to documents only. There can be spreadsheets, videos, images, and a host of other file types present at any given moment. Data management involves providing a consistent definition of the elements irrespective of their nature and storage composition, across the organization. This helps users in easily locating an item for executing their assigned task. Metadata management, therefore, becomes a vital part of the entire management process. It provides easy pointers to stakeholders for finding the relevant assets and enable efficient resource discovery. Machine learning-powered solutions can run through the information to suggest the best metadata structure for a specific element. This will help in creating efficient metadata without any scope for human error.

4. Spotting Errors To Maintain Data Quality

Raw data undergoes cleansing and enrichment to transform into sharable valuable information. However, errors can easily creep into elements at any stage of their lifecycle because of incorrect handling. A governance program is, therefore, essential for supporting the information management initiative. The monitoring group keeps a close watch on all the assets as well as the processes they are a part of. The governance teams can ease their workload by using solutions with machine learning algorithms. Such tools train to identify anomalies and send alerts whenever they spot one. This is similar to the job being done by traditional tools. However, the pace at which machine learning-enabled programs can function is greater. Properly trained systems also eliminate the risk of human error and can help maintain data quality more efficiently.

5. Accurate Mapping Of Data From Source To Target

Information can be valuable only if the users are able to understand it clearly. Problems can arise because of different access systems at different points. For instance, the field for an element in a source system may not be the same in the warehouse system. Data mapping from source to target helps in resolving such issues. Machine learning can help in automatic and accurate data mapping for integrating information successfully.


These are just a few ways to automate data management with machine learning and reduce the workload of human resources. The technology will be helpful in reducing human errors and making an information management program more efficient.

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