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This article mainly explains "what are the benefits of enhancing data management to enterprises". The content of the explanation in this article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought. Let's study and learn "what are the benefits of enhancing data management to enterprises?"

For the IT department, managing the unlimited growth of data storage is a great challenge.

The increasing administrative workload forces many IT departments to find ways to reduce the time they spend storing, analyzing, and presenting information to users. Companies have been looking for products to reduce the time their IT support professionals spend maintaining their current systems, thus enabling them to focus on higher ROI activities.

At the same time, vendors of all sizes are releasing new products or enhancing existing ones to capitalize on the interest of the IT community in automated data management activities. These vendors provide time-saving automation, artificial intelligence, and machine learning capabilities to help enterprises improve data integration, metadata management, database management, analysis, and information governance capabilities.

Enhanced data management growth

One of the fastest growing trends is artificial intelligence and machine learning enhancement tools that help employees collect, prepare, store and analyze data and share business insights. According to a recent study by Gartner, manual data management tasks will be reduced by 45 per cent by 2022 due to enhanced capabilities.

Let's take a look at the highly summarized version of the information lifecycle to learn more about enhanced data management. Data collection, archiving, and managing data governance is a strategic plan that defines the roles, rules, processes, and best practices that enterprises need to ensure the security, quality, and proper use of data. Data governance provides a control blueprint to ensure effective management of data at the enterprise level. The goal of data governance is to break the isolated island of data, to promote information sharing among enterprises, and to ensure that enterprise data assets are accurate, consistent across systems and not abused.

A key component of data governance is metadata management. In order to convert the data into information you can trust, you first need to collect the data and record its meaning, business purpose, source, inheritance, background information, and relationships with other elements. This information is called metadata-data about the data. Data experts explore metadata repositories to identify and better understand available data assets.

It is easy to assume that overall data governance and the effective management and archiving of enterprise data assets are time-consuming and expensive.

The information management market now has a wide range of products, from applications that focus on specific management activities to common platforms that try to cover the entire data governance process.

What these tool vendors have in common is that they use advances in automation, artificial intelligence and machine learning to distinguish their products from competitive products. Let's take a look at sample vendor products with a unique set of enhanced data management capabilities.

Informatica . Informatica is an industry leader in providing a variety of data management products. The vendor recently announced the integration of enhanced data management innovations in its intelligent data platform products.

Infogix . Infogix offers a wide range of data governance products, and now the company also provides enhanced metadata management strategies.

IBM . The industry giant continues to add the latest innovations that enhance data management to its suite of data governance products. IBM uses automation, artificial intelligence, and machine learning in its IBM Watson knowledge catalog to promote enterprise data governance.

Data infrastructure management

In a highly competitive market, database management system vendors know that their products must provide functionality to simplify, automate, and improve the quality of management activities. These vendors realize that easy-to-manage and time-saving automation is a key evaluation point for enterprises when comparing products.

Vendors will continue to integrate automation and product generation recommendations to reduce the time spent by database administrators in repeating day-to-day low-ROI management activities.

Operational artificial Intelligence (AIOps) is a collection of big data, artificial intelligence, machine learning, and automation technologies that help data infrastructure support staff solve problems quickly and support large-scale enterprise computing environments more effectively.

Examples of data infrastructure management automation include:

Oracle. An example of using automation to reduce management task time and improve system quality is Oracle's autonomous database.

Microsoft. Managing the data platform includes managing the cloud and local versions of its operating system. Microsoft Operations Management Suite provides a console that automates various management activities for cloud and local OS

BMC Software . In addition to product manufacturers, third-party management tool providers can also focus on automating data infrastructure support activities. BMC's TrueSight Automation for Servers is a good example of how third-party product providers can use enhanced data management capabilities to reduce management time and improve support quality.

Data analysis and presentation

In the early stages of data analysis, it was clear to everyone in the IT industry that data scientists spent too much time collecting, storing, and preparing data for analysis, which could actually be used to analyze information to generate business insights.

Analytical tool vendors quickly learned that if they want to improve the competitive advantage of their products, they must provide automation, artificial intelligence and machine learning capabilities to help data scientists prepare data for analysis and guide them in the process of data exploration.

At present, the industry buzzword describing these functions is enhanced analysis.

Enhanced analytics uses AI, machine learning, and automation to improve data acquisition, preparation, and analysis, with the aim of making it easier for data scientists to generate and publish high-quality insights. Enhanced analysis can also help experts and citizen data scientists explore data more effectively by generating recommendations to guide them through the data analysis process.

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