Data Quality Management Market Trends and Opportunities by Types and Application in Grooming Regions, Comprehensive Growth 2023 to 2030 | Information, Talend, Microsoft

Data Quality Management Market 2023 Report is Made by Executing a Superb Research Process to Gather Key Information of this Global Data Quality Management Market. The Analysis is Dependent on Just Two Segments, Especially, Chief Research and Extensive.

The Data Quality Management (DQM) Market is the market for products and services that help organizations improve the quality of their data. This market includes a wide range of products and services, such as data cleansing tools, data validation tools, data governance software, and data quality consulting services.

The market is expected to grow at a CAGR of 10.4% from 2022 to 2028, reaching a value of $27.7 billion by 2028.

The expansion of artificial intelligence and machine learning has led to a huge increase in the data quality management (DQM) market. To increase efficiency and efficacy, processes like data profiling, cleaning, and validation are being automated using AI and ML. The need for DQM solutions is also being driven by the significance of data governance, which assures data quality, correctness, and usefulness. Demand for DQM solutions is also furled by compliance with laws like GDPR and SOX. Additionally, the demand for DQM solutions that guarantee data quality and deliver precise insights for decision-making is being driven by the increasing demand for data-driven insights.

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Top key players:

Information, Talend, Microsoft, Syncsort, IBM, Pitney Bowes, Informatica, Oracle, SAP, SAS Institute, Experian

Sample Data Quality Management Market, By Type:
On-Premises
SaaS

Sample Data Quality Management Market, By Application:
BFSI
Healthcare
Retail
Telecommunication
Government
Transportation & logistics
Education
Others

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The Data Quality Management Market into districts, portraying every area’s serious scene.

North America (the United States, Canada, and Mexico)
Europe (Germany, France, the United Kingdom, Russia, and Italy)
Asia-Pacific (China, Japan, Korea, India, and Southeast Asia)
South America (Brazil, Argentina, and Colombia)
The Middle East and Africa are among the locales examined in the review (Saudi Arabia, UAE, Egypt, Nigeria and South Africa).

These experiences help in the improvement of plans and the production of new open doors for accomplishing remarkable accomplishments.

Data Profiling: DQM tools offer data profiling capabilities to analyse and assess the quality of data within an organization. This involves identifying data anomalies, inconsistencies, and inaccuracies.

Data Cleansing: DQM solutions provide data cleansing features to automatically or manually correct, standardize, and cleanse data to eliminate errors, duplicates, and inconsistencies.

Data Validation: DQM tools enable data validation by applying predefined rules and validation checks to ensure that data meets specified quality criteria and business rules.

The years examined in this study are the following to estimate the Data Quality Management Market:

History Year: 2017-2021
Base Year: 2022
Estimated Year: 2023
Forecast Year: 2023 to 2030

Key Features:

Image Analysis and Processing: CAD systems use advanced image analysis and processing techniques to enhance and interpret medical images. These systems can identify subtle changes or abnormalities that might be missed by the human eye.

Automation: CAD systems automate the process of image analysis, reducing the dependence on human expertise and potentially speeding up the diagnosis process.

Assistive Tool: CAD is designed to assist radiologists, pathologists, and other medical professionals in their diagnostic tasks rather than replace them. It provides a “second pair of eyes” to help detect anomalies.

Multi-Modality Support: CAD systems are capable of working with various medical imaging modalities, including X-rays, mammograms, MRI, CT scans, and ultrasound, making them versatile tools in healthcare.

Early Detection: One of the primary advantages of CAD is its ability to detect abnormalities at an early stage, potentially leading to earlier diagnosis and treatment.

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The research methodology for Data Quality Management (DQM) market involves defining research objectives, conducting secondary research, identifying the target audience, designing primary research, collecting and analysing data, and writing a research report. The primary research method includes surveys, interviews, focus groups, and expert interviews. Surveys gather data from a large audience, while interviews provide in-depth insights into the motivations and decision-making processes of DQM buyers. Focus groups gather feedback from a specific group, while expert interviews provide insights from industry experts about the DQM market. The choice of research methods depends on the study’s objectives and available resources, but a variety of methods can provide a comprehensive understanding of the DQM market and its potential.

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Table Of Content:
1. Data Quality Management Introduction
1.1 Objectives Of The Study
1.2 Data Quality Management Market Definition
1.3 Overview
1.4 Currency And Pricing
1.5 Limitations
1.6 Markets Covered
2 Market Segmentation
3 Executive Summary
4 Premium Insight
4.1 Pestel Analysis
4.2 Porter’s Five Forces Model
4.3 Global Data Quality Management Market Analysis
4.4 Patent Analysis
5 Pipeline Analysis
6 Industry Insights
7 Data Quality Management Market Overview
8 Global Data Quality Management Market, By Type
9 Global Data Quality Management Market, By Application
10 Global Data Quality Management Market, By Region
11 Global Data Quality Management Market, Company Landscape
12 Swot Analysis
13 Company Profile
14 Questionnaire
15 Related Reports

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