Product Code: 66505
The data quality tools market is expected to register a CAGR of 17.5% over the forecast period 2020 to 2025. Data quality tools generally address four primary areas: data cleansing, data integration, master data management, and metadata management. As data quality is a significant stake for large organizations, software companies are proposing increasing numbers of tools focusing on these issues. The scope of these tools is shifting from specific applications (de-duplication, address normalization, etc.) to a more global perspective, integrating all areas of data quality (profiling, rule-detection, etc.).
- Additionally, increasing mobile connectivity and the adoption of IoT across all industries have led to a high data explosion leading to the extraction of data from various sources. This complex data types and formats drives the demand for the data quality tools solutions. According to the Harvard Business Review (HBR), it costs ten times more to complete a unit of work with flawed data, and finding the right data quality tools has always been a challenge. By choosing and leveraging smart and workflow-driven self-service data quality tools with embedded quality controls, one can implement a system of reliability.
- An AI analytics solution addresses data integrity issues at the earliest point of data processing, rapidly transforming these vast volumes of data into trusted business information. For instance, Anodot Ltd delivers the autonomous analytics platform, which is fully automated at each step in the data collection process, such as detection, ranking, and grouping, and provide alerts about changes in key business metrics, like missing data, unexpected data types, nulls, and malformed records, through real-time and large-scale AI analytics solution.
- Many other software companies are leveraging data quality tools based on AI and ML. For instance, in November 2019, Collective[i], one of the largest global networks for prediction and management, announced the "Intelligent WriteBack" solution. This solution automates the time consuming and error-ridden process of humans entering contact and sales activity data into CRM. Presently, large enterprise companies can have a perfect solution without losing productivity, and even more impactful is the dynamic data stream that allows marketing and sales organizations to improve various numbers of processes related to this data source from list management to forecasting.
- Moreover, the manufacturing sector handles multiple streams of data that need to be analyzed to optimize business resources. These industries typically require handling of routine, structured in-factory data, analog data, and information churned out from applications, which include enterprise resource planning (ERP) systems and various process automation and control systems. Maintaining data quality would be of significant value to optimize the supply chain in the manufacturing sector. For instance, additive manufacturing (AM) needs tools to manage data to ensure quality, repeatability, traceability, and reliability, especially in the heavily regulated aviation and medical industries.
- The COVID-19 pandemic is generating enormous amounts of data, which includes large numbers of data about infection rates, hospital admissions, and deaths per 100,000 are available within seconds. However, despite a large amount of data, people don't necessarily have a better view of what's happening on the ground, and the big COVID-19 data sets aren't directly translating into better decision-making. It's also critical to have a solid understanding around the context of the data, such as how it was assembled, metadata around each feature when it was last updated, and it's especially important to investigate the data's quality if it's going to be used for machine learning purposes
Key Market Trends
Healthcare is Expected to Witness Significant Growth
- Data management in the healthcare sector is a complex process. It is composed of several key ingredients: data governance, data integration, data enrichment, data storage, and data analysis. While data processing systems are becoming critical components of operational decision making and individualized treatment processes, poor data quality and management is becoming a primary interference of operational success and is causing significant strain on such methods.
- The most commercial data collection tools in the healthcare industry are enterprise data warehouses (EDWs). They are designed to cluster data from multiple sources into a single, unified, and integrated data repository. The data is embedded within the EDW, so users can analyze the previously fixed data and get more ROI from existing source systems. Moreover, hospitals and care providers are adopting big data analytics and population health management technologies to meet the new healthcare standards' requirements, with the growing demands and expectations of patients.
- For instance, in July 2019, GE Healthcare introduced Edison Datalogue, a new enterprise data management solution, designed to connect uneven data sources and types into a single, secure, and scalable data pool for the staff in the hospitals to collect, share and store patient data across the care delivery network. It is being deployed by hospital systems, including the National Consortium of Intelligent Medical Imaging (NCIMI), and it is the only tool that combines a vendor-neutral archive (VNA), analytics, and collaboration tools under a single platform.
- With the outbreak of COVID-19, the data that the United States is relying upon isn't present in great shape, according to data experts who talked to Datanami. While different areas of the data are better or worse, there are real concerns about the quality and availability of data underlying some of the most critical metrics that are being used to gauge the progress in fighting the novel coronavirus. The healthcare providers, researchers, governments, and business leaders need high-quality, real-time data to make informed decisions around policy, potential treatments, public health guidance, and employee safety where efficient data quality tools are expected to witness a significant surge.
Asia-Pacific Expected to Register the Highest Growth Rate
- In terms of revenue, the Asia-Pacific region is identified as the fastest-growing data quality tools market. This is primarily due to the increasing interest in data quality improvement solutions and a rising focus on data-driven scientific and strategic decision-making practices. With the growth of smart cities and the proliferation of IoT devices, the region is expected to witness an ultra-growth in the future. Further, the start-up culture, flexible government policies, and the flourishing eCommerce business are also significant factors driving the region's market.
- China introduced unified regional gross domestic product (GDP) accounting in early 2020 accounting to boost the data quality. The National Bureau of Statistics (NBS) will lead the centralized accounting of regional GDP after the reform. It will help in the organization of local statistics offices to map accounting methods, map and standardize the unified procedures in accounting, conduct unified accounting, and release the results. The reform can give better play to the statistical supervision and provide more robust data support for macro-decision-making where efficient data quality tools could be used.
- In December 2019, Niti Aayog, India's principal policy think tank, has begun work on a roadmap that will suggest steps to tackle quality issues confronting official data swiftly. It comes as a move by the Ministry of Statistics and Programme Implementation (MoSPI) to address data quality issues of various surveys, introducing checks for data collection apart from increasing the sample size of studies to improve accuracy. The ministry aims to not compromise in any manner on the quality of statistical staff at the field level and also regarding the usage of efficient data quality tools so that the credibility of the whole process is not jeopardized.
- Melissa recently demonstrated its comprehensive suite of big data quality tools and services at the Chief Data & Analytics Officer Singapore, July 23-24, 2019. Melissa's big data quality tools are used to prevent incorrect, incomplete, duplicated, or outdated data from entering enterprise systems, ensuring analytics are fueled with the actionable intelligence necessary for reliable results. In an era of big data and data sprawl, organizations in the region are increasingly using metadata to identify, classify, and gain insights from critical data in metadata; trends are not just about how it's mined, but how it's used and managed.
The data quality tools market is moderately fragmented, with several domestic and international companies offering advanced solutions. Due to the presence of significant vendors in the market, intense competition encourages them to focus on areas to enhance their customer reach across various geographies. Additionally, different data quality tools providers are focusing more on providing a comprehensive set of solutions to their customers to gain increased market traction.
- March 2020 - Talend Inc. announced the availability of Talend Cloud in Microsoft Azure Marketplace, online store providing applications, and services for use on Azure. With the embedded data quality and native integration performance of Talend Cloud, companies can run integration tasks securely across cloud and on-premise environments with only from an Azure account.
- March 2020 - Informatica LLC brings serverless compute to Data Integration Cloud, and among incremental updates are the addition of de-duplication capabilities to the data quality services. The de-duplication was only available on-premises or as part of bring-your-own-license support for running Informatica Data Quality on cloud infrastructure services such as Amazon EC2. The catalog has been enhanced with a wide variety of views for data engineers, business analysts, and data scientists through menus, allowing users to select logical or physical aspects of metadata.
Reasons to Purchase this report:
- The market estimate (ME) sheet in Excel format
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Table of Contents
- 1.1 Study Assumptions and Market Definition
- 1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET INSIGHT
- 4.1 Market Overview
- 4.2 Industry Value Chain Analysis
- 4.3 Industry Attractiveness - Porter's Five Force Analysis
- 4.3.1 Bargaining Power of Suppliers
- 4.3.2 Bargaining Power of Consumers
- 4.3.3 Threat of New Entrants
- 4.3.4 Threat of Substitute Products
- 4.3.5 Intensity of Competitive Rivalry
- 4.4 Assessment of COVID -19 impact on the market
5 MARKET DYNAMICS
- 5.1 Market Drivers
- 5.1.1 Increasing Use of External Data Sources Owing to Mobile Connectivity Growth
- 5.2 Market Restraints
- 5.2.1 Lack of information and Awareness about the Solutions Among Potential Users
6 MARKET SEGMENTATION
- 6.1 By Deployment Type
- 6.1.1 Cloud-based
- 6.1.2 On Premise
- 6.2 By Size of the Organization
- 6.2.1 Small and Medium Enterprises
- 6.2.2 Large Enterprises
- 6.3 By Component
- 6.3.1 Software
- 6.3.2 Services
- 6.4 By End-user Vertical
- 6.4.1 BFSI
- 6.4.2 Government
- 6.4.3 IT & Telecom
- 6.4.4 Retail and E-commerce
- 6.4.5 Healthcare
- 6.4.6 Other End-user Industries
- 6.5 Geography
- 6.5.1 North America
- 6.5.2 Europe
- 6.5.3 Asia-Pacific
- 6.5.4 Latin America
- 6.5.5 Middle East and Africa
7 COMPETITIVE LANDSCAPE
- 7.1 Company Profiles*
- 7.1.1 IBM Corporation
- 7.1.2 Informatica LLC
- 7.1.3 Oracle Corporation
- 7.1.4 SAP SE
- 7.1.5 SAS Institute Inc.
- 7.1.6 Talend Inc.
- 7.1.7 Experian PLC
- 7.1.8 Information Builders Inc.
- 7.1.9 Pitney Bowes Inc.
- 7.1.10 Syncsort Inc.
- 7.1.11 Ataccama Corporation
8 INVESTMENT ANALYSIS
9 FUTURE OF THE MARKET