The healthcare and life sciences vertical is expected to leverage the benefits of data quality tools substantially. The vertical has always been overloaded with data, chiefly from patient, clinical, claim, hospital system, financial, pharmacy, and most recently from wearable technology sources. The industry is pushing toward generating electronic health records and periodically updating reporting methods and data storage. The federal government is offering incentives to the hospitals to trade off paper medical charts with electronic health care records. With the records of the patients being captured digitally and getting stored on-premises or on cloud, any vulnerabilities in the data can be potentially hazardous. Thus, adopting a robust data quality program will be vital for this ever-growing industry.
Health insurance companies are also suffering huge revenue losses owing to poor healthcare data quality and data integrity within its critical business processes such as claims, billing and payment, enrolment, and membership. The healthcare industries need to comply with Affordable Care Act (ACA) but are grappling with the challenges of hidden data inaccuracies and lack of data integrity between numerous disparate systems. Data profiling and data matching features can come handy to tackle these challenges.
Frequently Asked Questions
What is the expected CAGR for the Data Quality Software market?The overall data quality tools market is expected to grow at a CAGR of 17.7% from 2017 to 2022.
What is the purpose of deploying data quality solutions?Data quality software refers to a wide range of tools and services specifically designed to deliver comprehensive and precise data to organizations. Data quality software vendors offer a broad range of functions and capabilities, which include data cleansing, profiling, parsing, monitoring, and enrichment. The data quality software allow organizations to comprehend, standardize, and monitor the data over the course of its lifecycle, ensuring continuous operation within the system. Ensuring data quality should be a paramount concern to the authorities, as a good dataset within an organization can be a key enabler to gain competitive advantage in the market by conducting better analysis and craft business strategies that will have long-term implication on realizing organizational goals. The 6 key considerations of data quality, which every enterprise should seek for, include consistency, conformity, completeness, uniqueness, accuracy, and integrity of data.
Who would need to deploy Data Quality solutions?Data quality solution providers, Governance, Risk Management, and Compliance (GRC) solution providers, Consulting companies, Government agencies, Risk assessment service providers, Investors and venture capitalists, Value-added resellers, Small and Medium-Sized Enterprises (SMEs) and large enterprises, Third-party providers, Consultants/consultancies/advisory firms, Support and maintenance service providers and Technology providers etc.
What are the major application areas where Data Quality Tools prove to be effective?Data quality tools are generally effective in four areas: data cleansing, data integration, master data management, and metadata management. The tools generally identify errors with the help of algorithms and lookup tables. They also helps in managing multiple tasks, that include validating contact details and mailing addresses, data mapping, data consolidation associated with ETL tools, data validation reconciliation, sample testing, data analytics etc.