Financial institutions, insurance companies as well as global asset management services companies have all significantly brought changes in their operations complying with the global regulations such as Basel III, Dodd-Frank, MiFID (Markets in Financial Instruments Directive), EU Solvency II and Volcker Rule in the regions of Europe and North America. Even regulatory reforms have also tightened up in Asia regions. At the same time, the financial sector over the last few years have witnessed phenomenal surge in their data volumes, which officials want to leverage at any cost in the quest to have a leaner organization, improve operational efficiencies, and enhance the revenues for their firms. Sound data quality remains a pivotal parameter in delivering unparalleled results for these billion dollar firms.
Generating volumes of data is one thing but making it to use and harness it to one’s competitive advantage is pretty complex and challenging for financial institutions. The concerns were even testified by a recent SAS sponsored study which revealed 35% of the banks had difficulties in aggregating customer data and managing the requirements that come with it. Supplementing to this, the US Postal Service estimates that 40% of its customer data repository is either incorrect or incomplete. This inaccurate information about customers is a major roadblock faced by the banks, which is jeopardizing their business objectives.
With the sheer volumes of data stored in the repository of the banks databases, not all data will be of intrinsic value to the banks. All the information stored in the bank will not make sense unless cleansed, standardized, validated, corrected, and enriched. Data quality tools thus play a crucial role where functions such as data profiling, data cleansing, and data de-duplication will be used abundantly to gain a wider control of the business and achieve competitive advantage in the market.
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.