Telecom companies worldwide are grappling with the unprecedented challenge of enhancing and maintaining the quality of their data assets to minimize revenue leakages and reduce their process failures. The latest predictions from CFCA 2015 Industry Survey reveals that telecom operators globally incur an average loss of 13% or USD 294 billion due to numerous uncollected revenue and frauds prevailing in this industry. Poor administration of back office database clubbed with inefficient data quality tools are the major reasons for bringing such upheavals in this industry.
Data quality challenges arises in the industry owing to the sheer complexities in integrating numerous systems and exponential amount of data generated across the industry. The problems get accentuated owing to the severe competition, advancements in technology, and frequent offers offered by other Communication Service Providers (CSPs) operating in the market space.
Adopting data quality tools could help CSPs to identify, cleanse, and enrich their in-house generated data using data-deduplication tools. Clean and enriched data provides better insights to comprehend customer’s demand which could be capitalized by offering customized services ensuring customer retention and establishes higher brand loyalty.
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.