Manufacturing industries handle multiple sets of data, which needs to be continuously captured and analyzed to streamline business applications and optimize business resources. The industry handles routine structured in-factory data, along with analog data, images, raw sensor data, and information churned out from applications inside the factory and other sources, which include Enterprise Resource Planning (ERP) systems, manufacturing execution systems, time and attendance logs, supplier information, and various process automation and control systems.
With the manufacturing systems becoming increasingly sophisticated and software-driven, it is imperative for data scientist to use effective data quality tools. All the associate systems such as Customer Relationship Management (CRM), ERP as well as Supply Chain Management (SCM) would have an adverse effect and will suffer owing to poor data quality ingested into these systems. Moreover, maintaining data quality would be of significant importance to optimize supply chain and induce efficiencies in the manufacturing processes. Effective data management program could be highly helpful in reducing the product development costs as well. Having a robust data management program within the processes would help in countering numerous types of risks such as economic, technical, and supplier risks.
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.