Data Quality, Metadata, and Information Lifecycle Management

Introduced here is a rigorous, evidence-based analysis that synthesizes data quality, metadata, and information lifecycle management using a formal, scholarly framework. The focus is on definitions, standards, governance, and lifecycle practices, supported by recognised data quality dimensions, metadata standards, and compliance considerations. Data quality related to both integrity and trustworthiness is an essential foundation for metadata. Metadata adds important context to data, information and knowledge to aid discovery, use and understanding; its governance and stewardship are key concerns of in any organisation. Information lifecycle management considers actions to be taken on assets during their lifecycle from creation through to disposal. The information lifecycle varies across asset type, and policies and procedures to define retention, archiving, and asset disposal provide organisations with essential support for compliance obligations.

Why is data quality important? Poor quality data increases risk and compliance costs, decreases revenue, reduces productivity and wastes time and resources. The lack of information quality increases operational costs and risk management costs. Poorly governed data can be a company’s most poorly developed asset, eroding the value of other assets. Moreover, bad data severely limits the success of business initiatives like data mining, customer relationship management and enterprise resource planning. Hence, there are obvious reasons to be aware of these aspects. Any organisation that has quality issues with its data should want to know. Bad data might need to be managed, sourced from elsewhere, or fixed at the source. Having quality issues may lead to difficulty in doing business. Data governance should ensure quality of the related assets, especially a data-cleansing process.