We think Philip Russom’s argument that data in poor condition leads to poor decision-making – is spot on. Russom, a senior manager of research at TDWI, explains “The Six C’s of Trusted Data” – that data must be complete, current, consistent, clean, compliant and collaborative.
In my last column for TDWI Experts in BI, I defined “trusted data” as:
Data that is drawn from carefully selected sources, transformed in accordance with data’s intended use, and delivered in formats and time frames that are appropriate to specific consumers of reports and other manifestations of data.
These and other data properties assure that data is trustworthy from a technical viewpoint, as well as trusted by users who consume the data through reports and applications. Trust is important for data. Without trust, users may ignore supplied data and build their own data stores. Data in poor condition can lead to poor decisions.
In this column, I will drill deeper into how to achieve trusted data. A base assumption is that the problems resulting from non-trusted data can be avoided by following modern best practices in data integration, plus related disciplines such as data quality, data profiling, master data management, and metadata management.
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