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Business Intelligence Architecture in Support of Data Quality
Abstract
Business Intelligence (BI) projects that involve substantial data integration have often proven failure-prone and difficult to plan. Data quality issues trigger rework, which makes it difficult to accurately schedule deliverables. Two things can bring improvement. Firstly, one should deliver information products in the smallest possible chunks, but without adding prohibitive overhead for breaking up the work in tiny increments. This will increase the frequency and improve timeliness of feedback on suitability of information products and hence make planning and progress more predictable. Secondly, BI teams need to provide better stewardship when they facilitate discussions between departments whose data cannot easily be integrated. Many so-called data quality errors do not stem from inaccurate source data, but rather from incorrect interpretation of data. This is mostly caused by different interpretation of essentially the same underlying source system facts across departments with misaligned performance objectives. Such problems require prudent stakeholder management and informed negotiations to resolve such differences. In this chapter, the authors suggest an innovation to data warehouse architecture to help accomplish these objectives.
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