Data science is, in many ways, an advanced and sophisticated form of analysis and reporting that is used to produce models for predictive and prescriptive analytics. Data lineage awareness through metadata can be used to communicate about the use of trusted data sources in the analysis. Data quality awareness through metadata is used to tune models to compensate for noise and uncertainty in the data, and to determine confidence levels and confidence intervals in some analytic models.

It may seem that business intelligence (BI) is similar to data science (DS). Data science encompasses predictive analytics, machine learning, data mining, and even parts of what is considered to be artificial intelligence. It is routinely touted as improving revenue, profit, and ROI. It is often presented as automated and able to discover knowledge. These huge impacts and the automated nature of many of its applications make DS particularly dangerous.