Combining Data from Multiple Sources
Combining data from different sources enables analysts to generate complete, accurate, and actionable insights. By integrating information from varied systems, such as databases, APIs, and spreadsheets, organisations improve decision-making, efficiency, and data quality across processes.
Technique Overview
Combining Data from Multiple Sources Definition
Data combination is the process of integrating data from multiple sources, structured or unstructured, into a unified and consistent view for analysis (Dong et al., 2013). It involves extracting, transforming, and joining datasets using relational joins, linking, or automated ETL pipelines to improve completeness, accuracy, and accessibility (Ryan, 2008; Ogunsola et al., 2022).
Combining Data from Multiple Sources Description *
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Business Evidence
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Business Application
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Professional Tools
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Further Reading
Combining Data from Multiple Sources web and print resources *
Combining Data from Multiple Sources references (4 of up to 20) *
- Christen, P., 2012. The data matching process. In Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection (pp. 23-35). Berlin, Heidelberg: Springer Berlin Heidelberg.
- Dong, X.L., Berti-Equille, L., Srivastava, D. (2013). Data Fusion: Resolving Conflicts from Multiple Sources. In: Sadiq, S. (eds) Handbook of Data Quality. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36257-6_13
- Fletcher, R.J., Hefley, T.J., Robertson, E.P., Zuckerberg, B., McCleery, R.A. and Dorazio, R.M. (2019). A practical guide for combining data to model species distributions. Ecology, p.e02710. doi:https://doi.org/10.1002/ecy.2710
- Johnson, T. and Chatziantoniou, D. (2000). Joining Very Large Data Sets. Lecture Notes in Computer Science, pp.118–132. doi:https://doi.org/10.1007/10721056_9
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