Collating and Formatting Data
Before any analysis can happen, data needs to be collated from correct sources and formatted so it follows clear organisational standards. Research shows that inconsistent structures and formats make data harder to combine, process and trust (Jagadish et al., 2014). Collating and formatting ensure the dataset is clean, consistent and ready for use.
Technique Overview
Collating and Formatting Data Definition
Collating and formatting data is the process of gathering datasets from approved sources and restructuring them into a consistent, standardised format that follows organisational rules. This includes applying correct data types, clear labels and appropriate file formats so the dataset is ready for reuse, sharing and analysis. Creating consistent structure is essential because variations in schema, naming and formatting can significantly reduce data quality and make later processing more difficult (Jagadish et al., 2014).
Collating and Formatting Data Description *
* The full technique overview will be available soon. Contact us to register your interest in our business management platform, and learn all about Collating and Formatting Data.
Business Evidence
Strengths, weaknesses and examples of Collating and Formatting Data *
* The business evidence section is for premium members only. Please contact us about accessing the Business Evidence.
Business Application
Implementation, success factors and measures of Collating and Formatting Data *
* The business application section is for premium members only. Please contact us about accessing the Business application.
Professional Tools
Collating and Formatting Data videos and downloads *
* The professional tools section is for premium members only. Please contact us about accessing the professional tools.
Further Reading
Collating and Formatting Data web and print resources *
Collating and Formatting Data references (4 of up to 20) *
- Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R. and Shahabi, C. (2014). Big Data and Its Technical Challenges.Communications of the ACM, [online] 57(7), pp.86–94. doi:https://doi.org/10.1145/2611567
- Hernández, M.A. and Stolfo, S.J. (1998). Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem. Data Mining and Knowledge Discovery, 2(1), pp.9–37. doi:https://doi.org/10.1023/a:1009761603038
- Provost, F. and Fawcett, T., 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".
- McKinney, W., 2022. Python for Data Analysis: Data Wrangling with Pandas & Numpy.
* The further reading section is for premium members only. Please contact us about accessing the further reading.
Learn more about KnowledgeBrief Manage and how you can equip yourself with the knowledge to succeed on Collating and Formatting Data and hundreds of other essential business management techniques