Format Transformation – synthesizing from custom examples
Data analysis is hard! Practitioners have testified time and again that extracting the much promised value out of data through analysis is no free lunch. There is a famous 80/20…
Data analysis is hard! Practitioners have testified time and again that extracting the much promised value out of data through analysis is no free lunch. There is a famous 80/20…
Format transformation is the process of carrying out changes to the representation of data values, such as values of a table column, with a view to reducing some of the…
Data preparation is the process of discovering, cleaning and integrating data sets for analysis. Although the cleaning that may be required varies from application to application, the need to address…
Wrangling Open Government Data: A Case Study with Data Preparer Open Government Data Governments collect and analyse significant amounts of data, for example for monitoring the effect of their policies…
Data Preparer - A market validation journey The Journey In January 2019, our team received a grant under the iCURE programme for a market validation of our business proposition. The…
Data Preparer – Research Prototype to Product Origins Research prototypes exist to provide proof-of-concept demonstrations, and to allow experiments to be carried out that evaluate both individual components and the…
The Data Value Factory has recently released its Data Preparer system. Data Preparer aims to significantly reduce the manual effort involved in developing programs for data integration and cleaning. What…
A possible process Data preparation preprocesses data for analysis. There is no widely accepted data preparation process. Furthermore, where processes are discussed, this is often in rather an abstract way…
Data preparation is not new. However, although data preparation platforms differ in how data preparation activities are specified, there tends to be fairly broad agreement as to what steps are required. The following…
Data preparation challenges Data preparation is a necessity for many analysis tasks, but is identified as taking 60% to 80% of a data scientist’s time. Why is this percentage so…