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Data Blending

Data Blending

What is Data Blending?

Data blending is the process of combining data from multiple sources into a functioning dataset for further analysis and calculation.   It brings in additional information from a secondary data source and displays it with data from primary data source.  Unlike joins, data blending keeps the data sources separate and simply displays their information together. This is ideal when the data is at different levels of granularity.  While each data source is queried independently, the results are aggregated to the appropriate level and visualized altogether.

Instead of using sophisticated SQL script or Advance Excel Formula/VBA, Data Blending Tools, in particularly for handling large and complex data sets, used to play an important role in helping users blend, including cleansing and mash up, the data from different sources, eg. spreadsheets, web analytics, application systems, databases, etc.
It can help technical or non-technical users to get rapid results for numerous dimensions.  Some Data Blending Tools also provide additional features like visualization, dashboard.

What are the benefits of applying data blending tool?

  • Data Blending Tools allow both technical and non-technical users to dynamically combine and visualize data from multiple heterogeneous sources without any upfront integration effort.
  • Data Blending Tools provide user-friendly visualization tool (code free) for building an intuitive workflow for data blending rather than complex syntax in SQL/VBA scripts.
  • It enable users to automate time-consuming data manipulation tasks and perform repeated blending with different sets of parameters.


What are the common data blending tool applications?

  • Data Acquisition: Data Blending Tool assists data acquisition and identification of the required data.
  • Data Joining:     It joins acquired dataset which the edited data is combined for further analysis.
  • Data Cleansing:     It transforms the acquired dataset into a functional format.  Bad/irrelevant data is removed or corrected during this step. 
  • Data Analysis:     It helps organization to connect all possible dataset from various sources for data analytics purpose and provides useful information and insights for decision making.
  • Data Modeling:     It helps to define the data modeling process within a user friendly environment via graphical visualization capability.


What do we use data blending in HKUST?

Financial Affairs Working Group (FAWG) Cost Allocation

Finance Office applies Data Blending in building its Cost Allocation model to satisfy the complex FAWG requirements which demand considerations of both financial data and non-financial data such as building and room areas, numbers of undergraduate and postgraduate students and classroom usage information, etc.

Predictive Analytics

Data blending is also applied for predictive analytics of student performance (SSCI/MATH, LANG).