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• Project Owner
• Creator

In this article

1. Record Level/Respondent Level
2. Summary Level/Aggregate Level Data
3. Column-based Data Files
4. Row-based Data Files

1. Record Level/Respondent Level Data

• Each record represents a respondent, a log entry, a sales transaction, etc.
• Analyzing the count of records is relevant - i.e., How many respondents?

2. Summary Level/Aggregate Level Data

• Summed and/or categorized data that can answer research questions about populations or groups of organizations.
• The data has been compiled from record-level data.
• Analyzing the count of records is irrelevant.
• The analysis only has meaning when a measure is applied, e.g., scores, GRPs, Spend, etc.

The process of importing aggregate data into Harmoni is the same as for respondent-level data. If the respondent and aggregated data are in the same project, you need to ensure that all analyses use appropriate bases, e.g., ensure the base for the survey data doesn’t now include the aggregate record counts. 

Dashboards can include analyses from different projects, so if the data is feeding a separate analysis on the same page, it doesn’t have to be in the same project. Please note that if you want to apply page-level filters, they will need to be available in all analyses on the page, so you would need to create ‘dummy’ filters (i.e., have the same variable and element labels but include all records in them) to apply the filters to the aggregate data.

You need a field in the data for each value you want to report and each cut you want to report it by.

The data drives the possibilities, so if you want to have the filters change the data, you need to have the results for each of the filters within the aggregate source.

For example, if you are reporting the Share of Voice for five brands, the source data could look as simple as:

In Harmoni, Brand would be a standard categorical axis, and you want the Value as a Measure. You could then drag Brand into the down and Value into the measure drop zone. The Brand 3 row would then show 900 in the counts (123), and when you select ∑%, you’d see the Share of Voice, i.e., 45% (900/2000), where 2000 is the sum of all the values. 

Learn more about calculation types.

If you also need to slice the result by another dimension, it is just a case of adding that to the data, e.g., Month.

You would now have two standard axes and the Value as a Measure in Harmoni. You could then drag Brand into the down, Month into the across, and Value into the measure drop zone. The Brand 3 row would then show:

• Total Column
  • ∑ = 1400
  • ∑% = 38% (1400/3700)
• Jan 2021 Column
  • ∑ = 900
  • ∑% = 45% (900/2000)
• Feb 2021 Column
  • ∑ = 500
  • ∑% = 29% (500/1700)

Keep in mind that in an analysis from aggregate data, the counts (and therefore %) are the count of records, which is typically meaningless. It is just the calculation types that look at the values that will be useful, i.e. ∑, ∑%, AVG, etc.  

Learn more about calculation types.

3. Column-based Data Files

Column-based file formats organize the data by field, keeping all the data associated with a field next to each other. Column based files are the most common file formats and can provide performance advantages when querying data.

Harmoni accepts column-based data files for all uploads or direct connections.

Learn more about column-based data.

4. Row-based Data Files

Row-based file formats organize data by record, keeping all the data associated with a record in the same row. Row based files are the traditional way of organizing data.

Harmoni accepts row-based data files for direct connections using SQL Server. The default setting is column-based, but you can use the row-based option if required.

Learn more about using row-based data.

Where to from here?

Learn more about Harmoni data sources:

• Add data to your project - Upload or Connect to data sources
• Best practice - preparing your data before importing into Harmoni
• Best practice - preparing your data before connecting to Harmoni.