Data Profiling

This feature scans connected tables to create a detailed profile, highlighting key characteristics like missing values, data types, and distribution. This helps you quickly understand data quality, ensuring accurate and reliable analytics. Profiling information, available in the Data profiling tab, provides insights into table dimensions, empty values, and value distribution, allowing you to spot anomalies and ensure data accuracy.

Review Your Dataset at a Glance

Once you connect a data source to the LDM, you go on to create a new datasets from the available tables. After dataset creation, GoodData automatically gathers profiling information for the dataset. You can review this information in the Data profiling tab that resides in the dataset’s View details dialog:

Data profiling tab in a dataset's View details dialog. The tab is selected at the top of the dialog and shows a table with multiple columns such as order_line_id, order_id, order_status, and date. Summary metrics below the header of each column indicate the presence of empty or duplicate values.

You can hover over the columns to display additional information about the field. Overall the data profiling tab provides information about:

  • Table dimensions
  • Empty (null) values
  • MIN, MAX, AVG and a histogram of values for facts
  • Frequency distribution of unique values for attributes

Spot Anomalies in Your Data

Use the Data profiling tab to do a quick review of the database tables you have connected to ensure your mental model of the data is in line with reality.

Table Dimensions

Something as simple as checking what the table dimensions are might reveal useful insights.

Top section of the Data profiling tab displaying the table dimensions. The text indicates that the table contains 11 columns, 6,000 rows, and 7% empty values.

Are you dealing with annual sales data for your entire company? Is 6000 rows, corresponding to 6000 transactions a plausible number? If you believe you only made 100 sales, or conversely if you believe your company made at least 1 million sales over the past year, this may be an indication that there is an issue with your data.

Suspicious Outliers

Reviewing the profiling information will let you do a quick “sanity check” on your data. For example a suspicious looking histograms can alert you to data outliers:

Price column in the Data profiling tab showing a histogram of values. A tooltip displays statistical details including minimum value of 9, average of 71, and maximum of 20001. The histogram visually emphasizes a small number of high-value outliers.

Do you really expect there to be an item worth $20,001 if your average item price is less than $100?

Missing Values

Fields with missing values will display warnings:

Order lines dataset preview showing the campaign_id column with a warning icon. The summary below the column header indicates that 75% of the values are empty. Other columns such as customer_id and product_id show no empty values.

GoodData can work with empty values, but they can create unseemly gaps in your data when it is visualized, so it is good to know the gaps are there before you start building analytics on top of the data.

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