How can you use data visualization to monitor data quality in a warehouse?

One way to use data visualization to monitor data quality is to create a dashboard that displays key metrics related to data quality. For example, you could create a dashboard that shows the number of missing values in each column of your data, or the percentage of records that have been updated in the last week. By monitoring these metrics over time, you can quickly identify any changes in data quality and take action to address them.

Adicionalmente, um dos objetivos da visualiza??o de dados é transformar dados em informa??es, levando contexto e objetivo para quem está visualizando os dados. Utilizar agrupamentos, filtros e tipos de gráficos adequados colabora para uma melhor análise dos dados.

There's a number of ways in which you can design your data quality visualisation strategy. A good example would be utilising an existing, or building into a new, Data Warehouse and writing 'problem' data - poor quality data as defined by business requirements - to exception schema integrated into the transformation process. In doing this you can construct a data model (in keeping with dimensional modelling) around data errors by day and each data error by rule which can then be built within Power BI as an example. Once the data model is constructed and as such can be pushed as a dataset into a Power BI workspace, a dashboard or report can be attributed to visualise data quality and thus enables monitoring over time.

More examples for data visualization for data quality: ?? Outlier detection for time series data. Visualize the moving average of certain groups of data, detect and add outliers. ?? Partition your tables by day to keep the history for a couple of days. Compare the average and sum for numeric columns as well as distinct values for text based columns between days. Visualize the percentage difference as bar charts to spot data anomalies easily. ?? Visualize the amount of null and distinct values for columns. For all cases, I made good experience using Graphite + Grafana as a visualization layer, since it allows for very simple integration of data points. Be creative and make it fun looking at data quality ??.

Here are some practical examples of how you can use data visualization for data quality monitoring: Data Profiling Charts: For categorical variables, bar charts are super useful, whereas for numeric variables, column charts measured along a time axis can give you quick insights. Trend Plots: For numerical data points, observe how values change over a certain time period. This can help in detecting sudden spikes, drops, or any other anomalies. Counts Plot: If you're working with batch data pipelines that process information daily, a line chart mapping the count of data against a time range can be invaluable. This helps in ensuring consistent data inflow and can highlight any interruptions or inconsistencies.