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Multi-Temporal Example from the Travel Industry

In one of our previous blog posts, we gave an insight into the basics of multi-temporality in Data Vault 2.0. This article will give you a short practical example of the topic.

When you book your holidays, you might change something after the initial booking: the duration, the flight departure location, the hotel, the start and/or end date of your journey or might just book some extras. From an operational perspective, these changes are not a problem to deal with, and the expectations are not high.

The data set for your journey will be updated in the source systems’ database, and only the most recent data is shown in the graphical user interface. The employee might see some logs in the history part, if they exist.

That’s usually enough to keep the operational part working. In the business intelligence world, the way to deal with data is a bit different. There, we want to see everything. Each time a record in the source system is updated, we catch all the changes and store them in a separate database.

This means that we introduce a new timeline which provides us new opportunities but also comes with new challenges. Let’s have a look into some example data sets of a source system over time:

Day 1: Initial booking of a holiday trip

Day 2: Correction of the start and end date and a small price adjustment

Day 2: Correction of the start and end date and a small price adjustment

When you have a look into the source system today, you will see that booking B4711 is cancelled, or maybe it is completely gone, and B4712 is the active one (ID = 2, created on day 3). From an analytical perspective you might have some more questions you want to have answered.
For example:

  • What was the estimated revenue for July, looking from February?
  • What was the estimated revenue for July, looking from March?
  • Which dates do I need to use to answer the two questions above?
  • In which month do we have the most bookings for the next summer season?
  • Can we reproduce a report we created some months ago?
  • What if some business dates change? What affects my reports in the past?
  • Where can we prepare the data to answer all these questions? In the data warehouse? In the front-end BI tool? Or somewhere else?

In our webinar “Multi-Temporality in Data Vault 2.0” we will provide you answers to all these questions by using Data Vault entities.

If you are interested to see some more use cases related to multi-temporality: we offer a 2-day class especially for this topic: “Multi-Temporal Data Vault 2.0 Class”

by Marc Finger (Scalefree)

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