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Data quality process at OpenHealth
Data quality process at OpenHealth

This article details the stages of data quality processes at OpenHealth

Maxime LE MOIGNIC avatar
Written by Maxime LE MOIGNIC
Updated over a week ago

Data quality calls on our ability, as a healthcare data management company, to maintain the sustainability of data over time. This notion covers a set of criteria such as:

  • Completeness of the data

  • Their validity

  • Their precision

  • Their consistency

  • Their availability

  • Their news, or last update date

  • Traceability

Completeness of the data
Our data quality teams constantly check the completeness of the data, that is to say the absence of ' hole 'in our databases. For example, we check that our data sources are transmitting on a regular basis, and that there is no partial transmission. These controls are all the more important in the context of data refreshed on D + 1, which is the trademark of OpenHealth

Data validity
Data validity refers to the work of normalizing data: input format, output format. A typical example is that of product and label codes, where OpenHealth must constantly manage reports of new codes and labels

The data displayed in our analytics platform The HUB must be perfectly accurate. This is why our sources, territories, indicator sheets and objects are subject to completely transparent specifications based on our knowledge base. At all times, all users must be able to understand precisely how our indicators are calculated, the data sources and the limitations of our data

OpenHealth continuously calculates the consistency of our data in the database as well as in the HUB. Examples of control: the sum of the sectors must be equal to the national level, the indicators in Sales & Marketing Essential must give similar results in Sales & Marketing Advanced, etc.

The availability of data and the ease of access of data and its documentation to the end user. Our Customer Success Center is at your entire disposal for any questions relating to data availability

At OpenHealth, we believe in real time. Our data is therefore updated on D + 1. We also guarantee the recalculation of back-data, which is the possibility of recalculating past data in the event of modification of a calculation parameter of your data (eg: I add in September 2019 to my market a product launched in August 2018 . OpenHealth gives you the sales history since August 2018 and not since September 2019 as some panelists do)


At any time, each user can find perfect traceability of our data, even retroactively. OpenHealth undertakes to share its technical specifications and calculation rules in a fully transparent manner.

Examples of data quality check at OpenHealth:
Sales-decomposition process
Level 1 investigation
Level 2 investigation

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