How can you manage data quality issues in PMS integration?

One consideration when defining your standards is what is the expected final output should be. - What data do you need to capture and in what format? Drop down, free text etc... This will be driven by the customers expectations of the final product. So many times, I've seen complex projects and integrations occur without consideration of what the final output should be which results in rework that could have been avoided.

Managing Data Quality Issues in PMS Integration Data Cleaning Protocols Establish thorough protocols for cleaning and validating data. Regular Data Audits Conduct routine audits to identify and rectify inconsistencies. Standardized Data Formats Enforce standardized formats to maintain consistency across platforms. User Training Programs Provide training to users on accurate data input and verification. Data Governance Framework Implement a robust governance framework to oversee data management. Here’s what else to consider: Collaborate with IT specialists to enhance integration processes. Foster a culture of data responsibility among users. Monitor and address issues promptly to prevent cascading effects.

I indeed found crucial to first 'define data you need'. But the point is also to identify who is the 'YOU' in that sentence: too often, the IT group decides upon the needs, when the requirements should come from the business. And even within the business, both the 'data producer' (the one who will generate the data in the system) and the 'data consumer' (the one who will make use of the data) must be consulted. A good place to look for these requirements are the process design documents.

I believe it is important to speak about "Integration" which means that data elements about a given physical asset possibly held in different systems can easily be reconciled. It should as much as possible be avoided to DUPLICATE data across systems even if this seems a good way to 'keep ownership of MY data': it will always lead to discrepancies.

Data cleansing and validation rules should be applied on both 'the river' (the process to create/update data) and 'the lake' (the existing data in the warehouse).

Very often, individual data is combined into more complex PIs or KPIs used by management to take decisions; Having a clear understanding of your 'basic data' quality can let you build a 'trust indicator' for the KPIs (sort of 'metadata' of the KPI) derived from the basic data, hence warning leaders that their decision might be based on 'questionnable' data.