Data Quality / Data Governance

Definition:

Data Quality describes how complete, accurate, consistent and up to date Information in a company is.
It determines whether data can be reliably used for operational processes, automation, and analyses.

Data Governance comprises the Policies, Responsibilities, and Processes, which ensure that data is correctly maintained, approved and documented.
It defines responsibilities (“data ownership”) and determines which sources are considered trustworthy.

High data quality is the basis for effective system integrations (e.g. ERP, TMS, CRM), reliable supply chain planning and data-based decisions.

_{Sources/further links:
Gartner — Data Quality Definition: Data Quality: Why It Matters and How to Achieve It
DAMA International — Data Management Body of Knowledge (DMBOK): DAMA® Data Management Body of Knowledge (DAMA-DMBOK®) - DAMA International®}

_{PwC — DataGovernance Framework: https://www.pwc.com}

Data quality challenges:

• ‍Inconsistent data sources: Information is maintained in parallel in various systems, resulting in incorrect or contradictory data sets.‍
• Lack of responsibilities: Without a clear data governance structure, it is unclear who is responsible for specific types of data.‍
• Manual maintenance processes: Excel lists and email reconciliations lead to version conflicts and a high level of coordination effort.‍
• Lack of transparency: Changes are not comprehensibly documented, meaning that incorrect values go unnoticed.‍
• High communication costs: Business partners have to manually reconcile information over and over again, which leads to delays in transportation, purchasing and billing.

_{Sources/further links:
Deloitte — Data Management Challenges 2023: Deloitte Germany
IBM — The State of Data Quality: What Is Data Quality? | IBM
KPMG — Data Governance Benchmark Report 2024: KPMG International | Home}

Loady's solution:

Loady ensures uniform, verified and verifiable master data along the entire supply chain.
Data on locations, charging points, products and safety requirements are stored in a central, standardized structure maintained and over clear roles and approval processes controlled.

Thanks to integrated versioning and change tracking, every piece of information remains transparent and auditable.
All parties involved — shippers, carriers, recipients — work with the same data source, which eliminates duplicate records and reconciliation errors.

The result: Higher data quality, reduced maintenance costs and consistent information across all systems.