What are the key features of a good transfer pricing data quality framework?

1 Data quality dimensions

A good transfer pricing data quality framework should address the following dimensions of data quality: completeness, validity, accuracy, timeliness, consistency, and relevance. Completeness means that all the required data elements are available and captured. Validity means that the data conforms to the predefined rules and formats. Accuracy means that the data reflects the true values and transactions. Timeliness means that the data is available and updated within the appropriate time frame. Consistency means that the data is coherent and compatible across different sources and systems. Relevance means that the data is suitable and meaningful for the intended purpose and users.

2 Data quality roles and responsibilities

A good transfer pricing data quality framework should also define the roles and responsibilities of the data owners, data providers, data users, and data stewards. Data owners are the business units or functions that have the authority and accountability for the data. Data providers are the entities or systems that supply the data to the data owners or users. Data users are the transfer pricing practitioners or stakeholders that consume and analyze the data. Data stewards are the individuals or teams that oversee and coordinate the data quality activities and processes. A clear allocation of roles and responsibilities can help to ensure data quality ownership, accountability, and collaboration.

3 Data quality processes and controls

A good transfer pricing data quality framework should also establish the data quality processes and controls that govern the data lifecycle, from data creation to data consumption. Data quality processes include the steps and procedures for data collection, validation, transformation, reconciliation, and reporting. Data quality controls include the checks and measures for detecting, correcting, preventing, and monitoring data quality issues. A systematic and standardized data quality process and control can help to ensure data quality consistency, efficiency, and traceability.

4 Data quality tools and technologies

A good transfer pricing data quality framework should also leverage the data quality tools and technologies that can facilitate and automate the data quality tasks and functions. Data quality tools include the software applications or platforms that can perform data quality functions, such as data extraction, integration, cleansing, profiling, matching, enrichment, and visualization. Data quality technologies include the hardware or infrastructure that can support the data quality tools, such as cloud computing, data warehouses, data lakes, or data hubs. A smart and scalable data quality tool and technology can help to ensure data quality speed, flexibility, and scalability.

5 Data quality metrics and indicators

A good transfer pricing data quality framework should also measure and monitor the data quality performance and outcomes using data quality metrics and indicators. Data quality metrics include the quantitative or qualitative measures that can evaluate the data quality level or improvement. Data quality indicators include the signals or flags that can alert or inform the data quality status or issues. A relevant and reliable data quality metric and indicator can help to ensure data quality visibility, transparency, and accountability.

6 Data quality culture and governance

A good transfer pricing data quality framework should also foster a data quality culture and governance that can embed and sustain the data quality principles and practices across the organization. Data quality culture includes the values, beliefs, attitudes, and behaviors that can promote and support the data quality awareness, commitment, and improvement. Data quality governance includes the policies, standards, guidelines, and procedures that can define and enforce the data quality objectives, expectations, and responsibilities. A strong and effective data quality culture and governance can help to ensure data quality alignment, integration, and maturity.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?