Is Your Financial Data Effective? Building a Trustworthy, Discoverable, and Usable Data Foundation_News_北京智达方通科技有限公司

In today's data-driven environment, data analysis and artificial intelligence play pivotal roles. The effective operation of these analytical tools and AI technologies relies on the support of high-quality data. However, many finance professionals still spend a significant amount of time on data cleansing rather than focusing on uncovering deep insights from the data. This is because data often suffers from issues of incompleteness, inconsistency, or unreliability. Surveys indicate that over 80% of data analysis and AI workloads are concentrated on the data preparation stage. So, what exactly does data preparation entail? From the perspective of modern finance development needs, it encompasses the filtering, transformation, and organization of raw data to convert it into a format suitable for report generation, analytical research, and innovative tool application. However, merely completing data preparation is far from sufficient; data must also undergo quality validation to ensure it meets practical usage requirements.

Multi-dimensional Assessment Ensures Data Quality

Data quality refers to the fitness of data for its intended purposes in operational, compliance, and decision-making scenarios. Currently, especially in large enterprises, the scale of data generation and collection is growing exponentially, making the improvement of data quality a critical and urgent issue. Multi-dimensional assessment serves as the primary step in optimizing data quality, aiming to measure and enhance data quality in the realm of financial planning and analysis across dimensions such as completeness, accuracy, consistency, timeliness, uniqueness, and stability. Modern intelligent financial tools and analytical models cannot function effectively on flawed or incomplete data. Moreover, if trust in the data is persistently lacking, any analytical result loses its practical application value. Through the outcomes of multi-dimensional assessments, finance teams can accurately pinpoint the root causes of data issues, formulate targeted solutions based on a solid data foundation, and apply them to data analysis or the construction of intelligent financial models.

Leveraging the results of multi-dimensional assessments, FP&A teams can ensure the reliability, timeliness, and credibility of "Budget vs. Actual" reports. Enterprise leaders can then respond quickly to genuine business variances without expending energy chasing data errors. Financial policies, allocation rules, and reporting standards can also be consistently enforced, thereby reducing audit risks. High-quality and trustworthy data further provides solid support for advanced forecasting models, scenario planning, and predictive analytics, making the resulting insights more credible and actionable.

From Data Quality to Data Usability

Once data quality is improved, the ensuing challenge is: Can this data be easily discovered, shared, and reused across different regions, tools, processes, and teams? Can the required information be located quickly using suitable data and systems? Is it feasible for use across tools and departments? Is the documentation detailed enough to support reuse needs? If integrating financial actuals from multiple regions involving diverse ERP systems, modeling and forecasting, and comparing against external benchmarks, then even if the data meets quality standards, a deep consideration is still required: Is it truly prepared for data analysis and intelligent operations?

In the FP&A field, time is money, and time spent searching for data equates to wasting money. Core methods for accurately locating data include:

● Adopting globally unique identifiers, such as unified naming conventions, version management, DOIs (Digital Object Identifiers), etc.

● Utilizing metadata to tag datasets and models, covering dimensions like source, owner, format, and time range.

● Clearly documenting the data owner, the person responsible for quality, the person responsible for updates, and the contact person for clarifying questions.

● Employing searchable repositories like centralized data catalogs, MDM systems, internal wikis, and other tools.

Data being discoverable does not necessarily mean it is accessible. Key operations to ensure data availability include:

● Adopting standard data access protocols such as APIs, secure file sharing, SQL endpoints, etc., to retrieve data from a centralized data platform.

● A centralized data platform is the cornerstone of modern data management. It eliminates data silos, ensures data consistency, and promotes more efficient data utilization within the organization.

● Clearly defining rules for data access permission assignment and simplifying the permission request process.

● Implementing mechanisms like secure API gateways, access control via Single Sign-On (SSO), and Role-Based Access Control (RBAC) authorization.

● Clearly stating any known assumptions, limitations, or caveats; this information helps prevent insights from being misused or over-generalized.

Secondly, enhancing interoperability is crucial for breaking down barriers and promoting the realization and availability of data across the enterprise. Core operations for achieving this include:

● Clearly explaining calculation methods, the semantic meaning of specific data fields, and various business rules or transformation rules applied to the data. This effectively avoids misunderstandings and ensures consistency in reporting and analysis work.

● During data integration, adopting open and mature data formats like CSV, JSON, Parquet, etc.

● Establishing common business definitions, such as standardized account hierarchies, product description formats, etc.

● Leveraging proven ETL (Extract-Transform-Load) tools to build data pipelines for the centralized data platform. ETL data pipelines can transfer data from multiple sources to centralized platform systems like data warehouses or data lakes, supporting reporting, analysis, and AI applications.

Finally, data reusability is key to achieving speed and scale. When data is trustworthy and can be reused, its adoption rate increases significantly. Core operations are as follows:

● Define and clearly document data licenses and usage rights to ensure all data usage complies with organizational policies, legal frameworks, and external regulations.

● Clarify the subject who created the data, the creation time, the usage logic, version control information, covering the data source, its flow path within ETL data pipelines, and the transformations or aggregations it has undergone.

● Track changes over time in schemas, logic, or field definitions to help users accurately interpret historical analysis results.

Once data quality is enhanced and usability improved, open data access through a secure, compliant, and searchable platform. This ensures the right people can efficiently obtain and use trustworthy data while meeting various standards for governance, privacy, and regulation. Data sharing must have a clear purpose, be limited to authorized personnel with data literacy, and support collaboration across teams, processes, and systems. This stage ensures that data creates value in a responsible and efficient manner.

In the age of artificial intelligence and data analytics, trustworthy and usable data is the most core competitive asset for an enterprise. For financial planning and analysis teams, providing faster, smarter insights must start with a high-quality data foundation. Multi-dimensional data quality testing and usability testing together construct a comprehensive implementation roadmap, helping finance teams prepare data that is not only accurate and clean but also capable of scaling smoothly across processes, personnel, and platforms. This empowers finance professionals to reduce inefficient work, improve forecasting accuracy, and drive smarter, faster decision-making.