In the current era of digital transformation, financial data is the “barometer” of enterprise operations, and its governance is crucial. A good financial data governance system can improve data quality and security, and enhance the competitiveness of enterprises. This paper discusses the system construction and implementation path of financial domain data governance from four aspects: governance framework, platform architecture, asset catalog and application services.
Today, when the wave of digital transformation is sweeping the world, data has become a new production factor and strategic asset for enterprises. As a “barometer” of enterprise operations and a “compass” for decision support, financial data is directly related to the core competitiveness and compliance level of enterprises.
Building a systematic, intelligent and forward-looking financial data governance system is not only an inevitable requirement for “improving the intelligent and forward-looking financial digital intelligence system”, but also a key support for enterprises to achieve refined management and high-quality development. This article will delve into the system construction and implementation path of financial domain data governance from four dimensions: governance framework, platform architecture, asset catalog, and application services.
Data governance is also a very good choice from the financial domain, because the relevant indicators and data of finance are the results, and the results must be displayed accurately and in a timely manner, which must drive governance at the business level. Therefore, data governance through financial traction is a well-known thing. It is also smoother to advance.
First, build a system and clarify “why, who will manage it, and what to manage”
Organizational guarantee is the foundation, and the most feared thing about data governance is that the rights and responsibilities are unclear. And financial data governance is not an isolated technical project, but a systematic project involving strategy, organization, process and technology. Therefore, the core is to establish a closed-loop management framework to ensure data accuracy, consistency, security, and availability.
The primary challenge of data governance is “data silos” and ambiguous responsibilities. Lack of unified leadership can lead to inconsistent standards and prevarication. Establishing a data governance committee led by a senior leader (such as a CIO or CDO) is central to developing strategies, approving reports, coordinating resources, and mediating conflicts. Synchronization also needs to be responsible for daily data governance operations, and the corresponding data OWNER is also set up in the business department, specifically responsible for the implementation and supervision of data definition, quality, and security rules in the domain. This hierarchical governance structure clarifies “who owns, who is responsible, and who implements”, which is the organizational guarantee for the implementation of governance.
The second thing to do is the problem of standards. The standard mainly involves four things (master data, data model, metadata, indicator data) For the financial domain, first of all, it is necessary to unify the language, the pull of master data (such as accounting accounts, profit cost centers, suppliers, merchants) and the calculation caliber of indicators, etc., must be clarified, if it is clear, it is best to promulgate it through the system. Clarify the process of standard formulation, release, publicity, implementation, monitoring and continuous optimization. Utilize metadata management tools for standard registration, publishing, and querying.
To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.
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With standards, an important part of governance is to monitor operations. Establishing a quality index system can effectively inspect the implementation of standards. For example, we can define quantifiable and monitorable quality indicators (such as voucher error rate, report timeliness rate, master data repetition rate, etc.) and incorporate quality indicators into the performance appraisal of relevant positions.
2. Financial indicator system design
The financial indicator system should include several dimensions, such as financial performance, risk control, operational efficiency, strategic analysis, etc. Our common ones include:
Financial performance: profitability (ROE, ROIC), operating capacity (accounts receivable turnover, inventory turnover), solvency (asset-liability ratio, current ratio), cash flow position (net cash flow from operating activities).
For example: return on equity (ROE) = net profit margin = net asset profit margin × equity multiplier, in which we need to further refine to the atomic indicator, the net profit margin on sales is equal to = (net profit / sales revenue) × 100%. Among them, net profit (after-tax profit) = total profit – income tax expense = main business income + other business income – main business cost – other business costs – business tax and surcharge – period expenses (sales expenses + management expenses + financial expenses) – asset impairment loss + fair value change income (negative loss) + investment income (negative loss) – income tax expense.
And asset turnover ratio = total turnover / total asset value. (Or current.) And the turnover also excludes returns, discounts, etc.) So you see, a financial indicator actually involves a lot of data, once a certain data quality is not high, or the caliber is not unified, the data that comes out is also different.
Risk control: capital risk (capital concentration, gap warning), tax risk (tax rate fluctuation, compliance), exchange rate risk (exposure monitoring), investment risk (IRR achievement rate).
Operational efficiency: financial process efficiency (closing time, reimbursement cycle), cost control (unit cost, budget achievement rate).
Strategic analysis: business insight contribution (proportion of analysis reports that drive decision-making), resource optimization (budget accuracy, return on capital).
For the above indicators, we need to do several things, one is that the calculation formula must be clear, the business meaning, the calculation formula, and the numerator denominator data source. The second is which Owner and maintainer this indicator is. There is also the update frequency, data lineage, and visualization construction. (How to conduct an enterprise data asset inventory)
3. Suggested implementation route
Phase 1: Solid foundation (0-6 months): Assess the status of existing finance-related systems; Select and deploy a core data governance platform (focusing on metadata, data quality, and basic data standard management); sort out key financial data assets and start master data governance; Establish a preliminary governance organization and process.
Phase 2: Deepening governance and integration (6-18 months): Deepening data quality management rules and applications; Improve master data management and integration with source systems; Build an enterprise-level data asset catalog; promote the implementation of data security policies; Optimized data models support more complex analysis.
Phase 3: Intelligent Application and Value Unlocking (18 months+): Deep application of BI and advanced analytics; explore predictive scenarios (such as cash flow forecasting, risk warning); Realize the closed loop of data-driven decision-making; Continue to optimize the governance system and platform capabilities.
4. Data asset catalog: L5 framework construction and management dimension
The data asset catalog is the entrance to the centralized display and value release of data governance achievements.
Refer to external best practices (such as DAMA, IBM, Collibra) to build an L5-level directory framework:
- L1: Business Domain: Divided by the core value chain of the enterprise, such as “finance”, “supply chain”, “human resources”, and “sales and marketing”.
- L2: Data Subject Area/Subdomain: Subdivided under the financial domain, such as General Ledger Accounting, Receivables Management, Payables Management, Cost Management, Funds Management, Tax Management, Fixed Assets, Management Accounting (CO), Financial Reporting.
- L3: Data Asset Set: Represents a collection of core data assets under a specific topic, usually corresponding to a business-critical object or report. For example, General ledger voucher line items, customer master data, supplier master data, Cost center actual/budget data, Cash flow statement data, and balance sheet data.
- L4: Data Entity/Information Item: The specific logical data entity or key information item that makes up the asset set. For example, “Customer Code”, “Customer Name”, “Credit Rating”, and “Payment Terms” under the “Customer Master Data” asset set; Under General Ledger Voucher Line Items, Voucher Number, Fiscal Year, Account, Credit Amount, Cost Center, and Profit Center.
- L5: Physical Data Object: The implementation of the data entity in a specific physical system. For example, the ledger account is listed in the system name, database type, physical table/view/file name, field physical definition, storage location, access method, responsible person (technical).
5. Data application services: scenario-driven governance implementation
The last one is also the key, whether the governance is good or not, whether it is a mule or a horse, it has to come out, so under normal circumstances, we can choose some financial business scenarios to test the results of data governance, for example, we can choose comprehensive budget management and financial risk intelligent monitoring as the first batch of in-depth governance and application scenarios to verify the value of governance.
Financial data governance is a strategic and continuous work of “merit in the present and benefits in the future”. By building a solid governance framework covering organization, standards, quality, and security, designing a scientific indicator system, planning and implementing an integrated governance platform, creating a clear and usable data asset catalog, and promoting governance requirements from the source and application side with business scenarios as the traction, enterprises can effectively improve the credibility, security, and value density of financial data.
The road to governance is not achieved overnight, and it is necessary to uphold the principle of “overall planning, step-by-step implementation, and continuous optimization”, so that data can truly become a new engine for the high-quality development of financial empowerment enterprises.
