The Importance of Financial Data Management

What Is Financial Data Management?

Financial data management is a set of processes and procedures, implemented with specialized tools, that organizations use to measure and analyze their financial information. Used to collect, analyze, and manage financial data, it helps organizations aggregate and visualize it to improve operations and performance as well as stay in line with legal requirements. Financial data management provides information used by entities outside an organization, such as creditors, investors, and regulators.

Financial data management creates a single source of financial truth by eliminating silos and bottlenecks and pulling information from data streams to provide continuous updates. Data analytics are applied to financial information and presented through dashboards that are easy to understand and access. Financial data management also uses automation tools that improve reporting and agility.

Several common types of traditional financial data types that require financial data management include the following:

Assets
Anything that an organization owns that has value that can be converted into cash is considered an asset. An asset can be physical assets or intangible assets. For financial data management, assets fall into four categories:

• Real property—land and any buildings or other structures on the land
• Personal property—any property that is not real property
• Tangible property—physical property (e.g., inventory, raw materials, equipment, tools, furniture)
• Intangible property—any asset that does not have a physical form but still has value, such as patents, copyrights, or customer lists 

Cash flow
The term cash flow refers to the flow of money into and out of an organization. Financial data management can be used to analyze an organization’s cash flow to determine how well they manage their finances from a cash perspective. Financial data management includes the three types of cash flow that are reported, These are:

• Financing cash flow— the cash that is generated from a company’s financing activities, such as issuing new debt or selling equity
• Investing cash flow—the cash that is generated from a company’s investing activities, such as buying tangible property  
• Operating cash flow—the cash that is generated from a company’s normal business operations

Equity
Financial data management includes tracking and reporting equity, or the portion of a company that its shareholders own. Equity is the value of a company’s assets minus its liabilities. If a company’s liabilities exceed its assets, there to no equity. There are two types of equity: 

• Common equity is the portion of equity that is held by common shareholders.
• Preferred equity is the portion of equity that is held by preferred shareholders. 

Expenses
Expenses include all of the expenditures required to support operations and business activities, such as the cost of goods sold (COGS), costs of sales, administrative costs, and interest payments. Financial data management includes categorizing and tracking expenses.  

Financial statements
A financial statement is used to present the financial position, performance, and cash flow of an organization. The main three types of financial statements are:

• Balance sheet
• Cash flow statement
• Income statement 

Income
Income is the money that an organization earns from operations and business activities. An organization is considered profitable when income is greater than expenses. Conversely, an organization is considered unprofitable when expenses are greater than income. Financial data management covers all income, including:

• Interest income from investments
• Rental income from property
• Revenue from sales

Liabilities
Financial obligations or debts that an organization owes (i.e., to another person or entity) are called liabilities. Liabilities can be short-term (i.e., debts due within one year) or long-term (i.e., debts due after one year). Examples of liabilities, that are included in financial data management are:

• Money owed for goods or services that have been received 
• Money owed for loans or other debts 
• Taxes owed to the government  

Shares
A share is a unit of ownership in a corporation, also referred to as stock, and it can be either common stock or preferred stock. 

• Common stock is the portion of a company’s ownership held by common shareholders, who have voting rights, but do not receive dividend payments or have a priority claim on assets in the event of bankruptcy.
• Preferred stock is the portion of a company’s ownership that is held by preferred shareholders, who do not have voting rights but have a priority claim on a company’s assets in the event of bankruptcy and receive fixed dividend payments.

What Is the Role of Document Control?

The role of document control is to manage documents to ensure reliability and trust in the veracity of the contents as well as that organizations get the most value from documents with the least risk. A document control system integrates interrelated processes, workflows, and software products to support the production and management of documents within an organization. 

Document control is important for any organization, regardless of the industry, as it helps keep documents up to date and having proper approvals by providing structure to manage the entire lifecycle—creation, revision, distribution, updates, and destruction or archiving at the end of life—in a systematic, verifiable way.  

There are many reasons why document control is used, including to:

• Avoid using outdated, incorrect, and inaccurate documents that could result in expensive mistakes.
• Enhance productivity and performance.
• Ensure the safety of employees in organizations, such as construction or engineering firms. 
• Ensures the reliability and validity of documents. 
• Help meet regulatory compliance requirements. 
• Improve the quality of information. 
• Keeps verifiable records of activities surrounding the creation and modification of documents.
• Make sure that documents have undergone necessary reviews and approvals. 

Additionally, document control helps organizations by eliminating productivity losses caused by:

• Having to recreate missing documents manually.
• Losing files over email or miscommunication.
• Wasting time manually tracking documents

Why Is Financial Data Management Important?

The importance of financial data management is that it can be used to: 

• Assess risks
• Benchmark an organization’s performance against its peer
• Eliminate data silos
• Eliminate monotonous and time-consuming tasks
• Identify trends
• Inform decisions related to the allocation of resources and finances
• Manage the volumes and forms of financial data
• Provide a consolidated view of data across disparate locations
• Provide insights into an organization’s financial health and performance
• Reduce the number of data sources and streams
• Replace reports with data that can be weeks or months old with real-time financial information
• Support decision-making related to pricing, investment, and other strategic actions

10 Best Practices in Data Management for Finance Teams

1. Back up data and have a business continuity plan

No matter the circumstances, downtime is costly—in terms of time and money. Companies of all sizes suffer when there is unscheduled downtime. Worse is when downtime is caused by a data breach. To minimize the impact of a data loss incident, organizations should not only have data backups, but test them and practice restorations to ensure an efficient recovery when it is needed. Recommendations for business continuity plans include:

• Assign and document personnel roles and responsibilities
• Create a communication plan for disasters
• Define physical facility needs
• Document response procedures to restore services smoothly and as quickly as possible
• Establish recovery time objectives (RTO) and recovery point objectives (RPO)
• Identify and catalog sensitive data
• List locations and access protocols for recovery systems
• Maintain an inventory of hardware and software  
• Test recovery and service restoration plans with drills

2. Be sure to account for data biases

Data bias is real and, when introduced into financial data management practices, can cause errors by causing some dataset elements to be over or underrepresented. Commonly cited examples of data biases include: 

Available data not representative of the population

Breadth of data required for analysis is not used 

Inaccurate weights throw off data models

When executing financial data management programs, data analysts should be aware of different biases at each data management and analysis stage to avoid related pitfalls that can irreparably skew the resulting analyses. Common sources of data analysis bias include:

• Availability bias—relying on available data rather than searching for a more complete and representative dataset 
• Confirmation bias—focusing on the evidence that confirms an existing theory
• Historical bias—relying on outdated datasets to train and inform new data analysis projects
• Outlier bias—losing or obscuring the truth by failing to remove outliers  
• Selection bias—using samples that are not representative of the population for the analysis
• Survivorship bias—drawing a sample made it through a selection process rather than drawing from the entire group

3. Build behavioral models and forecast outcomes using data

Financial data management enables organizations to perform predictive analysis work. The resulting insight makes it possible to take a proactive approach to addressing opportunities and risks. Financial data management supports the development of reliable customer behavior models that can be used to predict outcomes accurately.

4. Develop a financial data management strategy and execution plans

Creating a stratetic financial data management plan is essential for successful execution. It will inform why analysis is required and what data is needed. This allows teams to focus resources on the relevant data. In addition, a strategic financial data management plan helps identify and remediate issues early, preventing time wasted with incorrect approaches or datasets. To get the most out of a financial data management strategy, it is important to follow five data analysis steps:

1. Definition—It is important to define why data analysis is needed and the questions that should be asked to find answers. This streamlines the process by narrowing the scope of the query only to relevant information.  

2. Collection—Establish data governance rules and processes that ensure that the right data is gathered and the right people have the appropriate access to data.

3. Cleaning—Create procedures for processing and aggregating raw data from multiple sources to ensure that it is clean and error-free before moving to the analysis phase. Here it is important to follow standard conventions for naming and storing data.   

4. Analysis—Effective analysis is dependent on building a financial data management foundation as part of the first three phases. Done correctly, this makes the analysis faster and easier with better results. 

5. Application—Once the analysis has been completed, the final phase is interpreting the results and turning them into actionable insights to inform decision-making. 

5. Create a team of in-house data analysts

An internal team of data analysts allows organizations to leverage increasing volumes of varying data sources continuously. With such a team in place, organizations can benefit from financial data management programs that can provide timely reporting and data analytics. 

6. Eliminate data silos

Proactively seek out data that is an archive controlled by a single entity or is otherwise. These data silos limit the efficacy of financial data management by obscuring views into potentially powerful information. Deep, valuable insights come from the analysis of complete datasets that draw relevant information from all available sources. Several problems that can result from data silos include:

• Lack of a complete view of an organization’s data
• Poor data integrity
• Reduced collaboration due to the inaccessibility of data
• Resource duplication expenses

7. Establish access levels based on projects, job roles, and functions with limits based on the principle of least privilege

Successful financial data management balances giving access to data with protecting it. Following the principle of least privilege, users get access to the information they need to do their jobs for as long as they need it—no longer. To facilitate this, a data classification system should be used to identify sensitive information and who should or should not have access to it.  

8. Foster a culture of compliance

Building a culture of compliance in financial data management goes a long way to protecting data, meeting requirements, and being audit-ready. This involves leveraging financial data management tools, such as data discovery, to review, identify, and track data flows to ensure multijurisdictional compliance. Technology should also be complemented with policies that enforce compliance best practices.

9. Make data security a top priority

Data protection and security should be top of mind at all times. Because financial data management involves so much sensitive information, data security must be baked in at all levels. The consequences of a data breach are never good, but for financial organizations, the impact can be catastrophic, with irreparable reputational damage, lost opportunities, and severe penalties for failing to meet regulatory requirements. Data protection should also be required for vendors or partners handling sensitive data. A few tactics to ensure data security include:

• Advanced firewalls
• Encrypting data flows 
• Multi-factor authentication
• Secure sharing 
• Security awareness training  

10. Narrow financial data management scopes to focus on the most valuable information 

Being selective with what is being measured and analyzed, allows organizations to deliver accurate and useful insights. This is not possible when every piece of data is collected and run through analytics. Experts agree that effective financial data management relies on the enforcement of the seven standards of reliable data:

1. Accurate and free from error

2. Collected on time

3. Complete and comprehensive

4. Consistent across all systems

5. Current and relevant

6. Extracted from a credible source

7. Standardized format

The Benefits of Financial Data Management and Combining Multiple Data Sources

Financial data management for combining multiple data sources is a powerful tool that innovative organizations and investors. And analysts are using to gain a deep, multi-dimensional view of opportunities and risks. Below are several commonly cited reasons that financial data management combining multiple data sources has become a go-to solution for leaders in all industries.

• Allows separate data sets to be combined and normalized
• Bases analytics on data from different sources
• Centralized control over data requires a centralized data repository such as a data lake, data warehouse, 
• Consolidates data from your existing financial systems to centralize into one single source of truth
• Makes all the available data sets and historical changes available in real-time
• Offers more granular insights into the performance and financial details of a business
• Provides robust data that can be used with predictive analytics to make business decisions
• Uses pre-built APIs that pinpoint and pull data from specific financial accounting endpoints

Financial Data Management as a Strategic Advantage

Financial data management solutions transform disparate and siloed information into a strategic advantage. Financial data management is the key to unlocking this strategic value. Using financial data management systems, organizations have ready access to the tools needed to efficiently use information that had been difficult or impossible to access. By allowing it to be aggregated, analyzed, and made actionable, financial data can be effectively used to inform decision-making and as a predictor of future opportunities, risk, and performance.

Egnyte has experts ready to answer your questions. For more than a decade, Egnyte has helped more than 16,000 customers with millions of customers worldwide.

Last Updated: 13th October, 2023