After Baylis and Harding switched to a new financial data management platform five years ago, managerial decisions significantly improved. The company reported greater flexibility in key metric tracking, which provided execs with the right information at a glance. Financial data management is tricky, because it’s customizable at every firm. Read on to get our 7 key recommendations for efficient financial data management.
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What is financial data management?
Financial data management is the understanding, tracking and controlling of a company’s wealth. In particular, it refers to the tools and processes that help businesses to report their financial information. Organizations use these tools to analyze their past and predict future performance.
However, financial data management is not simply a department or function; automation capabilities can transform an organization’s efficiency. Financial data management enables the consolidation of account information and streamlining of progress reports towards financial goals and KPIs.
Why is financial data management important?
Financial data management is important as it is used to underpin the majority of strategic decisions within enterprise-level companies.
For example, strong financial data management can help companies pivot towards new ideas faster than relying on traditional financial reporting timelines (such as on a quarterly basis). That’s what computer accessories brand Logitech achieved in 2011/12 when they used financial data to pivot their business focus.
Financial data showed that the production of computer mice and scanners, products traditionally associated with their business, was no longer as profitable as digital products. Thus, Logitech switched their focus towards gaming and music.
This enabled Logitech to reduce costs and increase overall profits. Moreover, by relying on the data, the team was able to continue a sustainable operation where other PC companies have died out.
Alternatively, poor financial data management could lead to investment into the wrong areas and end up straining your financial situation. For example, Crumbs Bake Shop was once the world’s largest cupcake vendor.
Despite its initial growth, the company failed to pay attention to:
- Market share information, which proved it was losing out to the competition
- Increasing rent and resources costs, which dented it’s profits
- Falling sales per store, while they continued to open new stores
Because of this poor financial management, Crumbs Bake Shop stopped making cupcakes when it went bankrupt in 2011, closing their remaining 58 stores all in a single day.
Best practices in financial data management
Good financial data management can be the difference between company success and early closure. So without further ado, here are seven of the best practices in customer and business data management:
- Eliminate data silos
- Benchmark key performance indicators
- Enhance regulatory compliance
- Account for data biases
- Limit data access
- Build an operational resilience plan
- Consider data security standards
Eliminate data silos
Data silos can occur when separate departments are unable to share their information and collaborate, leaving teams to work without the full picture. Not only is this incredibly inefficient (as some teams might be collecting the same information as others), but it’ll also lead to skewed conclusions.
Eliminating data silos is about ensuring widespread access to data and enabling the information to flow. This means setting up data SOPs such as:
- Ensuring the data is collected and stored in the same format across different departments
- Maintaining secure cloud-based storage ERP technology for all data, which can be accessed by multiple teams
- Automatically promoting the right information to answer certain questions, to prevent biases in conclusions
- Onboard all vendors into a single database for transparent management
A pensions firm recently underwent its own data transformation to eliminate silos between data sources. The team was able to interlink and enrich data from various sources to increase interdepartmental connectivity and integration. This led to increased visibility and discoverability.
Overall, this case study proved that pension firms benefited from reduced time and investment spent on data cleansing and maintenance.
Benchmark key performance indicators
Key performance indicators (KPIs) are the metrics that your company will choose to track its progress towards goals. In terms of financial KPIs, management is often focused on:
- Liquidity metrics like working cash flow
- Revenue, profits, and expenses
- Product margins and inventory turnover Leverage (assets/equity)
In truth, there are hundreds of financial KPIs that a business could measure. But it’s important to select the most relevant and benchmark for the highest quality data insights.
Benchmarking KPIs and analytics will help your team to reach their goals. But they’re also helpful to set a standard when team members aren’t sure what is considered good or bad, as it gives a little bit of context to the mindless mound of numbers.
For example, the average invoice reconciliation process takes ten days, but best-in-class companies complete the process in less than four. So, setting a benchmark KPI of 6 days for invoice reconciliation could aid your company’s liquidity and working cash flow, while maintaining supplier relationships and without increasing pressure on your employees.
Enhance regulatory compliance
Regulatory compliance is a hugely important focus area for enterprise-level businesses. While it might not seem related to financial data management, you might be surprised to learn that enhancing your compliance is absolutely among the best practices.
Global financial data standards, such as ISO 200022, provide payment information for corporations, including for cross-border supplier and customer transactions.
However, adhering to the requirements for these standards is important not only to avoid the financial and reputational penalties that come with non-compliance. ISO 20022 also supports compliant businesses to “use richer and structured data to forecast their future inbound and outbound payment flows.”
The solution to following the rules is setting up your business for automatic compliance. You’ll also benefit from accelerated services, an improved reputation for winning new contracts, and best-in-class security standards.
Account for data biases
Data biases are a less common focus of financial data management but are important nonetheless. This term refers to the inherent skewing of information, thanks to things like collection errors, historical prejudices, and field choice.
Even algorithms operate with bias, as highlighted in a recent search query online:
“In my personal experience, when searching for “professional hairstyles for women”, I was confronted by a series of images of Caucasian women (it is worth noting that this has since been fixed following a major user backlash on social media)”, said Financial Times journalist Grace Ata.
Accounting for data biases means first recognizing them in your governance strategy, both in the gaps of missing data and skewed collection. Best practices for accounting for data bias include:
- Centering DEI principles around all processes
- Removing the traditional obstacles to fair data collection and analysis, including prejudices
- Conscious collection to account for biases in categories, questions and data sets
- Analysis of data bias for each data set to improve the transparency, accuracy and quality of information
Set data access
While certain financial data is publicly available, others are incredibly sensitive.
For example, companies that enter into mergers and acquisitions may be cautious about sharing certain data, worried that it might put off potential investors.
In fact, HP suffered from a huge due diligence disaster after they bought out Autonomy in 2012 without having properly investigated the financial data. As it turned out, the income statement, balance sheets, and cash flow were all inflated, losing HP over $9 billion.
Moreover, data limitations might be used to control employee access. Junior employees, for example, may have no justifiable reason to see private financial information, whereas a senior finance executive may need to approve certain budgets and transactions as part of their job.
Setting automatic access limitations inside your solution system, based on role and seniority, is therefore a good way to prevent your financial data from getting into the wrong hands.
Build an operational resilience plan
Operational resilience is an ongoing battle for every business, as it strives to continue daily processes even while experiencing extreme pressure. Threats like supply chain disruptions, system breaches, and new competitors can all change the way that your business operates.
It’s imperative to back up your financial data in a secure digital environment to build operational resilience. Cloud-native platforms are traditionally the most suited to backing up important data, especially for larger companies with terabytes of information.
Including financial data models in your operational resilience plan could look like:
- Focusing on governance: ensuring the right decision-makers get access to the information they need in good time
- Testing: scenario testing can help your organization to problem-solve threats
- Risk assessments: forecasting changes to the financial data can help provide insight to the impact of risk events and signal which risks should be prioritized
- Communication strategy: ensuring access to data is important in communicating clearly and transparently with stakeholders to prevent panic
Consider data security standards
Finally, considering data security standards is one of the most important aspects of managing financial data. Trustpair’s 2024 fraud study revealed that 83% of companies were targeted by cyber fraudsters in the last 12 months.
In fact, the Finance Department of the Australian Government accidentally shared its confidential information for the second time in four months. In an entirely human error, one of the administrators at the government department sent out a mass email embedded with the confidential data of third-party service providers. Data security standards can protect businesses against both accidental breaches and fraudulent attacks.
Consider using data protection measures such as:
- Upgrading your firewall and spam filters to prevent hacks
- Training your staff to recognize social manipulation techniques, such as phishing
- Use Trustpair to validate third-party information, automatically match invoices to genuine financial information and prevent vendor fraud
To conclude: successful financial data management can improve your business
Good financial data management can be the difference between bankruptcy and a profiting enterprise. 7 best practices include eliminating data silos, benchmarking KPIs, regulatory compliance, accounting for data biases, limiting data access, building an operational resilience plan, and setting data security standards, like Trustpair’s data monitoring platform.