Clean Up Your Financial Processes for Growth

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Rose Report: Issue 53

By Ted Rose, CEO and president, Rose Financial Solutions

Spring is a time of renewal and change. It’s when we plant seeds and watch them grow. Like a garden, financial processes need to be tended to ensure they are prepared for business growth.

In this after-tax season, many business owners realize that their financial and accounting systems aren’t as well-organized as they assumed. If your business accounts are messy, outdated, and lack processes that tie your financial information together, you are not alone. Many business owners do not have the time and expertise to manage their finances and accounting functions. If you want to reduce the amount of time you spend on accounting processes, to gain financial clarity and to grow, it’s time to clean up your financial operations.

If you do not understand your numbers, you don’t understand your business. Having easily accessible financial data is a game-changer. The right accounting processes and software help you work smarter and faster allowing you to spend your time growing your business. While there is widely available off-the-shelf cloud-based accounting software, most lack the flexibility and integration of a workflow system that standardizes and automates your internal financial operations. Automated workflow technology involves structuring finance and accounting data and its underlying metadata—all while eliminating tedious processes, improving efficiency, limiting risks, and reducing human error.

To streamline your financial operations, you must build a solid accounting system, equip it with the right accounting software, and then connect your financial records. A gap analysis of your accounting processes will help you identify inefficiencies and assess whether you’re getting the most out of your resources. A thorough assessment also evaluates the gap between your existing accounting structure’s performance and your targeted goals and outcomes. Once you have identified the deficiencies in your accounting system, you can begin to improve and streamline your operations by organizing your finance and accounting structure and data.

The chart of accounts (COA) is a crucial financial management tool that groups all of a company’s financial operations, such as revenue and costs, into logical categories. A COA helps you develop more accurate financial reporting by breaking down transactions into defined categories such as revenue, direct expenses, indirect expenses, assets, liabilities, and equity. When putting together a COA, the better organized your data is, the more information you will be able to gain. As an organization grows it should consider adding “dimensions” to its COA to facilitate a greater understanding of financial performance. Examples of dimensions include projects, locations, departments, items, clients, etc. The addition of dimensions to the mix often increases the need to upgrade from basic accounting software to an “ERP.”

An organized COA with dimensions provides improved financial insight into your company’s financial health through financial reporting. A well-functioning system will generate timely, accurate, and easily accessible reports including financial statements, project reports, department reports, KPIs, ratios, and other financial information.

At RFS, we’ve wrapped over 25 years of expertise into our Finance as a Service solution that combines a team of experienced professionals with our leading-edge workflow technology, Easby. What makes our FaaS solution so unique is that it seamlessly integrates with your current accounting software, streamlines processes, and ties all your financial and non-financial information together into a high-level dashboard that delivers a 360-degree view of your company’s financial performance.

Schedule an introductory virtual meeting to find out how RFS can help you clean up your financial processes for growth.

This content is for information purposes only and should not be considered legal, accounting or tax advice, or a substitute for obtaining such advice specific to your business.

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ROSE Report – Data Driven and AI Enablement Strategies for 2024

By Matthew Scroggs • January 10, 2024
Issue 72 - Data Driven and AI Enablement Strategies for 2024

Empowering Business Expansion Through Data Driven Decision Making

By Matthew Scroggs • January 10, 2024
Recent findings from Pigment’s Office of the CFO 2024 survey highlight a critical issue for business leaders – the prevalent use of inaccurate data in their decision-making processes. The survey reveals that a staggering 89% of finance leaders are basing their decisions on incomplete or faulty data. The foundation of successful business strategies depends on the quality and accuracy of the decisions made. As businesses navigate expansion and heightened competition, the reliance on data-driven insights has become critical. Harnessing the transformative power of accurate, reliable data enables informed and effective decision-making. Businesses with financial clarity will outpace companies that struggle with flawed data. Financial visibility will help businesses avoid common pitfalls while shaping a future oriented strategic vision. Why Is Most Financial Data Flawed? Financial Data often ends up flawed due to several factors. Disparate systems and fragmented processes within an organization can cause increased inaccuracies over time. The lack of standardization of data within an organization introduces complexities and leads to inconsistencies in data handling. Nomenclature and connectivity issues further compound the problem, making it challenging to establish a framework for data organization. When these issues persist, they pave the way for flawed data, hindering accurate analysis and decision-making. Improving Financial Data with a “Single Source of Truth” Addressing the complexity of inaccurate financial data requires a strategic approach. Streamlining systems and processes and implementing standardized, data-oriented procedures across departments can mitigate inaccuracies stemming from disparate systems and fragmented processes. Moreover, establishing a unified nomenclature and resolving connectivity issues are pivotal to ensuring data integrity. By instituting a cohesive framework for data organization and management, businesses can tackle the root causes of flawed financial data. Establishing a single source of truth consolidates data into a single data structure. This allows for the streamlining of processes, reduction of complexity, standardization of nomenclature and improved connectivity. In essence, a single source of truth reduces errors by ensuring everyone in an organization refers to the same accurate information. This unified data hub speeds up decision making and lays the groundwork for integrating AI into future financial operations. Enter Easby, a system of engagement that standardizes financial activities and data while improving data integrity. As a CFO Co-Pilot, Easby streamlines data handling and reporting, allowing leaders to make better decision based on better information. Easby reduces administrative activity and promotes data-accuracy, improving decision-making and driving companies toward success in our competitive business environment. Easby connects with your accounting system of record to become a “single source of truth”, centralizing data and refining processes. By streamlining data collection and reporting, Easby empowers leaders to refocus their efforts on strategic growth initiatives. To discover how Easby can become your CFO Co-Pilot while fortifying the future of your organization, we invite you to schedule an introductory call with Rose Financial Solutions (ROSE). Schedule an Introductory Call

Optimizing Data for Effective AI Integration

By Matthew Scroggs • January 10, 2024
Technology, Data and Automation are transforming decision-making, especially with the democratization of Artificial Intelligence (AI). This transformation is especially pronounced within finance, where AI's emergence is influencing financial system strategies, placing a premium on structured data for AI-driven initiatives. However, the ability to utilize AI effectively heavily relies on data organization and security. Organizing data includes data consolidation, categorization, and tokenization. This organization can help establish the groundwork for your company to benefit from the full potential of a wide-range of AI-driven use-cases. Consolidating Diverse Data for Unified Insights Data consolidation includes merging and unifying diverse data sets from multiple sources into a single source of truth. Let’s consider a corporation that operates across various states. Each division might maintain financial and operational records, such as sales figures, payroll, operational expenses, and inventory in disparate systems. Data consolidation in this scenario involves merging these diverse datasets from different divisions into a singular, centralized system. For instance, combining sales data from different regions, integrating it with payroll and inventory records, and aligning financial reports across divisions creates a comprehensive overview of the company's overall performance. This consolidated data allows for better analysis of revenue streams, cost optimization strategies, and more accurate forecasting across the entire organization, aiding in strategic decision-making for the whole company. Enhancing AI Precision through Categorization Categorization involves sorting data into specific items or categories based on various parameters or attributes. It's about organizing and labeling data in a structured manner. For example, in accounting, data categorization refers to sorting expenses into a variety of dimensions, such as general ledger codes, department codes, project codes, etc. These codes are normally broken down into logical categories that help users and AI understand that certain vendors are related to travel and others are related to office supplies, or utilities. In AI-driven strategies, categorization is paramount for contextualizing and organizing information effectively. By classifying data into relevant categories or items, AI systems can understand the nuances of different data sets. This categorization allows for more precise analysis, facilitating the extraction of actionable insights and comparisons that are crucial for decision-making. Tokenization for Advanced Data Efficiency and Security Tokenization is the segmentation of complex data into smaller, more manageable units known as tokens, each representing individual pieces of data or information. This process primarily focuses on maintaining confidentiality when inputting data into AI systems. Its core objective is safeguarding sensitive data by substituting identifying information with distinct tokens or representations. By implementing tokenization, organizations create a protective barrier around sensitive information, like personal or financial data, thwarting AI from associating the data from a specific entity. Tokenization ensures that AI algorithms work with transformed data. For instance, tokenization involves converting sensitive data, like vendor names, into random tokens in financial transactions. This not only enhances security by safeguarding sensitive information but also streamlines data analysis by reducing the complexity of the dataset. In AI strategies, tokenization is a critical step. By segmenting data into tokens, AI algorithms can more effectively identify patterns, trends, and correlations within the information, ultimately enabling more accurate predictions and insights, all without providing the AI with sensitive information. Leveraging Integration Opportunities with AI Consider a company working to streamline its accounting processes. The organization creates a unified database through data consolidation and tokenization. The integration of AI technology allows for the use of machine learning to automate transaction coding, a move that significantly reduces manual workload while improving processing accuracy. Other examples of AI integration include automating graphic analysis and categorization creation. For instance, AI-driven tools can autonomously generate visual representations of complex datasets. Moreover, within categorization, AI systems excel at continuously refining and automating the sorting of diverse data sets into specific categories or segments, ensuring accuracy and efficiency in data handling. Finally, AI-driven tools leverage historical patterns to track and analyze financial behaviors. For instance, by examining past expenditures, these systems identify trends, anomalies, and potential cost-saving opportunities. This level of insight allows businesses to make more informed decisions regarding budget allocation, identifying areas for optimization and possible financial risks. Scaling Efficiently Through AI-Driven Strategies By merging AI-driven strategies with data management, businesses gain adaptability. This agility powers informed decisions, intelligent resource allocation, and proactive risk management. This approach isn't just about navigating competition; it's about efficient scaling and strategic growth, representing a shift towards growth while benefiting from financial clarity. This strategic combination empowers businesses to thrive, evolve, and seize opportunities in a constantly changing business environment. Schedule an introductory call with us today to explore how optimizing your data strategy can enhance your adaptability, drive informed decisions, and propel your business towards scalable growth. Schedule an Introductory Call
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