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Rose Report: Issue 57

By Ted Rose, CEO Rose Financial Solutions

The new government fiscal year is right around the corner, opening opportunities for prime contractors in the upcoming year. If you are a government subcontractor or commercial company wanting to make the jump to working directly with the federal government, now is the time to implement processes to manage subcontractors and ensure that the work defined by the contract is completed on time and within budget.

A major challenge for contractors, according to Deltek’s Clarity 2022: Government Contracting Industry Report , is keeping up with the rising demand for critical functions, such as finance, procurement, and project teams, to help them eliminate manual processes and recognize risks before they become systemic. For businesses aiming to become prime contractors having the proper mechanisms in place to avoid pitfalls is essential. Here are some tips to help put you on the path to winning contracts.

DCAA compliant accounting system

First and foremost, having a Defense Contract Audit Agency (DCAA) certified accounting system for your business is a necessity. If you aren’t DCAA compliant, you run the danger of being turned down for government opportunities. Your accounting system must have sufficient internal controls to ensure the validity and accuracy of your financial statements. Can you prove that you correctly report all transactions and strictly follow required policies and procedures? If not, you should schedule an evaluation of your accounting system immediately.

Project management systems

Contractors working for the government know that success depends on executing tasks on time and within budget. However, according to Deltek’s 2022 report, many survey respondents indicated that staying on schedule presented a more significant challenge than budgets did in 2021. During uncertain times, government contractors must ensure that projects remain within budget and timeline, despite shortages in human resources and supply change issues. Implementing a project management system that connects finance and accounting functions, provides budgeting and forecasting, and tracks project progress helps guarantee that you meet each project’s objectives in terms of time and cost.

Analyze and reconcile accounting records

The DCAA requires contractors to divide their costs into categories, which commonly include general and administrative expenditures, overhead costs, and fringe benefits, to determine the indirect rates charged to the government. To ensure the budgeted indirect rates you give the government at the start of the year remain in line with the actual costs of carrying out the work, you should reconcile your balance sheet and income statement accounts monthly. At the same time, make sure to keep an eye on indirect cost pools.

Forecast expected revenues and expenses

To set an appropriate rate for your product or service for next year, you must forecast expected revenues and expenses. Financial reports from the previous year can be used to establish budgets, predict costs, establish provisional rates, and set revenue expectations. To estimate future revenues, analyzing the inflow and outflow of previous funds is necessary. These figures and current and expected contract figures should provide a benchmark of what your firm can expect to earn.

As your business grows and secures more contracts, your accounting processes must evolve too. At RFS, we have a 27+ year track record of preparing our clients for growth through a cost-effective and scalable finance and accounting infrastructure.

Winning more contracts translates to more revenue, more employees, and more transactions. We make sure your back-office is ready for this increase in activity by providing:

  • Audits for DCAA compliance
  • Accurate project profitability and indirect rate reports for your contracts
  • Month-end close, AP, AR, and payroll support
  • Project management system that connects finance and accounting functions, provides budgeting and forecasting, and tracks project progress

 Please find out how cost-effective our GovCon solution is by visiting our pricing calculator.

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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