Easby® Makes it Easy

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By Ted Rose, CEO, Rose Financial Solutions

Managing finance, accounting, and tax functions is complex. In fact, it is often compared to rocket science and brain surgery. It’s time to end complexity! At ROSE®, we make it easy to manage your financial activities so you can more efficiently run your business and build stakeholder value. 

Through Easby®, our Finance as a Service (FaaS) solution extends beyond traditional finance and accounting outsourcing by combining the power of people, processes, and technology. With ERP-level technology, Easby helps you better manage your back-office functions by connecting them into a unified system that facilitates visibility, communication, and access. Easby makes managing your financial processes easy and helps guide your success, taking you from where you are to where you want to be. Here’s how:

Efficiency

CEOs are forced to rely on piecemealed data and reports when utilizing outdated systems. Easby houses all of your finance and accounting information within the same platform, eliminating the need to log into multiple databases to manage your financial functions. With an enhanced user experience and user-friendly navigational tools, Easby brings your financial data to life, providing an unprecedented view of your company’s fiscal health.

Reliability

Due to the lack of a “big picture” view of the organization, outdated financial and accounting systems can impede business growth. CEOs can only make intelligent business decisions if they have access to reliable and timely information. Easby ensures that your financial data is kept current so you can track positive and negative changes that occur within your business.

Visibility

Easby’s complete financial package provides all the reporting necessary to navigate your business toward its goals, including the ability to track your KPIs against your financial statement to determine where operational issues exist so you can swiftly take corrective actions.

Financial Guidance

Our team of experienced finance, accounting, and tax professionals provides superior guidance for financial management, performance improvement, and minimizing compliance-related risks.

Scalability

Companies frequently choose a finance and accounting solution that addresses their immediate needs, not future requirements. As your company grows, Easby scales to suit your demands in terms of the volume of transactions executed and the incorporation of new technologies.

Cost-effective

In these trying economic times, many CEOs are looking for ways to cut expenses, increase productivity, and gain financial clarity without investing in a costly accounting system or adding additional staff. On average, Easby saves our clients 30% to 50% of the cost of an in-house team.

Security

With control structures in place, Easby decreases fraud risk by enforcing protocols allowing only those authorized to access, review, and approve payments. Additionally, Easby integrates self-authentication and provides enterprise-level security.

Flexibility

Whether you need a fully staffed solution or need support in specific areas of your accounting and finance department, we configure Easby to fit your needs. Our fully staffed solution includes our full technology platform, our best practices, and our people. Or you can make your accounting and finance staff more effective using Easby with your existing accounting software.

The Future of FaaS is Easby

As a leading industry innovator, ROSE has been at the forefront of the paradigm shift from traditional finance and accounting outsourcing to utilizing technology to simplify back-office functions through FaaS. Easby is a cost-effective, scalable solution that grows with your company and provides the advantage of an ERP without abandoning your current accounting and finance system. Please schedule an introductory call for more information on why the future of FaaS is Easby.

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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