Gartner Magic Quadrant: Exciting EPM Software Trends Every Business Should Know in 2024
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Are you struggling with inefficient financial planning and outdated processes? This blog explores the evolution of Enterprise Performance Management (EPM) software, highlighting the transition from manual methods to advanced AI and ML-integrated solutions. You will gain insights into the latest market trends and technological advancements driving EPM growth and learn how to select the right EPM tools for your business needs in 2024.
Enterprise Performance Management (EPM) has undergone a remarkable evolution, transitioning from rudimentary manual processes to leveraging cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) technologies.
This transformation has not only streamlined financial planning, budgeting, forecasting, and reporting processes but also equipped organizations with strategic insights for informed decision-making.
As we explore EPM software, we uncover the technological advancements and shifting business dynamics that have shaped their development.
From the initial reliance on paper-based systems and spreadsheets to the integration of sophisticated software solutions, the progression towards cloud computing, and the recent adoption of artificial intelligence (AI) and machine learning (ML), each phase reflects a leap towards more efficient, insightful, and agile financial management.
Moreover, this article explores the current EPM market’s size, emphasizing its significant growth trajectory and the factors driving this expansion.
As EPM software continues to evolve, it promises to offer even greater capabilities, integrating advanced analytics and fostering tighter collaboration across enterprises.
This article aims to provide a comprehensive overview of how EPM software has evolved, what different EPM tools presently offer, and the exciting trends on the horizon for 2024, guiding organizations in choosing the right EPM solution to meet their evolving needs.
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The Evolution of EPM Software
The evolution of EPM software has been marked by significant technological advancements and shifting business needs, leading to more sophisticated, integrated, and user-friendly solutions. This evolution can be traced through several key phases, each introducing new capabilities and focusing on addressing the growing complexity of organizational financial planning and analysis needs.
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Phase 1: Manual Processes and Standalone EPM Tools
Early Stages
Initially, EPM processes were highly manual, relying on paper-based systems and basic electronic spreadsheets. Organizations used standalone tools, primarily spreadsheets, for budgeting, planning, and reporting. This approach was time-consuming, error-prone, and lacked real-time insights.
Lack of Integration
Data resided in siloed systems, making it difficult to achieve a unified view of financial performance across different departments and business units.
Phase 2: Emergence of EPM Software Solutions
Dedicated EPM Software Introduction
In response to the limitations of manual processes, dedicated EPM software solutions began to emerge. These solutions aimed to streamline financial planning and reporting processes, offering more accuracy and efficiency than spreadsheets.
On-Premises EPM Software Systems
Early EPM software was primarily deployed on-premises. These systems offered better data consolidation and facilitated some degree of automation in financial reporting and analysis.
Phase 3: Advancements in Technology and Integration
Integrated Planning
EPM software evolved to support integrated financial planning, linking strategic planning, budgeting, forecasting, and financial reporting into a cohesive system. This integration enabled better alignment between financial goals and operational plans.
Business Intelligence (BI) Integration
The integration of BI tools with EPM tools enhanced reporting capabilities, providing more insightful analytics and data visualization options to support decision-making.
Phase 4: Cloud Computing and Accessibility
Cloud Adoption
The shift to cloud-based EPM software represented a significant evolution, offering scalability, reduced IT overhead, and enhanced collaboration. Cloud platforms enabled real-time data access, anywhere and anytime, fostering more agile and flexible financial planning processes.
Mobile Accessibility
The introduction of mobile access to EPM tools further increased flexibility, allowing finance teams to access critical financial data and perform tasks from any location.
Phase 5: Incorporation of Artificial Intelligence (AI) and Machine Learning (ML)
AI and ML Integration
The latest evolution in EPM software includes the integration of AI and ML technologies. These advancements bring predictive analytics, scenario planning, automated anomaly detection, and natural language processing to financial planning, significantly enhancing forecasting accuracy and operational efficiency.
Automated and Continuous Planning
EPM tools now support more dynamic, continuous planning processes, moving away from the traditional static annual budgeting model. This shift allows organizations to adapt more quickly to market changes and business needs.
Phase 6: Focus on Strategic Insights and Decision Support
From Reporting to Strategic Analysis
EPM software’s role has evolved from focusing mainly on financial reporting to providing strategic insights supporting decision-making at all levels of the organization.
Collaboration and Workflow
Modern enterprise performance management software emphasizes collaboration, with features that streamline workflow, enhance communication among stakeholders, and integrate financial planning processes across the enterprise.
The Future of EPM Software
The future of EPM software is expected to see further integration of advanced analytics, more sophisticated AI-driven insights, and even tighter integration with operational systems. The goal is to provide a holistic, real-time view of enterprise performance, supporting strategic decision-making and driving business growth.
The evolution of EPM platforms reflects the broader digital transformation journey of organizations, focusing on enhancing efficiency, insights, and agility in financial planning and performance management.
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EPM Market Size: Current State and Predictions
The current enterprise performance management (EPM) market size is estimated at USD 7.53 billion in 2024 and is expected to reach USD 11.86 billion by 2029, growing at a Compound Annual Growth Rate (CAGR) of 9.5% during the forecast period (2024-2029).
The enterprise performance management (EPM) market is experiencing significant growth due to a combination of technological advancements, changing business needs, and the increasing importance of strategic financial planning. Here are some key factors contributing to the growth of the EPM market:
1. Increased Demand for Efficient Financial Processes
Organizations worldwide are seeking more efficient ways to manage financial planning, budgeting, forecasting, and reporting. The need for streamlined and automated financial processes to replace manual, time-consuming tasks is a major driver for EPM adoption.
2. Need for Real-Time Data and Insights
The acceleration of digital transformation across industries has heightened the demand for real-time data access and insights into financial performance. EPM solutions provide organizations with the ability to analyze financial data in real time, supporting faster and more informed decision-making.
3. Advancements in AI and Analytics
The integration of artificial intelligence (AI) and advanced analytics into EPM platforms has significantly enhanced their capabilities. These technologies enable predictive forecasting, scenario planning, and anomaly detection, offering deeper insights into financial data and future trends. This has made EPM solutions more attractive to businesses looking to leverage data for competitive advantage.
4. Growing Complexity of Business Environments
As businesses expand globally and operations become more complex, there is a growing need for comprehensive financial planning and analysis that can accommodate multiple currencies, regulatory environments, and business models. EPM solutions are designed to manage this complexity, making them essential for large, multinational organizations.
5. Strategic Planning and Performance Management
Organizations are increasingly recognizing the importance of aligning financial planning with strategic goals and operational performance. EPM software facilitates this alignment by integrating financial data with operational data, enabling holistic planning and performance management.
6. Cloud Adoption
The shift towards cloud-based EPM solutions has made these platforms more accessible to a wider range of businesses, including small and medium-sized enterprises (SMEs). Cloud deployment offers scalability, reduced IT infrastructure costs, and ease of access, contributing to the market’s growth.
7. Increased Focus on Regulatory Compliance and Risk Management
EPM solutions help organizations navigate the complex landscape of regulatory compliance and risk management by providing tools for financial reporting, compliance tracking, and risk assessment. This capability is particularly important in industries with stringent regulatory requirements.
8. Digital Transformation Initiatives
Many organizations are undergoing digital transformation to improve efficiency, agility, and customer service. EPM is a key component of these initiatives, providing the financial insight and control needed to support transformation strategies.
In summary, the growth of the EPM market is driven by the need for more sophisticated, efficient, and integrated financial planning and analysis capabilities. As businesses continue to navigate a rapidly changing economic environment, the demand for EPM solutions is expected to increase, further fueling market growth.
AI Integration in EPM Software: Types and Applications
Enterprise performance management (EPM) software is increasingly incorporating various types of artificial intelligence (AI) to enhance financial planning, analysis, and decision-making processes. These AI capabilities transform traditional EPM tools into more predictive, automated, and insightful systems that can handle complex data analysis at scale. Here are some of the primary types of AI used in EPM software and their applications:
1. Machine Learning (ML)
ML, a subset of AI, is widely used in EPM software for predictive analytics, forecasting, and trend analysis. ML algorithms learn from historical data to predict future financial outcomes, such as revenue forecasts, expense trends, and cash flow projections. By analyzing patterns and relationships in data, ML can provide more accurate forecasts, helping organizations plan more effectively.
How it’s used:
• Predictive Forecasting: ML models analyze past performance data to predict future outcomes, enabling more accurate budgeting and forecasting.
• Anomaly Detection: ML algorithms identify outliers in financial data, which can indicate errors, fraud, or unusual trends needing further investigation.
2. Natural Language Processing (NLP)
NLP allows EPM tools to understand and interpret human language, making it easier for users to interact with the software using natural language queries and receive insights in easily understandable formats.
How it’s used:
• Chatbots and Virtual Assistants: NLP powers chatbots and virtual assistants that can understand user queries, provide financial reports, and offer insights in natural language.
• Data Entry and Extraction: NLP is used to automatically extract relevant information from unstructured data sources, such as invoices or contracts, reducing manual data entry.
3. Deep Learning (DL)
DL, a more complex subset of ML, is used for processing large volumes of data and identifying patterns that might not be visible to traditional analysis methods. It’s particularly useful for high-dimensional data and can enhance the accuracy of financial models.
How it’s used:
• Complex Forecasting: Deep learning models tackle complex, multi-dimensional forecasting problems, such as demand forecasting, by analyzing vast datasets including external factors like market trends and economic indicators.
4. Generative AI
Generative AI, which includes technologies like Generative Adversarial Networks (GANs), can create data that mimics real-world financial scenarios. This capability is useful for scenario planning and stress testing.
How it’s used:
• Scenario Planning: Generative AI creates realistic financial scenarios, helping organizations explore various strategic options and their potential impacts.
• Data Augmentation: Enhancing datasets with artificially generated data to improve the accuracy of financial models, especially when historical data is limited.
5. Cognitive Computing
Cognitive computing combines ML, NLP, and other AI technologies to mimic human thought processes in a computerized model. It’s used in EPM tools to provide insights and recommendations based on a comprehensive analysis of financial data.
How it’s used:
• Decision Support: Offering insights and recommendations for financial decisions by analyzing a wide range of internal and external data sources.
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• Risk Management: Identifying and assessing financial risks by understanding patterns and correlations within complex datasets.
AI Integration in EPM Software
The integration of these AI technologies into EPM software enables more automated, accurate, and insightful financial planning and analysis. For example, AI can automate routine data analysis tasks, freeing finance professionals to focus on strategic decision-making. AI-driven forecasts can adapt to changing market conditions faster than traditional methods, providing organizations with a competitive edge. Moreover, AI-enhanced reporting and insights help communicate complex financial information across the organization, building a decision-making culture based on data.
As EPM tools evolve with AI, organizations can expect these technologies to become more sophisticated, offering deeper insights, improving operational efficiency, and enabling more proactive and strategic financial management.
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Gartner Magic Quadrant 2024: Exciting EPM Software Trends
To categorize the vendors based on the types of improvements they plan to release for 2024, we can group them into four main categories: AI and Machine Learning Enhancements, Data Integration and Management, Generative AI and Natural Language Processing (NLP), and Forecasting and Scenario Planning. This categorization reflects the focus areas of innovation and enhancement in the financial planning software market, according to Gartner’s Magic Quadrant for Financial Planning Software.
AI and Machine Learning Enhancements
• Acterys: Plans to release automated AI data models for enhanced forecasting capabilities.
• Insightsoftware: Aims to integrate Azure Machine Learning to enhance planning speed and accuracy.
• Planful: Expects to deploy new AI and ML features, including AI-driven financial narrative generation.
• Prophix: Plans to enhance its back end to support complex resource management and planning models with AI.
• Workday: Plans to release its Predictive Forecaster, leveraging external data for ML time series forecasts.
Data Integration and Management
• Anaplan: Expects to enhance its data integration and management capabilities.
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• SAP: Plans to introduce planning as a service, potentially serving as the data persistence layer for SAP Analytics Cloud.
Generative AI and Natural Language Processing (NLP)
• Anaplan: Introduction of AI conversational chat, including NLQ capabilities.
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• Board: Plans to build on its ModelOps support with features based on GenAI, including LLM technology foundations.
• Jedox: Plans to release analysis and scenario planning driven by GenAI and natural language.
• OneStream: Plans to release GenAI capabilities, providing intelligence through financially aware LLM interaction.
• Oracle: Plans to release a secure cloud-based GenAI finance assistant for automated management reporting.
• Vena: Expects to leverage GenAI using Azure OpenAI for natural language questions and what-if scenarios.
• Wolters Kluwer: Plans to add NLP, AI chatbots, and enhanced anomaly detection for insights from data.
Forecasting and Scenario Planning
• IBM: Intends to offer multivariate forecasting capabilities for complex decision-making.
• Syntellis: Intends to release market-based demand planning for accurate forecasting and planning.
These categories underscore the strategic focus of each vendor, highlighting their commitment to leveraging AI, ML, and data integration technologies to drive innovation in financial planning and analysis tools. The advancements reflect a market trend towards more intelligent, efficient, and user-friendly financial planning solutions.
What to Consider when choosing an EPM Software Solution
Choosing the right Enterprise Performance Management (EPM) solution is crucial for the effective management of an organization’s financial planning, budgeting, forecasting, and reporting processes. Here are key factors to consider when selecting an EPM system:
1. Integration Capabilities
• Data Integration: Ensure the EPM solution can seamlessly integrate with your existing ERP, CRM, HRIS, and other critical business systems to consolidate data across the organization.
• System Compatibility: Check for compatibility with your IT infrastructure to avoid integration issues.
2. Scalability
The chosen EPM solution should be able to scale with your business, accommodating growth in data volume, number of users, and complexity of operations without significant performance degradation.
3. Customization and Flexibility
Assess the degree of customization the EPM solution offers to meet your specific business needs and the flexibility to adapt to changing business processes and requirements.
4. Usability and User Experience
A user-friendly interface and ease of use are crucial for ensuring high adoption rates among stakeholders. Evaluate the learning curve for new users and the availability of training and support materials.
5. Advanced Analytics and Reporting
The solution should offer robust analytics and reporting capabilities, including dashboards, visualizations, and predictive analytics, to provide deep insights into financial performance and forecasts.
6. Cloud vs. On-Premises
Decide whether a cloud-based or on-premises solution best fits your organization’s needs, considering factors like cost, security requirements, and the need for remote access.
7. Security and Compliance
Verify the EPM solution’s security features and compliance with relevant regulations (e.g., GDPR, SOX) to protect sensitive financial data and ensure regulatory compliance.
8. Performance and Reliability
Evaluate the system’s performance, particularly in processing large volumes of data, and its reliability in terms of uptime and support response times.
9. Artificial Intelligence and Machine Learning
Consider the extent to which the EPM solution incorporates AI and ML for advanced forecasting, scenario planning, and anomaly detection to enhance decision-making.
10. Vendor Reputation and Support
Research the vendor’s reputation, customer service record, and the quality of technical support offered. Consider the vendor’s stability and commitment to future development of the EPM solution.
11. Total Cost of Ownership
Analyze the total cost of ownership, including license fees, implementation costs, ongoing maintenance, and potential costs for upgrades and additional modules.
12. Collaboration Features
The ability for team members to collaborate within the EPM platform is vital for efficient planning and performance management processes.
13. Futureproofing
Consider the vendor’s roadmap for future development to ensure the solution will continue to meet your needs as new technologies and business practices emerge.
Choosing the right EPM solution involves a thorough evaluation of your organization’s current and future needs, available budget, and the specific features and capabilities that will support your financial and operational goals.
EPM Software FAQs
1. What is Enterprise Performance Management (EPM) software?
Enterprise Performance Management (EPM) software is a suite of tools designed to help organizations plan, budget, forecast, and report on their financial performance. EPM software integrates data from various sources to provide a unified view of the organization's financial health, supporting strategic decision-making. It streamlines processes, improves accuracy, and enables real-time insights, making it an essential tool for aligning financial goals with business operations.
2. How has EPM software evolved over the years?
EPM software has evolved significantly, starting from manual, spreadsheet-based processes to sophisticated cloud-based solutions. Early EPM tools were standalone systems, often siloed and error-prone. Over time, they integrated with Business Intelligence (BI) tools, enhancing reporting capabilities. The shift to cloud computing marked a major evolution, offering scalability and real-time data access. Today, the integration of AI and ML has transformed EPM tools into powerful platforms for predictive analytics, scenario planning, and automated financial processes, significantly enhancing decision-making and operational efficiency.
3. What are the key benefits of using EPM software?
EPM software offers numerous benefits, including improved financial accuracy, streamlined processes, and enhanced strategic planning. Key advantages include:
Efficiency: Automates and simplifies financial processes, reducing manual errors and time spent on data entry.
Real-Time Insights: Provides up-to-date financial data, enabling quick and informed decision-making.
Predictive Analytics: Uses AI and ML to forecast future financial trends and identify potential risks.
Scalability: Cloud-based EPM solutions can grow with the organization, accommodating increased data volumes and users.
Collaboration: Facilitates better communication and collaboration across departments, aligning financial and operational goals.
4. How do AI and Machine Learning enhance EPM software?
AI and ML enhance EPM software by bringing advanced analytics and automation to financial planning. These technologies enable predictive forecasting, anomaly detection, and scenario analysis, providing deeper insights into financial data. AI can automate routine tasks, freeing finance professionals to focus on strategic initiatives. Machine learning models learn from historical data to improve forecasting accuracy, while natural language processing (NLP) allows users to interact with the software using conversational queries. Overall, AI and ML enhance the efficiency, accuracy, and intelligence of EPM systems.
5. What should organizations consider when selecting an EPM software solution?
When selecting an EPM software solution, organizations should consider several key factors:
Integration Capabilities: Ensure the software can integrate with existing systems like ERP, CRM, and HRIS.
Scalability: The solution should accommodate business growth without performance issues.
Usability: A user-friendly interface and robust training support are crucial for adoption.
Advanced Analytics: Look for strong analytics and reporting features, including AI-driven insights.
Cloud vs. On-Premises: Decide based on your organization's needs for cost, security, and remote access.
Security and Compliance: Ensure the software meets regulatory requirements and protects sensitive data.
Vendor Reputation: Consider the vendor’s track record, customer service, and future development plans.
By carefully evaluating these factors, organizations can choose an EPM solution that aligns with their strategic goals and operational needs.
Achieve Peak Performance with B EYE’s EPM Services
B EYE’s enterprise performance management services offer a comprehensive suite for financial planning, budgeting, forecasting, and beyond, utilizing Anaplan for insightful, data-driven decision-making. Our cutting-edge solutions empower businesses to conquer operational challenges and unlock maximum profitability. Connect with us to explore how our EPM services can transform your financial planning, risk management, and operational efficiency.