Just when some light seemingly appeared at the end of the tunnel as the global pandemic waned, 2022 proved to be a challenging year for both individuals and corporations thanks to a host of other reasons. Geo-political instability due to the war in Ukraine led the headlines for most of the year. But higher fuel prices, widespread inflation, continued supply chain bottlenecks, rising interest rates and falling financial markets all played a role, too. With planning and budgeting season here, what assumptions are CFOs and Finance executives making about what lies ahead in 2023? And how are those assumptions impacting corporate planning?
For the past few years, OneStream Software has sponsored Hanover Research surveys of Finance executives to better understand how they’re helping their organizations navigate the complexities of today’s economic landscape. Hanover Research recently surveyed over 650 financial decision-makers in North America, as well as EMEA, to understand their expectations for 2023.
The survey asked about decision-maker’s expectations regarding inflation, a potential recession, supply chain disruptions, talent management, Environmental, Social & Governance (ESG) and Diversity, Equity & Inclusion (DEI) initiatives, and technology investments.
Here’s a summary of what we learned from the 2022 Hanover Research Finance Decision-Makers survey.
The takeaways from the report emphasized renewed enthusiasm towards machine learning (ML) and its impact on organizational performance. Increased economic uncertainty has emergedin recent months (e.g., inflation, tax reform, supply chain shortages, the lingering effects of the COVID-19 pandemic and a potential recession). Amid that environment, businesses continue to reallocate spending within their businesses.
With inflation continuing to plague both individuals and enterprises, price increases are the number-one way businesses have addressed inflation (56%), followed by slowed hiring or reduced specific operational costs (47%) (see Figure 1).
Almost half of businesses have slowed hiring or reduced specific operational costs, another significant increase from a year ago.
When asked how long they expect inflation to persist, three-quarters of financial leaders do not expect inflation to slow down until mid-2023 or later. This group includes one-fifth (20%) who do not expect inflation to slow down until 2024 or later, representing a shifting timeline. Last fall, half (54%) believed inflation would stabilize by the end of 2022, and earlier in 2023, under half (47%) expected inflation to slow in mid-2023 or later.
Over two-thirds of businesses regularly use cloud-based planning and reporting, and one in five (20%) report regularly using machine learning within their departments. Looking forward, over half of financial leaders predict investing more in cloud-based solutions.
Meanwhile, only one-third of companies (37%) predict investing more in machine learning, which is significantly fewer companies than predicted both last fall and earlier in 2023 (see Figure 2).
When asked about the top use cases for artificial intelligence or machine learning, financial leaders surprisingly identified financial reporting as the top opportunity in the fall 2022 survey. The financial reporting use case was followed by sales/revenue forecasting (41%) and demand planning (39%) as the second and third largest opportunities for organizations, respectively (see Figure 3).
With ESG reporting guidelines converging and new mandatory disclosure requirements being proposed by the US SEC and regulators in other countries, investments in ESG and DEI remain a priority. Half of the organizations surveyed expect to invest more in DEI and ESG goals and initiatives in 2023 compared to 2022 investments. This change is a significant drop compared to expectations from earlier in 2023 (65% in DEI and 60% in ESG). Still, over a third of enterprises expect to maintain their 2022 investment levels in both DEI and ESG in 2023 (38% and 39%, respectively) (see Figure 4).
When asked about their plans to prepare for changing ESG Reporting requirements, nearly half of the financial executives surveyed have started or plan to start forming an internal ESG/Sustainability team to define policies and disclosures. A similar proportion (41%) will begin (or have already begun) implementing new ESG/sustainability policies. Compared to earlier in 2022, fewer are planning to invest in software to support ESG data collection and reporting. Among those who currently don’t have a plan in place, half (50%) indicate they may implement a plan if ESG reporting mandates impact their organizations (see Figure 5).
The results of the recent Financial Decision-Makers Survey highlight the ongoing business challenges CFOs and Finance leaders face as they look to drive performance ahead in 2023. Inflation, higher interest rates, supply chain bottlenecks and recession are here to stay, and most Finance executives expect those challenges to continue into 2023.
The good news is that today’s cloud-based analytical software technologies are seeing increased adoption and proving their worth in helping Finance teams become more efficient, plan and navigate a volatile economic landscape, and increase their agility to respond. Artificial intelligence and machine learning adoption still lag behind mainstream planning and predictive analytics tools. But as these capabilities are embedded into modern planning, reporting and analytical software applications, Finance adoption is poised to expand rapidly.
This report delves into the latest trends in modern planning, reporting and analytics software applications, from predictive analytics to artificial intelligence and machine learning. And with expert insights from leading CFOs, you’ll gain valuable knowledge and actionable strategies to stay ahead of the game with more effective planning and budgeting.
To learn more,– download our CFO Executive Outlook Report today to gain a competitive edge in 2023.
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Machine learning (ML) has no doubt revolutionized how to handle data in the 21st century. Thanks to the ability to identify patterns and relationships within vast amounts of data, ML has become an essential tool in various fields, including Enterprise Performance Management (EPM).
Traditionally, technology limitations constrained how EPM could be used to monitor, analyze and manage business performance. EPM involves budgeting, forecasting, financial consolidation, reporting and more. Today, ML can significantly improve the accuracy, transparency and agility of EPM processes. How? By automating these activities and providing insights previously impossible to obtain.
As we shared in the first post of the Sensible ML for EPM blog series, today more than ever, organizations are looking to become more accurate, transparent and agile with their financial plans to stay competitive. And OneStream’s Sensible ML can help. How? It allows users closer to the business to infuse business intuition into the model, which can increase accuracy and ensure all the available information is considered.
Unlike the forecasting capabilities of “most” predictive analytics (which look at prior results and statistics and then generate forecasts based on past events), Sensible ML has unique sophistication. Sensible ML also considers additional business intuition, such as events, pricing, competitive information and weather to help drive more precise/robust forecasting (see Figure 1).
Sensible ML’s speed in responding to evolving business environments offers a clear advantage over traditional approaches. While a statistical-based system means planning teams often wait several weeks – or months! – for the financial and non-financial results needed to produce forecasts that respond to changes, Sensible ML can achieve the same result much, much faster. And it does so with a massive reduction in manual effort.
Forecasting is a critical activity that helps companies predict future demand, mitigate potential risks and capitalize on emerging opportunities. Due to the increasingly volatile environment, however, businesses are forced to depart from traditional forecasting methods, siloed processes and legacy technologies. Instead, businesses are focused on digitally evolving their forecasting capabilities and operations, aiming to mitigate the risk of continued value leakage throughout the company.
One of the most significant benefits of applying machine learning to EPM is that ML helps improve the accuracy of financial forecasts and predictions. Machine learning algorithms can analyze historical financial data and identify patterns that can be used to make more accurate predictions about future performance.
For example, a machine learning model can analyze data from sales transactions, inventory levels and customer demographics to identify patterns that can be used to predict future sales. By using these predictions to adjust resource allocation and inventory management, organizations can improve their financial performance and reduce the risk of stockouts or overstocks.
Machine learning can also help improve the accuracy of financial reporting. For example, ML algorithms can be trained to analyze financial statements and identify errors or discrepancies potentially missed by human auditors. Automating this process helps organizations improve the accuracy of their financial reporting and reduce the risk of non-compliance.
Machine learning is frequently referred to as a black box – data goes in, decisions come out, but the processes between input and output lack transparency.
Many solutions, especially those reliant on integration with a third-party ML solution, simply allow an organization to run the ML process. The results then get returned with no ability to understand how they were generated.
Consequently, many ML solutions now face increased skepticism and criticism as people question whether their decisions are well-grounded and reliable. Thus, the “transparency and traceability” of ML solutions are becoming increasingly important.
Sensible ML delivers both, improving the transparency of financial and non-financial reporting. By analyzing data from multiple sources, Sensible ML models provide a comprehensive view of an organization’s financial health (see Figure 2).
For example, machine learning can analyze data from financial statements, sales transactions and inventory levels to provide a more accurate picture of an organization’s financial performance. This comprehensive view can help identify areas where resources may be misallocated or opportunities for growth that may have been overlooked.
Machine learning can also be used to improve the transparency of financial audits. By automating the audit process, ML algorithms can identify potential errors or discrepancies more quickly and accurately than human auditors. This capability not only helps reduce the risk of fraud or other financial improprieties but also improves the accuracy of financial reporting.
As the pace of change increases – and disruption and uncertainty become more commonplace –organizations must increasingly not only recognize the signs that indicate change but also put in place a plan to react to the possible scenarios that result from any changes. ML-enriched forecasts provide a consistent process, framework and collaborative environment that enables organizations to react with agility and certainty in the face of uncertainty and constant change and disruption.
Applying machine learning to EPM comes with a significant benefit: ML can help organizations be more agile. By processing and analyzing data in real time, machine learning models can provide insights that enable decision-makers to make faster, more informed decisions.
Machine learning can also help organizations be more agile in financial planning and forecasting. By analyzing data in real time, ML models can identify changes in market conditions or customer behavior that may impact financial performance. This capability enables organizations to adjust their financial plans and forecasts quickly and stay ahead of potential challenges.
Sensible ML makes forecasting easy because OneStream breaks down the barriers that have traditionally held back Finance and Operations teams and others from embracing ML within core planning processes. While ML has powerful potential to help scale work like never before, organizations face several challenges when using traditional machine learning (see Figure 3).
Sensible ML enables organizations to more quickly and accurately foster success with the following use cases (see Figure 4):
Machine learning is here to stay. Accordingly, the Office of the CFO should now be looking to take advantage of Sensible ML and similar advancements in technology. What do FP&A leaders have to lose by adding another point of view or enriching their insights with the help of ML? Nothing, nothing at all.
At OneStream, we call this Intelligent Finance.
To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our Sensible ML blog series or download our white paper here.
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Scenario planning is a valuable tool for businesses looking to prepare for the unexpected, but creating accurate scenarios can be a complex and time-consuming process. Traditionally, these exercises required substantial iterative cycles and were very manual.
That’s where artificial intelligence (AI) and machine learning (ML) forecasting come in – these technologies can help businesses power their scenario plans with more accurate and reliable data, allowing them to make better-informed decisions and stay ahead of the curve.
Scenario planning involves creating multiple possible futures for a business, considering a range of different variables such as market trends, consumer behavior, and technological advancements. The process typically involves identifying key drivers of change, developing a range of plausible future scenarios, and assessing the potential impact of each scenario on the organization.
The goal is to identify potential risks and opportunities and prepare accordingly rather than simply reacting to events as they happen. Scenario planning can help organizations make more informed decisions by enabling them to anticipate potential future events and develop strategies to mitigate risks and take advantage of opportunities. (see figure 1)
Scenario planning involves creating multiple possible futures for a business, considering a range of different variables such as market trends, consumer behavior, and technological advancements. The process typically involves identifying key drivers of change, developing a range of plausible future scenarios, and assessing the potential impact of each scenario on the organization.
While scenario planning can be a powerful tool, creating accurate scenarios can be a challenge. Traditional scenario planning methods can be time-consuming and challenging to execute. One of the main challenges is forecasting. Forecasting involves predicting future events, such as changes in consumer behavior, market trends, and technological advancements.
Traditional forecasting methods often rely on historical data and expert opinions, which can be unreliable and may not reflect current market conditions or emerging trends. Additionally, traditional forecasting methods may not account for the complex interrelationships between different factors that can influence future events. It’s difficult to predict exactly how different variables will interact, and human biases can creep in, leading to scenarios that are overly optimistic or pessimistic.
That’s where AI and ML forecasting comes in.
Advances in AI and ML have made it possible to enhance scenario planning by providing more accurate and reliable forecasts. AI and ML can analyze vast amounts of data and identify complex patterns and relationships between different factors. This can enable organizations to develop more sophisticated and accurate forecasts that reflect current market conditions and emerging trends.
By incorporating AI and ML forecasting into scenario planning, businesses can create more realistic and useful scenarios, helping them to make better-informed decisions and stay ahead of the curve.
AI and ML can help organizations analyze large amounts of data and identify patterns and trends that are not visible to humans. This can provide insights into potential future scenarios and help organizations prepare for them.
Use Case: Enrich Data to Identify Patterns
AI and ML can be used in scenario planning by incorporating external data sources, such as social media, news articles, and weather forecasts to help understand to what extent these factors correlate with forecast performance. By analyzing these sources in real time, organizations can identify emerging trends and adjust their scenarios accordingly. (see figure 2)
For example, a manufacturer might use AI to analyze social media conversations about its products and identify emerging customer preferences. By incorporating this information into its scenarios, the manufacturer can adapt its product development and marketing strategies to meet customer needs better.
AI and ML can be used to predict future outcomes based on historical data. This can help organizations identify potential future scenarios and make informed decisions about how to respond to them.
Use Case: Predicting Consumer Behavior
One key variable in many scenarios is consumer behavior. Businesses need to understand how consumers will respond to new products, changes in pricing, and other factors in order to make informed decisions. AI and ML forecasting can be used to analyze consumer data and predict how consumers will behave in the future. This information can be used to create more accurate scenarios and identify potential risks and opportunities. (see figure 3)
For example, consider a retail company that is considering launching a new product. By using AI and ML forecasting to analyze consumer data, the company can predict how many units of the product it’s likely to sell in different scenarios. This information can be used to create different sales forecasts for different scenarios, allowing the company to prepare accordingly.
AI and ML can be used to create simulations of potential future scenarios. This can help organizations understand the potential impact of different decisions and prepare for them accordingly. (see Figure 2)
Use Case: Forecasting market trends
Market trends are another important variable in scenario planning. Businesses need to understand how the market is likely to change in the future in order to make informed decisions. (see figure 4)
For example, consider a financial services company that is creating scenarios for the next five years. By using AI and ML forecasting to analyze market data, the company can predict how interest rates, inflation, and other key variables are likely to change over that time period. This information can be used to create different economic scenarios, allowing the company to prepare accordingly.
AI and ML can be used to optimize scenarios by identifying the most likely outcomes and helping organizations prepare for them. This can help organizations be more effective in their scenario-planning efforts.
Use Case: Predicting Supply Chain Disruptions
Supply chain disruptions can have a significant impact on businesses, especially those that rely on just-in-time inventory or complex global supply chains. AI and ML forecasting can be used to analyze supply chain data and predict where disruptions are most likely to occur. (see figure 5)
For example, imagine a manufacturing company is creating scenarios for the next year. By using AI and ML forecasting to analyze supply chain data, the company can predict where disruptions are most likely to occur – for example, due to natural disasters or political unrest. This information can be used to create different scenarios for supply chain disruptions, allowing the company to prepare accordingly.
In each of these examples, AI and ML forecasting allows businesses to create more accurate and realistic scenarios, helping them to make better-informed decisions and stay ahead of the curve.
AI and ML technologies have been a catalyst for organizations to relook at how they leverage scenario plans, the pace at which they plan decisions, and the data they use to make those decisions. Customers can overcome the tedious and time-consuming scenario planning by enriching the process with AI and ML solutions by providing faster, more accurate and reliable forecasts.
To learn more about how FP&A teams are moving beyond the AI hype to enrich scenario planning, check out our white paper, Sensible Machine Learning for CPM – Future Finance at Your Fingertips.
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Machine learning (ML) has the potential to revolutionize Enterprise Performance Management (EPM) by providing organizations with real-time insights and predictive capabilities across planning and forecasting processes. With the ability to process vast amounts of data, ML algorithms can help organizations identify patterns, trends and relationships that would otherwise go unnoticed. And as the technology continues to evolve and improve, even greater benefits are likely to emerge in the future as Finance leverages the power of ML to achieve financial goals.
Join us as we examine Sensible ML for EPM – Future of Finance at Your Fingertips.
For CFOs, whether artificial intelligence (AI) and ML will play a role across enterprise planning processes is no longer a question. Today, the question instead focuses on how to operationalize ML in ways that return optimal results and scale. The answer is where things get tricky.
Why? Business agility is critical in the rapidly changing world of planning. To think fast and move first, organizations must overcome challenges spanning the need to rapidly grow the business, accurately predict future demand, anticipate unforeseen market circumstances and more. Yet the increasing volumes of data across the organization make it difficult for decision-makers to zero in on the necessary data and extrapolate the proper insights to positively impact planning cycles and outcomes. Further exacerbating the problem, many advanced analytics processes and tools only leverage high-level historical data, forcing decision-makers to re-forecast from scratch whenever unforeseeable market shifts hit.
But with AI and ML, business analysts can analyze and correlate the most relevant internal/external variables. And the variables then contribute to forecasting accuracy and performance across the Sales, Supply Chain, HR and Marketing processes that comprise financial plans and results.
Over the coming weeks, we’ll share a four-part blog series discussing the path toward ML-powered intelligent planning. Here’s a sneak peek at the key topics in our Sensible ML for EPM series:
Regardless of where you are in your Finance journey, our Sensible ML for EPM series is designed to share insights from the experience of OneStream’s team of industry experts. We recognize, of course, that every organization is unique – so please assess what’s most important to you based on the specific needs of your organization.
The aspiration of ML-powered plans is nothing new. But to remain competitive amid the increasing pace of change and technology disruption, Finance leaders must think differently to finally conquer the complexities inherent in traditional enterprise planning. ML has the potential to greatly improve EPM by providing organizations with real-time insights and predictive analytics. However, organizations must overcome challenges (e.g., ensuring good data quality and selecting the right ML algorithm) to achieve success. As ML continues to evolve, increasingly more organizations are likely to leverage its power to drive better financial and operational outcomes.
Several challenges lie ahead for organizations of all sizes, but one of the most important decisions will be implementing the right ML solution – one that can effectively align all aspects of planning and elevate the organization toward its strategic goals. Sensible ML answers that call. It brings power and sophistication to organizations to drive transparency and increase the velocity of forecasting processes with unprecedented transparency and alignment to business performance.
To learn more about the value of Sensible ML, download our whitepaper titled “Sensible Machine Learning for CPM – Future Finance at Your Fingertips” by clicking here. And don’t forget to tune in for additional posts from our machine learning blog series!
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As organizations begin their evaluations of potential enterprise performance management (EPM) software vendors, industry analyst reports are a great resource for identifying viable solutions. Some industry analyst reports are based on analyst opinions of the various vendors built through briefings, demonstrations, and customer references. Others are based more on customer surveys and reviews, providing a clear assessment of how actual customers view the software vendor and the value they are getting from their solutions. This is often referred to as the “wisdom of crowds.”
A good example of an industry analyst report that is driven mostly by customer reviews is the recently published Dresner Advisory Wisdom of Crowds® 2022 Enterprise Performance Management (EPM) Market Study.
The 2022 Wisdom of Crowds® EPM Market Study builds on the previous seven years of Enterprise Planning and EPM Market Studies published by Dresner Advisory and reflects the shift in the market towards a more holistic approach to performance management vs. relying on individual point solutions.
According to Dresner Advisory, an enterprise performance management system is a key element of performance management. It allows an organization to plan for the impact of various internal and external factors on its future performance and business outcomes. This includes strategic, operational, and financial planning and forecasting. EPM systems also include reporting and analytics capabilities that allow organizations to set goals and objectives and monitor performance against those objectives.
EPM software systems can vary significantly in complexity and automation capabilities, from relatively straightforward spreadsheet replacements to sophisticated multi-user systems that support collaborative planning, provide a wide range of analytics, and use advanced technologies such as in-memory computing and machine learning
This year’s report highlighted several key market trends, including the following:
What’s unique about this study is that the results are based 100% on surveys of customers using EPM software. Vendors are evaluated based on 33 criteria covering:
This was OneStream’s fifth year of inclusion in the Dresner Advisory Wisdom of Crowds Study and, once again, the results were outstanding. Each vendor was evaluated on 33 criteria, and as you can see in the spider chart below, OneStream Software is substantially above the overall sample for all measures, best in class for 9 measures and we received a perfect “5” recommend score.
Dresner Advisory provides two models to help clients understand the EPM market. Their Customer Experience Model positions vendors based on their combined scores on Product/Technology vs. Sales and Service metrics on two axes, positioning vendors into one of four quadrants.
Their Vendor Credibility Model considers how customers “feel” about their vendor, plotting value for the price paid against the integrity and recommending measures, creating a “confidence” dimension. The upper-right quadrant in both models contains the highest-scoring vendors, and those considered leaders in customer experience and vendor credibility.
Based on our scores, OneStream was positioned as an Overall Leader in both the Customer Experience and Vendor Credibility models. Here’s a view of how the various vendors are positioned in the Customer Experience model.
Commenting on OneStream’s results in the study, Howard Dresner, Founder and Chief Research Officer at Dresner Advisory Services said, “In 2022, OneStream received outstanding results across virtually all measures and is an Overall leader in the Customer Experience Model and Vendor Credibility Models. Customers rank the company best in class for sales professionalism, responsiveness, flexibility/accommodation and business practices, product robustness/sophistication of technology, reliability of technology, integration of components within product, ease of upgrade/migration to new versions, and technical support time to resolve problems and responsiveness. Additionally, it maintains a perfect recommend score. We applaud OneStream on their 2022 rankings and their continued recognition in our annual market survey.”
Making every customer a reference, one success at a time is the mission of OneStream Software and is our top priority companywide. Being named a leader in Customer Experience and Vendor Credibility by Dresner Advisory Services validates our approach and recognizes the ability of OneStream to address the advanced planning and performance management requirements of global enterprises.
To learn more, download a copy of the 2022 Dresner Advisory Enterprise Performance Management Market Study.
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The Oracle Hyperion enterprise performance management (EPM) applications have been in the market for over 20 years and have delivered a great deal of value for many customers. But as demand for EPM applications has shifted to the cloud, Oracle has reduced its investment in the Hyperion on-premise applications and is encouraging customers to migrate to the Oracle EPM Cloud applications.
This is creating a critical decision point for Hyperion EPM customers and is begging the answer to several questions. What is the future of Hyperion EPM? Will the Oracle EPM Cloud applications meet my needs and what will it cost to upgrade? What other options are available in the market? Read on to learn the answers to these questions.
Thousands of organizations around the world are relying on multiple Oracle Hyperion EPM applications to support their critical finance processes. This includes products such as Hyperion Financial Management, Hyperion Planning, Hyperion Strategic Finance, Hyperion Profitability, Cost Management, and others. These were market-leading products for many years, and customers have received great value from them. However, the fragmented nature of these products has created extra work and costs, including the following:
In addition, over the past few years, there has been limited innovation and declining support for these legacy products. And now, with the end of support having passed on 12/31/21 for older versions of these products – we have reached a decision point for Hyperion customers. The proverbial “fork in the road.”
Which path will you choose? Let’s look at the options available.
Path #1 – Upgrade to Oracle Hyperion EPM 11.2
The first option for customers facing the end of support for Hyperion 184.108.40.206 or older versions is to upgrade to Oracle Hyperion EPM 11.2. Oracle has communicated that customers upgrading to this version of the Hyperion applications will be supported through 2031. However, very little innovation is expected on these products, and Oracle has already communicated that some modules were deprecated and are no longer supported.
Path #2 – Convert to Oracle EPM Cloud
The second option is to convert to Oracle EPM Cloud versions of the on-premise Hyperion applications. The main advantages here are that moving to the cloud removes the infrastructure and IT support requirements, and upgrades to new releases are easier. However, this path basically amounts to a re-implementation of the applications, which are still fragmented, with multiple points of maintenance and data integration. And in some cases, the EPM Cloud applications offer more limited functionality than was provided in the on-premise Hyperion applications. Does anyone really want to go backward in functionality?
Path #3 – Convert to Another Solution
The third option is to convert to another solution – such as OneStream. While there will certainly be some time, effort, and costs required here – over 400 former Oracle Hyperion customers have converted to OneStream and have never looked back. Why? Because OneStream is a unified platform that replaces multiple Hyperion applications – so it’s easier to use and maintain and reduces total cost of ownership. And OneStream is “function-forward,” meaning customers get more advanced capabilities than they had before.
With over 900 organizations and more than 160,000 users globally, OneStream has been recognized as a market leader by IT industry analyst firms such as Gartner, IDC, Dresner Advisory Services, Nucleus Research, and others and has received the Gartner Peer Insights Customers’ Choice recognition in both Cloud Financial Close and Cloud FP&A solutions.
Our customers are using OneStream for planning, financial close & consolidation, reporting, analytics, account reconciliations, and more. In fact, 70% of our customers replaced multiple legacy applications such as Oracle Hyperion, SAP BPC, and IBM Cognos.
These organizations are achieving many benefits, including the following:
With the end-of-life looming, it’s a critical decision point for Hyperion EPM customers. What is the future of Hyperion? For customers who elect to upgrade to Oracle Hyperion 11.2 the future means you can get support, but limited innovation, which means limited ability to digitally transform your Finance operations. Migrating to Oracle EPM Cloud is an option, but buyer beware, you’ll face a lot of the same challenges that you face in managing and maintaining multiple Hyperion on-premise applications.
Check out our white paper titled “Why Now is the Time to Convert From Oracle Hyperion Applications” to learn why over 400 organizations have chosen to convert from Oracle Hyperion to OneStream’s unified CPM software platform. And contact OneStream if you would like a conversion assessment, where our team of experts will help you perform an ROI analysis of converting from Oracle Hyperion to OneStream.
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Oracle Hyperion enterprise performance management (EPM) applications have led the market for several years, providing trusted and reliable capabilities to organizations worldwide. These applications were developed one by one to form what is largely known as the Oracle Hyperion EPM Suite.
In the suite, the key EPM processes – such as financial consolidation, planning, financial reporting and others – were provided in separate applications and reference data, and data would be moved between applications via integrations. To give the appearance of a suite, several shared services were created to handle reference data uploads and synchronization along with user creation and security across the products.
But make no mistake, despite ‘integrations’ – the products are very much fragmented – and so is the user experience for Finance teams at large, sophisticated organizations.
Oracle is now pushing their customers towards an inflection point.
The terms enterprise performance management (EPM) and corporate performance management (CPM) have been in use in the market for at least 20 years. These terms are both used to describe a similar set of management processes, although there are some subtle differences in the intended meaning and scope of these processes and the software used to support them. What is EPM and how does it differ from CPM? Read on to learn more.
Enterprise Performance Management (EPM) Defined
According to IT industry research firm Gartner’s EPM definition: “Enterprise Performance Management (EPM) is the process of monitoring performance across the enterprise with the goal of improving business performance.” While monitoring performance across the enterprise is part of EPM, I’ve always preferred to think about EPM more broadly. In my view, EPM is a set of management processes and a system designed to help organizations achieve their financial objectives by linking their strategies to plans and execution in a continuous management cycle.
These management processes include the following: Goal Setting, Modeling, Planning, Financial Close & Consolidation, Reporting, and Analysis. The continuous management cycle and relationship of these processes can be seen in Figure 1 below.
Figure 1 – The Performance Management Cycle
How Does EPM Compare to ERP Systems?
An EPM system integrates and analyzes data from many sources, including, but not limited to, ERP systems, HCM, CRM, and Supply Chain applications, data warehouses, and also cloud and external data sources. And this brings up an important point and differentiator about EPM vs. ERP and other enterprise systems. While ERP’s and other systems help organizations “run the business”, EPM systems help organizations “manage the business.”
What does that mean? What it means is that EPM software systems provide management with data analytics and insights across multiple operational systems and processes (see Figure 2). EPM solutions provide agility in forecasting and strategic planning, reporting, and decision-making. And they help organizations create alignment across the enterprise.
Figure 2 – EPM/CPM Systems Integrate Multiple ERP Systems
How Does EPM Compare to CPM?
So now you might be asking, how does enterprise performance management (EPM) differ from corporate performance management (CPM)? The answer is – they are essentially the same. And for that matter so are terms such as business performance management (BPM) and financial performance management (FPM).
The latter term, FPM, is by nature more aligned to the Finance function, and CPM sounds more aligned to managing “corporate” functions. The EPM term was clearly intended to sound broader, encompassing management processes across the enterprise. But again, these terms are used synonymously in practice depending on the organization, and especially by different software vendors.
Alternative Software Approaches to EPM
This brings us to the next topic – what type of software is available to support EPM? The answer is that there are basically three alternative enterprise performance management software approaches here:
Spreadsheets – are the “go-to” EPM solution for many Finance processes and can suffice in a small enterprise. But organizations often outgrow the spreadsheet approach to budgeting and planning, and they don’t provide adequate controls and audit trails when used for financial consolidation and reporting.
GL/ERP Systems – the general ledger module found in most ERP systems does provide the ability to capture budgets, produce financial statements, and comparisons of actuals vs. budget. But these EPM products aren’t designed to support the budget data collection process or consolidate financial results from multiple GL/ERP systems. And the management reporting capabilities are limited in GL/ERP systems.
Purpose-Built EPM Applications – these applications have been available in the market for over 20 years and are the preferred approach to supporting EPM processes in organizations that have complexity that cannot be handled by the spreadsheet approach. They provide the ability to integrate data from multiple GL/ERP systems and have specific automation functionality required to support EPM processes such as budgeting & planning, financial close and consolidation, financial and management reporting, and various types of analysis including risk and impact analysis.
While purpose-built EPM applications were initially delivered as on-premise software, these applications are now available as cloud-deployed solutions with subscription pricing, also known as software as a service (SaaS). One caveat to be aware of is that not all EPM applications are created equally. Some are focused only on budgeting and planning, others only on financial close. And some vendors provide multiple applications to support the various management processes while others support them through a single EPM tool or platform.
Figure 3 – OneStream’s Unified Intelligent Finance Platform
For example, OneStream Software provides a single, unified platform (see figure 3) that supports all the EPM processes in the management cycle described earlier in this article. Customers who adopt OneStream are typically replacing spreadsheets they have outgrown, multiple legacy EPM applications, or cloud-based point solutions.
Whether you call it EPM, CPM, or some other term – a continuous management process that helps organizations link their strategies to their plans and execution is essential to creating and sustaining the corporate agility required to navigate rapidly-changing business and economic conditions. Spreadsheets and email can support the EPM needs of small enterprises, but purpose-built EPM software applications are becoming the preferred approach for most mid-sized to large enterprises with any level of complexity.
To learn more about EPM software and how various vendors in the market compare, download the Dresner Advisory 2021 EPM Market Survey report.
Contact us to learn more about the benefits of OneStream’s unified EPM software for your company.
Like all things that change over time, the corporate performance management (CPM) or enterprise performance management (EPM) landscape has been through several key changes. Many of you may remember the consolidation of smaller niche vendors nearly 15 years ago when OutlookSoft, Cartesis, Hyperion, Cognos and TM1, to name a few, were swallowed up by those mega-vendors of today – SAP, Oracle and IBM. The critical importance of CPM in Finance Transformation continues to grow.
With increasing market demand for Finance Transformation, newer CPM vendors have since flourished alongside continual market growth and increasingly more organisations recognising the need for such solutions. Indeed, the mega-vendors now have multiple areas of focus, outside of CPM, and have probably found themselves with declining CPM businesses, which only contribute a small percentage of mega-vendors’ overall revenues.
Not surprisingly, then, there is a natural push from legacy vendors to divert focus and attention towards full enterprise resource planning (ERP), human capital management (HCM), customer experience and/or supply chain opportunities to drive higher revenue and secure more of those larger deals. But there is a problem with that. Sometimes this approach dilutes the value of CPM, which is definitely not ideal.
De-Emphasising CPM Dilutes Finance Transformation
De-emphasising CPM ultimately has a negative effect. The result? The dilution of Finance Transformation. Specifically, what has transpired is the collapse of the lines between ERP and CPM businesses as a result of reallocating investments away from delivering true innovation. Instead, the focus has been on aligning the look and feel of the ERP and CPM solutions, creating a perception that CPM and ERP are the same, or at least interchangeable.
Legacy vendors are also willing to further dilute the value of CPM/EPM by heavily discounting CPM into multi-million-dollar ERP, HCM and supply chain software deals. While this approach might work for IT groups intent on using a single vendor, potential consequences abound. Here are just a few ways de-emphasising CPM impacts Finance teams:
While preparing for the next step in the transformation journey, Finance leaders must, must, must understand the key differences between ERP and CPM. And for organizations preparing to evaluate new software for Finance Transformation, understanding ERP and CPM is especially critical.
ERP Systems Run the Business
Transactional systems such as ERP are best used to ‘run’ the organisation. Any number of these systems can be present in a global organisation and can come from multiple suppliers.
While the term was first used in the 1990s by the Gartner Group, ERP systems actually have deep roots in the manufacturing industry and can trace their history back to the 1960s. At that time, manufacturers needed a better way to manage, track and control inventory.
Today, ERP is generally referred to as a category of business management software – and typically a suite of integrated applications – that an organization can use to collect, store, manage and interpret data from many business activities. Here are some examples of the business activities ERP systems help automate and track:
CPM Systems Manage the Business
The CPM or EPM solution (see Figure 1) is the management layer above all transaction systems. CPM software provides a level of agility and visibility which is now critical for any organisation that wants to successfully handle the complexities of growth and change. With an effective management layer in place, organisations can upgrade or replace underlying ERP/GL systems. And it can be done without disrupting critical management processes, such as planning and reporting, during the transition period.
Essentially, CPM systems monitor performance across the enterprise with a key goal at the centre of it all: improving business performance. A CPM system integrates and analyses data from many sources, including from applications across the organization such as the front-office, e-commerce systems, back-office, data warehouses and external data sources. Here are a few of the key management processes supported by CPM:
CPM Investments Are Agnostic to M&A Strategy
During a recent customer conversation, the customer explained how a OneStream CPM investment was a key part of his organisation’s M&A strategy. The company recently made a very large acquisition and had very little time or information to integrate the financial systems. To integrate or consolidate systems at the transactional ERP layer would take months or even years, not to mention cause major disruptions in day-to-day business operations. Comparatively, integrating the acquired company into OneStream can take just a matter of hours to a few days depending on the company size.
The Next Generation CPM Emerges
OneStream entered the CPM market to offer something completely different to the fragmented CPM systems acquired and developed by mega-vendors. To offer something better – a single unified platform to bring together all the key CPM processes and analytics in one place (see Figure 2).
The strength of the OneStream platform takes organisations to an entirely new level of CPM, one where they can move away from only viewing key data and ratios at the month end to receiving weekly or even daily signals. As a result, action can be taken at the speed of the organisation. OneStream is therefore empowering its customers to “lead at speed.”
With OneStream, multiple integrations down to source systems to feed each CPM process are not necessary. Instead, the intuitive workflow ensures data is loaded from ERPs only once and then becomes immediately available in any required process, such as planning and financial consolidation.
Thanks to OneStream’s ability to re-use core components of dimensionality, different hierarchies/business structures – which previously often resulted in separate CPM instances – can now be accommodated in a single platform.
That’s why over 700 organisations have chosen to convert from multiple legacy CPM products to OneStream’s Intelligent Finance platform – and they’ve never looked back. These organisations have achieved abundant benefits. Here are just a few:
When it comes to CPM vs. ERP, leaving CPM behind for an ERP system is not a great move for any medium to large organisation. A CPM solution is a critical investment for such organisations – one that ensures an effective management layer is in place. CPM solutions deliver key information and reporting relating not only to the performance of the business but also to how to manage effectively and take the right decisions going forward. In other words, CPM complements and integrates with transactional systems such as ERPs, which are key to running the business – true Finance Transformation relies on both CPM and ERP systems working together.
To learn more, click here to read about how our customer Xylem facilitates better, faster business decisions using OneStream.
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