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.

Creating Accurate, Transparent & Agile ML-Driven Forecasts

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).

Figure 1:  Sensible ML Process Flow

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. 

Increased Forecast Accuracy = More Effective Business Processes Downstream

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.

Transparency Is Critical for the Adoption of ML Forecasts for all Stakeholders Involved

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).

Figure 2:  Sensible ML Dashboard

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.

Agility Increases More Avenues of Value Creation in Response to Changing Conditions

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

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).

Figure 3:  Sensible ML Solves for Traditional ML Challenges

Sensible Use Cases Foster Success

Sensible ML enables organizations to more quickly and accurately foster success with the following use cases (see Figure 4):

        Figure 4:  Sensible ML Use Case Matrix


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.

Learn More

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.

Download the White Paper

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.

Powering Scenario Plans with AI & ML Forecasts

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.

Scenario Planning Process
Figure 1: Scenario Planning Process

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.

The Role of AI and ML in Scenario Planning

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.

Data analysis

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)

Sensible ML Feature Library
Figure 2: Sensible ML Feature Library

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)

Sensible ML Prediction
Figure 3: Sensible ML Prediction

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)

Sensible ML Workspace
Figure 4: Sensible ML Workspace

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)

Scenario Planning Sensible ML Analysis Overview
Figure 5: Sensible ML Analysis Overview

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.

Learn More

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.

Download the White Paper

Artificial intelligence (AI) and machine learning (ML) have revolutionized many industries, but the field of financial planning & analysis (FP&A) has been slow to adopt this technology.  Despite the numerous benefits AI – and more specifically, ML – can bring to Finance (e.g., increased efficiency, accuracy and strategic insights), many organizations still hesitate to implement either in their FP&A processes.  What’s holding FP&A back from reaping the vast benefits of ML?

To answer this question and more, this blog will explore some of the challenges holding FP&A back from fully embracing ML and how those challenges can be overcome.

Market Appetite for ML

While not yet as widely accepted as the move to the cloud for the financial close and planning processes, ML adoption is already increasing, according to the 2022 Data Science and Machine Learning Market Study by Dresner Advisory Services.  In 2016, less than 40% of responding organizations reported using or actively exploring ML.  That same metric was about 70% in 2022 (see Figure 1), showing a steady increase over the last seven years.  On the surface, that progression underscores the AI hype and excitement for the potential benefits of using AI for FP&A.

Figure 1:  Dresner Advisory Wisdom of Crowds® Data Science and ML Market Survey

But what happens if the data gets broken down by function?  A bit of a different reality emerges for the Office of Finance and FP&A.

In fact, the study shows that only 20% (see Figure 2) of Finance organizations are currently using AI and ML, and Finance actuals lag most functions, despite all the buzz and chatter out there.

Figure 2:  Deployment of AI and ML by Function

What’s Holding FP&A Back?

With so much buzz yet low adoption, what key barriers are holding FP&A and Operations teams back from mainstream adoption of ML solutions?  Figure 3 depicts the barriers.

Figure 3:  AI Barriers to Entry for FP&A

Below, the details about these key barriers show why they’re preventing widespread implementation of cutting-edge ML technologies:

Lack of Expertise
Lack of Scale
Lack of Business Intuition & Transparency
Figure 4:  AI in Current CPM Solutions

As a strategic business partner, FP&A must instill confidence in forecasting processes.  And while leveraging AI and ML is likely to increase forecast accuracy, P&L owners cannot assess the drivers that comprise forecasts – P&L leaders who can’t will never own their forecasts.

And if P&L owners don’t own their forecasts, forecasting processes break down and fail altogether.  That means FP&A has failed too.

Fragmented & Disconnected Processes


Despite these challenges, ML has the potential to significantly improve Finance operations and outcomes.  By automating manual processes, ML can help Finance professionals save time and improve accuracy, which can lead to more effective decision-making.  Additionally, ML can provide real-time insights into financial performance.  Those insights can then help Finance professionals identify trends and make informed decisions.

As AI and ML for FP&A enter the mainstream, organizations will undoubtedly have several choices to consider.  On one spectrum, solution vendors for AI (see Figure 5) are offering everything from AI infrastructure solutions to data science toolkits and complete AI platforms to create and deploy ML models.  While these are powerful tools addressing varying use cases, the tools aren’t designed for FP&A teams.

Figure 5:  AI General Vendor Landscape

Corporate performance management vendors are also investing in AI capabilities to support extended planning & analysis (xP&A) processes such as demand planning and sales planning.  As Figure 5 illustrates well for AI vendors, CPM vendors will also solve their customers’ AI needs in different ways.

So then, what’s the lesson in all this?

Don’t let AI hype cloud the evaluation process.  Start with a clear understanding of “what” business outcomes the FP&A team is trying to achieve with ML.  Identify “who” is using the solution and “how” the solution is unified into existing planning processes.

And with answers to these questions in mind, use the evaluation process to “get under the hood” to learn whether the solution will unleash the organization from the key barriers holding FP&A back from moving beyond the hype.

Learn More

Want to learn more about how FP&A teams are moving beyond the AI hype?  Stay tuned for additional posts from our blog series, or download our interactive e-book here.

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The information technology (IT) market is chock full of many buzzwords and terms that often get used interchangeably.  But in some cases, there are subtle differences between terms that are important to understand and that can impact the selection of tools and how they are deployed.  One example is the use of the terms business intelligence vs. business analytics or BI vs. BA.  Read on to learn how these terms and tools are differentiated and how they complement each other.

Let’s start with a history lesson.

Business Intelligence Emerges from Decision Support

Although there were some earlier usages, business intelligence (BI) as it’s understood today evolved from the decision support systems (DSS) used in the 1960s through the mid-1980s.  Then in 1989, Howard Dresner (a former Gartner analyst) proposed “business intelligence” as an umbrella term to describe “concepts and methods to improve business decision making by using fact-based support systems.”

The more modern definition provided by Wikipedia describes BI as “a set of strategies and technologies used by enterprises for the data analysis of business information.”  Another definition offered up by TechTarget states that “Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers, and workers make informed business decisions.”

The TechTarget definition goes on to describe how, as part of the BI process, organizations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data, and create data visualizations, BI dashboards, and reports to present data and make the analytics results available to business users for operational decision-making and strategic planning.

Business Analytics Takes Over as the Umbrella Term

Business analytics,” or “data analytics” is the more modern term being applied to the broader domain of BI, corporate performance management (CPM), and analytic tools and applications.  What I like about the term analytics is that it denotes a more “active” approach to consuming information.  Where BI is often viewed mainly as the process of gathering information and formatting it for delivery to end-users – analytics speaks more to the process of accessing, processing, consuming, manipulating, slicing, dicing, and drilling into the information to understand trends and get answers to analytic questions.

Below is the International Data Corporation (IDC) Taxonomy (see figure 1) for Big Data and Analytics Software, which depicts how all of these tools and applications fit together.  There are three primary segments to the market in this taxonomy:

  1. On the upper left, you’ll see performance management and analytic applications. This includes financial EPM/CPM applications, as well as other analytic applications, such as CRM, supply chain, workforce, and others used across business operations
  2. On the upper right, you’ll see business intelligence and analytics tools. This includes query, reporting, multidimensional/OLAP, and visual discovery, as well as advanced and predictive analytics.
  3. Then underlying both of these segments are the analytic data management and integration platforms. This includes data integration tools, as well as data warehousing and management technologies that can serve up data to BI and analytic tools or can be leveraged by performance management and analytic applications.
IDC Taxonomy
Figure 1 – IDC Taxonomy for Big Data and Analytics Software

Business Analytics in Action

With the IDC taxonomy identifying the various types of business analytics tools that are available in the market, let’s talk about the use cases for business analytics.  There are essentially three types of analytics that businesses use to drive their decision making:

Descriptive analytics make up the majority of today’s management reporting.  It’s the analysis of historic data using simple techniques such as data aggregation and data mining, which are used to uncover trends, signals and patterns. This information is delivered to end-users via reports and management dashboards that include visual data representations such as line charts, bar charts and pie charts that provide useful insights and provide the foundation for additional analysis of the underlying details.

Predictive analytics is a more advanced method of data analysis that applies statistical analysis techniques and machine learning to historical data to project future outcomes, and the likelihood of these outcomes.   The use cases for predictive analytics include problems such as demand or sales forecasting, fraud detection, and customer churn analysis.

While closely related to descriptive and predictive analytics, prescriptive analytics takes the process a step further by showing decision-makers which future scenario is the best path forward using a variety of statistical methods.  This is achieved through gathering data from a range of descriptive and predictive sources and applying them to the decision-making process. It enables teams to view the best course of action before making decisions, saving time and money while achieving optimal results.

Whilst each of these methods are useful when used individually, they become especially powerful when used together.

OneStream’s Approach to Predictive Analytics and Machine Learning

OneStream empowers Finance teams to lead at speed by unifying predictive analytics with core CPM processes: planning, budgeting, and forecasting; financial consolidation; reporting; and financial data quality.  And with our built-in predictive analytics solution (see figure 2), OneStream is unleashing Finance transformation to take budgeting, planning, and forecasting processes even further – allowing teams to plan, analyze and predict with confidence.

Figure 2 – OneStream’s Predictive Analytics 123

As announced at OneStream’s Splash Virtual event in 2021, OneStream’s AI Services and Sensible ML solution will provide Finance teams with the power to leverage predictive ML models without extensive work by data scientists.  This solution will take users through a step-by-step process for each part of the ML model-building and deployment process. Including feature engineering through advanced algorithm configuration, training, and deployment.

Learn more

Business Intelligence tools are part of a broader range of business analytics tools that include analytic data infrastructure, CPM and analytic applications, as well as advanced predictive analytic tools.  These business analytics tools and applications are all designed to help organizations gather, organize, and disseminate information to executives and decision-makers and provide the “analytics intelligence” required to make timely and informed decisions that can drive improved business performance.

To learn more about OneStream’s approach to predictive analytics and machine learning, download our white paper, and contact OneStream if your organization is ready to transform Finance by aligning advanced predictive analytics and machine learning with core CPM processes.

Download the White Paper

The year 2020 was one of the most challenging ever for CFOs and Finance executives.  To truly understand the impact of the pandemic on financial decision-making, in July of 2020 OneStream sponsored a Hanover Research survey of Finance decision-makers.  The survey results highlighted the impacts of the global pandemic on hiring, upskilling of IT and Accounting staff, as well as investments in cloud-based planning, reporting and analysis tools. The survey also highlighted how most organizations (61%) were deferring certain investments until after the US presidential election.

Now that the 2020 elections are behind us and the global pandemic is winding down, we thought this would be a good time to again take the pulse of Finance decision-makers. So in March of 2021 we launched another survey of Finance decision-makers in North America and gathered responses from 340 Finance executives across industries.

Here’s a summary of what we learned from the 2021 Hanover Research Finance Decision-Makers survey.

Key Findings: COVID-19 Response & Recovery

The good news is that almost three quarters (73%) of companies expect that they will return to normal growth by the end of 2021, while 18% expect a return to normal growth in 2022.

Hanover Survey

During COVID-19, approximately 11% of employees switched from entirely in office work to fully remote work during COVID 19 but expect to return to the office post pandemic. The number of hybrid employees stayed approximately the same throughout the pandemic and is not expected to change when the pandemic ends. Regarding the return to the office, nearly all companies (98%) have made budgetary plans for returning to the office, one third (36%) of which plan on dedicating over 15% of their budget to reopening the office.  Data privacy tools is the most common (18%) priority for the earmarked return to office budgets, with hybrid cloud technologies (18%) and office reconfiguration following closely (18%).

Hanover Survey

Pandemic-Related Investment Changes

Since the COVID 19 pandemic, over half of companies increased their data analysis tool investments and usage. Specifically, companies most commonly invested in artificial intelligence (59%), predictive analytics (58%), cloud-based planning and reporting tools (57%) and machine learning (54%).

Hanover Survey

And the survey also found that organizations are using data analysis tools more than before the pandemic. In August 2020, half (46%) of companies reported using cloud-based solutions constantly, while a quarter used predictive analytics (28%), machine learning (21%), and artificial intelligence (20%). Now, over half of companies have increased their usage of each tool, with cloud-based planning and reporting topping the list at 65% claiming increased usage.

Hanover Survey

Given that more than half of companies have increased their investments in machine learning, it’s unsurprising that most are planning to optimize new departments and use cases with the technology.  Specifically, companies are planning to optimize IT/cybersecurity (30%) and are prioritizing customer service (15%) and accounting & finance (12%). 

Hanover Survey

Administration-Related Investment Changes

Despite many companies deferring investments until after the election, over half of companies report that it positively impacted their investment decisions for 2021. Launching new products and services have been the most positively impacted investment areas, followed by physical expansions, including new employees, software, acquisitions, and facilities.

Most companies (86%) said they will need to change their financial forecasts in the event of a tax change by the new presidential administration Similarly, most companies (89%) have already made plans to change hiring and staffing plans to accommodate wage increases.

Hanover Survey

In addition, most companies are increasing, or are planning to increase, investments in environmental, social and governance (ESG) management and reporting systems (85%) as well as DEI training (86%).


Running a survey like this one is always interesting because it provides a chance to validate our assumptions about key market trends.  We were pleased to see the positive outlook by most Finance executives about economic recovery in 2021. It was also encouraging to see 98% of companies in North America preparing for the return to the office.

The survey also validated what we are seeing in the market, with increased demand for cloud-based planning and reporting solutions, as well as advanced analytics tools, typically replacing spreadsheets or legacy corporate performance management (CPM) applications.  And we have also seen increased usage of cloud-based planning and reporting tools – with many organizations increasing the frequency of their planning and reporting cycles during the pandemic.

One area that did surprise us was that 85% of companies indicated they plan to increase their investments in ESG management and reporting systems.  The media buzz on this topic clearly increased in the 2nd half of 2020, as has OneStream customer interest in this topic.  Several of our customers are already leveraging our platform to collect, manage and report on ESG and sustainability initiatives.

To learn more, download the 2021 Hanover Research Finance Decision-Makers survey and contact OneStream if your organization needs to improve its ability to “lead at speed” and more easily navigate ongoing market volatility.

In a recent webinar with our partners at PwC, we explored how Finance leaders are increasing the value and guidance their teams provide to their organizations while driving increased performance.  In this discussion on Office of Finance Transformation, Scott Stern, Senior Director of Product Marketing at OneStream, first examines how Finance teams can evolve from a scorekeeper to a coach role with Colby Conner, Finance Partner at PwC.  Then Scott examines some examples of customer transformation with Tana Treearphorn, Director of Advisory at PwC.

This webinar details the organizational attributes and technology required for Finance teams to successfully navigate this transformation.  What does success in Office of Finance transformation look like?  Mr. Conner suggests the following rule of thumb.  When Finance and business unit leaders spend just 2 minutes or less of strategy meetings agreeing on the accuracy of the numbers and spend the remaining 58 minutes developing insights and solving challenges, the Office of Finance Transformation can be deemed a success.

While a bit simplistic, this “2-minute test” illustrates exactly what Finance leaders of sophisticated organizations should strive to achieve.  Under this ideal, Finance transcends the role of data aggregator and summarizer to become a trusted partner of business unit leaders.  Transforming essentially elevates Finance’s role to focus on providing insights and guidance to drive performance for the entire organization.

Why Embark on the Office of Finance Transformation Journey?

Mr. Conner explains how today’s organizations have an urgent need for Finance to better support the business.  He describes how many factors – including increasing economic pressure, emerging technology, new data sources and increasing data volumes – all challenge organizational performance.  He then describes how these internal and external factors present opportunities for Finance to lead at speed to not only meet the pace of change but also conquer increasing complexity.

He also examines how many Finance organizations limit their role to being scorekeepers.  These teams spend much of their time wrangling data and reconciliations with a focus on aggregating data and producing reports.  In contrast, organizations that have embarked on an Intelligent Finance journey progress to a coach role and add value by providing knowledge, insights and operational decision guidance across their organizations.

Finance teams that complete this journey evolve to become owners of an “insight supply chain.”  These teams can then take data from inside the organization and turn it into insights to define new futures and create market leadership.

Addressing Office of Finance Transformation Challenges

So why isn’t every Finance team successfully launching this transformation?  The answer is pretty simple:  there are significant challenges to realizing an Office of Finance Transformation.  The primary challenges are outdated technology and manual processes that force many teams to spend too much time managing data and tools instead of conducting analysis and providing insights.

Mr. Conner redefines these challenges as being opportunities. He suggests Finance teams turn the status quo of manual tasks and inefficient processes into the “fuel” that powers transformation.  More specifically, he argues that implementing a modern corporate performance management (CPM) solution to automate processes will give Finance teams the extra time they need.  That time allows Finance teams to first spend time implementing transformation and ultimately find themselves with the time needed for high-value analysis and insight development.

Mr. Conner specifically identifies OneStream’s Intelligent Finance platform as a solution that empowers Finance teams in two ways.  First, it gives teams the ability to begin the Office of Finance Transformation by conquering the complexity of CPM processes.  Second, it provides teams the capability to complete that transformation with advanced analysis and reporting.  Some example opportunities to increase efficiencies in CPM processes include streamlining the financial close process or building efficiencies in reporting or budgeting & forecasting (see Figure 1).  He explains that OneStream’s powerful process automation capabilities enable Finance teams to automate processes and eliminate wasted time spent on manual efforts.

Finance Transformation
Figure 1. PwC’s Leading Finance in the 2020s: Automation Holds the Key to Improved Efficiency

Five Attributes for Finance Transformation Success

Mr. Conner then defines the five organizational attributes (see Figure 2) for Finance Transformation success and provides a detailed explanation of each.

Finance Transformation
Figure 2. PwC’s Attributes for a Successful Finance Transformation Journey

A key highlight of these attributes included a discussion of how Finance teams absolutely must build trust across the organization as a coach for the operational business units – moving the role of Finance from Scorekeeper to Value Adder and Wealth Creator.  While many factors will engender trust (see Figure 3), Mr. Conner specifies that Finance teams must maintain confidence in numbers that are shared with the organization on a timely basis.  In his words, “If the data isn’t always right, if it is always being revised or if it takes too long to put together, then it erodes trust.”

He also explains that Finance teams must understand each operational unit’s goals and have the analytic ability to provide insightful and relevant analysis.  He identified the OneStream Intelligent Finance platform as having not only the financial data quality capability to build confidence in governed financial and operational data, but also the ability to empower advanced financial and operational analytics.

Leading Finance in the 2020s
Figure 3. PwC’s Leading Finance in the 2020s:
Elevating from Scorekeeper to Wealth Creator

Intelligent Finance in Action

To close out the webinar, Tana Treearphorn shares two customer examples of Finance Transformation. In the first example, he examines how a $21B SaaS provider of cloud-based customer relationship management (CRM) services and complimentary enterprise applications (e.g., customer service, marketing automation, analytics and application development) conquered the complexity of rapid growth.  With the OneStream platform and guidance from PwC, this Finance team transformed from being a report provider who spent 80% of their time reconciling data to being the provider of insights to the entire organization.

In the second example, Mr. Treearphorn shares how PwC guided a $75B global freight and logistics provider using the OneStream platform to unify their fragmented closing and planning processes from across the globe.  In doing so, the provider powered their transformation by building efficiency in their processes and increasing the relevance of their operations insights.

Learn More

To learn more about how OneStream empowers organizations to lead at speed in Office of Finance Transformation and how PwC guides organizations on that journey, watch the webinar replay of “Intelligent Finance: Driving a New Level of Business Agility.”  And if you’re ready to conquer complexity in your own Office of Finance Transformation, contact OneStream today.

In today’s competitive global market, providing managers with accurate insights into profitability by products, standard service lines, distribution channels, customers and other dimensions of business is essential to agile and effective decision-making.  Yet many organizations struggle to create the visibility and transparency for these insights either due to the lack of time, technology, perceptions it is too complex to do, or executive support.

Addressing this challenge was the focus of a recent OneStream-sponsored webinar titled “How to Enhance Business Insights and Agility with Effective Profitability Management.”  The featured speakers were Gary Cokins, Founder and CEO, Analytics-Based Performance Management LLC and Linda Hellebuyck, Corporate Controller at Henniges Automotive.  Read on to hear the highlights of the webinar or watch the replay to see the details.

Cost Allocations and Profitability Management Best Practices

Gary Cokins is an internationally recognized expert, author and speaker on enterprise and corporate performance management (EPM/CPM) methods including measuring and managing customer profitability (using activity-based costing principles). He has over 30 years of experience in the field and has authored several books on these topics.

Mr. Cokins started his presentation off with a review of the basics of activity-based costing (ABC). His key message here is that when CFOs and Finance teams “allocate” indirect expenses (i.e., overhead) to products and standard service-lines, they spread it like “butter across bread”.  And in doing so, CFOs violate cost accounting’s universal “causality principle.”  Activity-based costing (ABC) resolves this by “tracing and assigning” expenses based on cause-and-effect relationships for how products and service lines consume work activities, which in turn consume the expenses of resources (e.g., salaries, supplies, utilities, etc.).

If we roll back the clock to the 1950’s, when direct labor and materials represented the majority of expenses in an enterprise, broadly averaged cost allocations for the indirect expenses was acceptable. But in today’s world, where indirect expenses represent most of the expenses in an enterprise, the averaged cost allocation approach can lead to large flawed and misleading cost errors.  

evolution of business

Mr. Cokins went on to highlight the value of ABC in service-based industries, such as insurance and banking.  Allocating expenses such as salaries, equipment, travel, supplies and occupancy to the various costs of work activities that occur in a department, such as claims processing, enables a clear view into which groups of customers are consuming relatively more versus less resources and their expenses.

GL to Data Base

He then reviewed the steps required to effectively implement ABC – allocating expenses from resources (e.g., GL accounts), to the costs of activities, and then to cost objects such as products, service lines, projects, and customers. While applying an ABC-based approach to cost allocations can take more time and effort than performing the traditional but simplistic cost allocations, the benefits are worth it. ABC provides CFOs and Finance teams, and more importantly line managers, with a clear view into which products or services are truly adding to bottom-line profits and which are detracting from profitability – and also a view to what are the drives causing the costs.

The Power of Customer Profitability

Mr. Cokins went on to highlight the importance of understanding distribution channel and customer profitability.  The value of a company is a function of the value it gets from its customers – therefore understanding which customers, or segments of customers, are adding value versus reducing value is critical to driving long-term stakeholder financial value including for shareholders and business owners.  Citing several examples from Jeffrey Colvin’s book “Angel Customers vs. Demon Customers,” his message is that by fully understanding customer profitability, CFO’s can help Sales and Marketing to better target customers.  This means answering questions like:


When these questions are answered, organizations can more effectively target the types of customers they want to retain, grow, and acquire; and also make the pricing or customer services changes required to convert less profitable and even unprofitable customers to be profitable customers.

In concluding his presentation, Mr. Cokins provided some guidance on how organizations can overcome the resistance they may encounter when implementing ABC, including technical, misperceptions of excess complexity of ABC, and organizational behavioral barriers. He said, “It is better to be approximately correct than precisely inaccurate”.

Product and Customer Profitability at Henniges Automotive

After a brief introduction to the capabilities OneStream’s Intelligent Finance platform provides to support customer and product profitability, Linda Hellebuyck joined the discussion to highlight the approach Henniges Automotive has taken to understand product and customer profitability.

Henniges Automotive is Leading global supplier of highly-engineered automotive sealing and anti-vibration systems with operations in 8 countries, including 19 manufacturing plants and 4 technical centers. After selecting and implementing OneStream to replace Hyperion Enterprise for financial close, consolidation and reporting – the Henniges team extended their use of the OneStream platform into several additional processes, including Product Line Reporting.


The challenge here is that Henniges produces thousands of automotive products that are very customer and vehicle specific, and as a result, profitability can vary significantly between products.  So it’s critical to understand profitability at a customer, platform (vehicle), and product level.  Using manual processes and Excel spreadsheets for this type of analysis was very painful, with 80% of the effort going into collecting the data and 20% on analyzing it.

By moving this process into OneStream, Henniges was able to harmonize, store, allocate, and aggregate the data at a detailed (part number) level enabling the Finance team to:

To accomplish this, the Henniges team leveraged many capabilities within the OneStream platform, including its extensibility.  This enabled the team to set up two cubes within a single application, one for financial reporting and another for profitability reporting.  While the two cubes share several common dimensions, the Profitability Cube has additional dimensions such as Customers, Products, Parts, and Platforms designed to support profitability reporting and analysis.

Profitability Reporting

According to Ms. Hellebuyck, “Because it’s one application, we can share both metadata and data across the two cubes, making cross-cube comparisons easy. In other multi-product solutions, marrying the consolidations data with the part-level analytical data would be significantly more complex.”

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Leveraging OneStream for financial and profitability reporting has yielded several business benefits to Henniges, and other customers.  This includes the ability to collect data faster and on a more frequent basis – moving profitability reporting from an annual to quarterly or even a monthly exercise.  The solution provides deeper insight into what pieces of the business are producing (or not producing) bottom-line profits – and to why. This insight helps managers make more informed decisions in areas such as quoting, commercial negotiations, rationalizing which customers to devote more effort on, and implementing cost improvement initiatives.

To learn more, watch the replay of the webinar or contact OneStream if your organization is ready to raise its game when it comes to understanding profitability by products, customers, channels or other dimensions of your business.

If you’re in Corporate Finance, you likely found your planning and analysis processes being stressed and challenged this past year.  Perhaps you were driven into increased rounds of forecasting due to COVID-19. Or maybe the Suez Canal being blocked by a large container ship left your team scrambling to adjust forecasts.  As a result, like many Finance teams, you’ve probably been finding out the hard way that you may not have the right tools for the job.

If you’re among those who don’t, there’s still good news: the corporate planning software solutions on the market today come in all shapes and sizes.  So, you’re sure to find a solution designed to meet the unique needs of your organization.

Some solutions are built for small companies.  Some offer more visualization capabilities.  Others are only point-solutions for planning or specific areas of planning.  What does all of that mean for you?  It means your Finance team has many choices – if you can cut through all the noise and get to the facts.

What’s the best way to understand the options?  By turning to your trusted peers in Corporate Finance, of course.  And that’s precisely what the BARC Planning Survey 21 allows you to do.

The Business Application Research Center (BARC)

The Business Application Research Center (BARC) is an industry analyst and consulting firm for business software.  BARC analysts have supported companies through strategy, organization, architecture and software evaluations for more than 20 years.  For more information, visit

To support Corporate Finance teams, BARC covers the following critical areas:

BARC Planning Survey 2021

BARC Planning Survey 21

The Planning Survey 21 examines user feedback on planning processes and product selection.  That feedback is based on findings from the world’s largest and most comprehensive survey of planning software users. Conducted from November 2020 to February 2021, The Planning Survey 21 compiles responses from 1,422 individuals analyzing 21 products or groups of products.

Specifically, the survey examines user feedback on planning product selection and usage across 29 key performance indicators (KPIs), including business benefits, project success, business value, recommendation, customer satisfaction, customer experience, planning functionality and competitiveness.

For more information on the survey, visit the BI Survey website.

OneStream Software: Dedicated to 100% Customer Success

With a corporate mission dedicated to delivering 100% customer success, we’re proud to share that OneStream earned 58 top rankings (see Figure 1) across its four peer groups.  The company was measured by several different KPIs, including business benefits, project success, business value, price to value, vendor support, implementer support, product satisfaction, data integration and customer experience.

Additionally, OneStream received a 100% recommendation score from all surveyed users – up from 97% in 2020.

OneStream Highlights Dashboard
Figure 1: The Planning Survey 21: OneStream Highlights Dashboard

Among our 58 top rankings, OneStream earned SEVEN ‘Perfect 10 Scores’ in the following KPIs:

OneStream also earned 34 leading positions across four different peer groups, including leading positions in project success, price to value, business benefits, business value, planning functionality and vendor support.

“OneStream’s performance in this year’s Planning Survey reflects the vendor’s dedication and mission to providing 100% customer success. OneStream’s unified, extensible platform and data model support a wide range of financial and operational planning use cases – and do so at scale and across the enterprise. This combination of financial control and operational relevance provides organizations with the opportunity to unify planning processes within a single platform and user experience, which is increasingly critical as Finance leaders adapt to rapid market-changes,” said Dr. Christian Fuchs, Senior Vice-President and Head of Data & Analytics Research at BARC.

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OneStream is honored to receive such high marks within BARC’s Planning Survey 21.  The report recognizes the continued strength of OneStream’s budgeting, planning and forecasting capabilities, as well as our broader capabilities in financial consolidation, reporting and analytics.  And the honor is especially positive given that the high marks on the survey come directly from our dedicated customers and users around the globe.

To learn more about OneStream results, click here to download the full BARC Planning Survey 21.

If government finance is about anything, it is about data. Often vast amounts of data. Data that is received (from source systems such as ERPs or other agencies), data that is processed (such as budget formulation, allocations, and projections), and data that goes out the door (data to other agencies and reports to the pubic).

In virtually any step of the financial data journey, we find ourselves in need of additional information about the number in front of us at a particular moment. If it is an aggregated value, what are the component parts? Where did the number come from? Was it imported from another system? Did someone enter the number? Was it calculated? Is this number tied to a specific fund, bureau, program, project, or strategic goal? Has this number changed? Who changed it? When did they change it? What was it before they changed it? Did it require approval to be changed? Who approved it, and when? What other numbers are impacted if this number changes?

This all comes down to what is possibly one of the most over-used, erroneously defined, and diversely understood terms in government finance: analysis. This is perhaps because the term is used outside of government finance in virtually every field imaginable. In fact, I recall in a music composition class in college, we analyzed Bach concertos. But, when it comes to government financial data analysis, it can be summed up as the process of uncovering the “back story” of numbers. How it got here and what it really represents. There are possibly as many ways to analyze financial data as there are to interpret the term. The following is a discussion of some of the most common methods of financial analysis in government today and some of the pros and cons of each:

1 – Call Someone

This is the most basic solution to the analysis problem. We need to know detailed information about a value so we phone/email the person we think may have the required information. This may be the correct person, or maybe not. The response may be swift, or maybe not. There is often no knowledge of the level of effort required from the responder to produce the information being requested. This method is most effective for executives or consumers of information who typically are just dealing with very high-level aggregations of data and infrequently have inquiries of this nature. The return on investment of their time to get access and training to use any other method may not be worth it to them or the agency.



2 – Use Spreadsheets


This method is widely used. This is the method used by many of the people on the receiving end of the requests in method 1. This involves IT produced data extracts which then are mapped and uploaded into legacy data structures such as Essbase or TM1. Then the add-ins are used to connect to that data. The effectiveness of this method can vary greatly depending on the structure of the source data, the structure of the intermediary data storage area, and skill and availability of the IT team involved in extracting and maintaining the data. Many agencies continue using this method simply because they have done so for a very long time.

While there certainly is a high level of familiarity in this method, getting to the needed information can be very time consuming. The needed data often resides in more than one system. There may be financial transactional data in one system, budget data in another, workflow and approval tracking in another, account reconciliations in another, and audit information in yet another. This can make the process extremely complex, or depending on the requirements, impossible.



3 – Use Business Intelligence Tools

Many agencies have various business intelligence (BI) tools such as Tableau, Qlik, or Cognos. These are used to explore data, build dashboards, track key performance indicators, and produce reports. Many of them have fairly sophisticated ETL (extract, transform, load) capability to join tables and pull data from source systems while others rely on 3rd party ETL tools. In most cases they rely on utilizing data in a data universe, warehouse, data lake, or data mart.

While BI tools require specialized training, most agencies with these tools in house have experts on staff. However, these experts tend to reside in an IT (Information technology) group or other operational teams and may not have the financial acumen needed. Rarely does any type of audit or control information get moved from source systems to a data warehouse and the BI tools lack any audit capability on their own. BI tools also lack financial intelligence, so any financial treatment of data requires extensive configuration and/or programming.



4 – Use a Financial Management Platform with Analysis Included

Financial Analysis

A newer option to address this need is utilizing an intelligent finance platform that has financial analysis capability built in such as OneStream. Instead of pulling data from a budget system, a consolidation system, an account reconciliation system, a document management system, a reporting system, and a workflow system, this is all done in a single platform. Several forward-thinking agencies are currently using this new technology or in the process of rolling it out. But the majority of agencies still have multiple siloed systems to manage these various functions as this was the only technology available until fairly recently.

These older systems were state-of-the-art when implemented 15 to 20 years ago. The newer technology manages these functions in a single platform with all the analytic capability residing in the same platform. This allows a user to drill-down and analyze a data element from anywhere in the system with full audit and data control. This could be a budget formulation data entry screen, a KPI dashboard, a CARS reconciliation, or a section of a CBJ or AFR. When a user sees a number and has a question regarding that number or visibility into who made any changes, they can get the “back story” from wherever they are in the process in real time. This is possible since all the functionality is contained in a single platform.



Hopefully this was a helpful overview of some of the most common ways to get the underlying details of your numbers. All have their place and their pros and cons. And every agency has to decide what works best to understand the “back story” of their numbers.

To learn more visit the OneStream web site.

The global pandemic of 2020 has reignited the need for agile enterprise performance management (EPM) applications and analytic tools that enable Finance teams to lead at speed.  Why?  Because these tools are essential to enabling organizations to have clear visibility and insight into key business drivers and trends for more agile reporting, analysis and planning.

This topic was the focus of a recent webinar sponsored by OneStream titled, “Navigating the New Normal with Agile Performance Management and Analytics.”  The featured speaker was Chandana Gopal, Research Director focused on Analytics and Information Management at International Data Corporation (IDC).  During the webinar, Chandana shared IDC’s market research on key market drivers, the challenges and benefits of implementing EPM and analytic software and lessons learned from successful implementations.

The webinar also included an interview with Alex Lee, Sr. Director of FP&A at Fibrogen where she shared how the company has deployed OneStream to support more agile planning and reporting. Read on to hear the highlights of the event, or watch the full webinar replay to hear the details.

Navigating the New Normal

Ms. Gopal led off the event with a view into what happened to us all in 2020.  “In essence, 2 years of change happened in a very short time, where digital transformation was accelerated in many organizations due to the disruption caused by the pandemic.  Remote work was supported quickly at scale and organizations adapted quickly to new business models.”

Ms. Gopal also highlighted that while many organizations demonstrated “business resiliency” in responding quickly to the disruption, the focus now should be on creating “digital resiliency” which will enable organizations to quickly adapt to future disruptions, and to capitalize on the new conditions.  She then highlighted several examples of industries that had to adapt quickly – including healthcare (telehealth visits), and the entertainment industry (new distribution models).

Digital Resiliency

According to IDC’s research, top areas of investment in the past 12 months have included process automation, security, digital/cloud infrastructure, collaboration and connectivity tools.   As part of this, investments in enterprise performance management (EPM) technology also accelerated during the pandemic and are proving critical in helping organizations move from crisis to recovery (see figure 1 below).

This includes ensuring business continuity in the early stages of the pandemic, helping to control costs, performing scenario modeling and contingency planning, then evaluating targeted investments as the recovery begins, and finally strategic planning as the global economy returns to normal.

Performance Management Recovery
Figure 1 – Performance Management is Crucial in Helping Enterprises Move from Crisis to Recovery

According to IDC’s research, organizations who are “data leaders” were more prepared to navigate the disruption caused by the pandemic.  Key benefits cited by users of EPM solutions include better management reporting, improved visibility into financial processes, more accurate forecasts, better efficiencies in EPM processes and others (see figure 2 below).

Figure 2 – Key Benefits of Investments in EPM Software

At the same time, buyers and users of EPM solutions highlighted some of the challenges they have faced in deploying these systems. These include reliance on IT for supporting some EPM software, inflexibility with legacy applications and high costs of ownership, lack of adequate training and low user adoption as a result.  In IDC’s research found that since the finance function funds 80% of EPM investments, they want to have administrative powers of the EPM software and not be dependent on IT to manage these systems.

So what lessons have buyers learned from implementing EPM solutions? Respondents to IDC’s survey recommended the following:

Ms. Gopal’s final recommendations included that enterprises considering EPM solutions should think big but start small.  “Don’t try to boil the ocean, focus on a project that can deliver rapid ROI and value.  Commit the right internal and external resources to the project.  And plan for the future – ensure the solution you are selecting can meet your needs now as well as 3 – 5 years into the future.”

Improving Agility in Reporting and Planning at Fibrogen

After a brief overview of OneStream’s Intelligent Finance platform and how we help organizations conquer complexity and lead at speed, I welcomed Alex Lee into the conversation to talk about how Fibrogen has leveraged the platform.  Fibrogen is a leading science-based biopharmaceutical company discovering and developing a pipeline of first-in-class therapeutics.

Fibrogen Logo

As a result of the transition from a drug development company to a global multi-channel commercial business, the Fibrogen team required better visibility into data and better tools for scientists and line of business users.  This includes the following:

Fibrogen selected and implemented OneStream to replace Excel and their legacy budgeting tools and align key finance processes including:  Financial Close and Consolidation, Planning and Forecasting, Financial Reporting and Tax Provisioning.

2020 Budget & 2021 Forecast

In an initial 4-month project, Fibrogen implemented OneStream for revenue planning by channel, operating expenses at the activity level, CapEx planning, people planning, FTE project allocation, and travel planning.  According to Ms. Lee,” The OneStream project exceeded every expectation. It’s a dream come true!”  As a result, Fibrogen has gained agility with an integrated plan that aligns drug development to finance performance and cash requirements while enabling leadership with a unified view of the company on a real time basis.

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While a return to normalcy now appears to be on the horizon, as vaccines roll out and the pandemic winds down, there will surely be other economic disruptions in the future.  To survive and thrive through economic volatility organizations need to have agile processes and systems that enable them to quickly adapt, while minimizing the impact.

Today’s modern performance management and analytic technologies are proving invaluable to navigating the new normal with the required agility.  To learn more, watch the replay of the webinar and contact OneStream if your organization needs help conquering complexity so you can lead at speed!

The COVID-19 pandemic has created ongoing challenges for CFOs and reshaped Finance teams. Finance teams are now assessing revenue, costs and cash flow on a weekly, daily and even near-real time basis to help guide current and future decisions. But financial data is only one piece of the puzzle. Why? Effective Finance teams know the numbers on the P&L, balance sheet and cash flow statements are driven by dozens, even hundreds, of decisions made across the Sales, Marketing, Supply Chain and Operations teams.


No matter where your team is in the Finance transformation journey, it’s always a good time to take more advantage of advanced analytics. Why? Well, like it or not, advanced analytics – such as predictive analytics and machine learning (ML) – are here to stay. How am I so confident? It’s pretty simple, actually. How many Finance leaders wouldn’t want to infuse statistically significant forecasts into the planning process to help reduce the fog of uncertainty? None!

In fact, with the explosion of data and compute power available today, the barriers of entry for using advanced analytics are lower than ever. So what’s holding Finance teams back? Here’s my take – Finance teams aren’t sure how to leverage advanced analytics for everyday processes without disrupting people and processes.