Your Comprehensive Guide to Operational Analytics in 2025

Operational analytics is the key to transforming data into actionable insights that optimize your business processes. It does this by combining the power of real-time data processing with advanced analytics capability, allowing you to access key insights and make the most of them immediately.

In this article, we’ll look at what operational analytics is and how it’s commonly used in Finance. We’ll also explore some of the features you’ll see in the best operational analytics solutions, which should give you a head start if you’re thinking about choosing one for your company.

What Is Operational Analytics in Finance?

Before we begin, it’s important to establish a clear operational analytics definition. In essence, operational analytics is the use of real-time and historical data analysis to optimize day-to-day operations as well as support financial decision-making and planning.

Integrating data from a variety of sources is a core element of operational analytics, meaning it can be a complex challenge to get right, particularly at the start. However, because it represents a useful step up from purely traditional analytics, many companies prefer it.

Operational Analytics vs. Traditional Analytics

Traditional analytics involves collecting and transforming data to gain a clear understanding of your company’s performance. Many organizations share the resulting insights with key staff using reporting and analytics software at regular intervals, but there may not be any mechanism to feed the insights back into everyday business operations directly.

A crucial point to note is that operational analytics and traditional analytics go hand in hand. On its own, traditional analytics focuses on deriving insights from specified key performance indicators. At base, it’s about informing strategy.

If you want to take that strategy and actually turn it into useful action on the ground, you need operational analytics. That’s because operational analytics enables you to take steps like feeding real-time sales data into your CRM to predict customer demand, for example.

The Challenge of Operational Analytics

When you’re trying to process operational data, the main difficulty is finding ways to get different platforms to communicate well so that data sharing between them goes smoothly and reliably.

Syncing data can pose problems simply because not all data takes the same form. You may be dealing with a broad variety of data formats, schemas, or access protocols.

However, although this is a tricky part of dealing with data, it isn’t the principal challenge of operations analytics itself. Your data warehouse software most likely already mitigates the issue of transforming data into a usable format to an extent.

Instead, the main challenge of operational analytics lies in developing methods to make good use of your data and insights you derive from it. And then there’s the issue of integrating data with systems that are used daily, a process that can be fairly time-consuming.

Despite this, many organizations have been adopting operational analytics as a core element of their operational strategy. Why?

The Rise of Operational Analytics: Why It Is Becoming So Popular

The overarching reason operational analytics is so popular is that it takes advantage of the data setup you already have and elevates it to make it more effective in practice.

Within that context, here are some of the specific factors that draw organizations toward the operational analytics approach:

Helps break down silos by taking data from the data warehouse and syncing it directly with operational platforms.
Improves operational efficiency and the ability to recognize bottlenecks in processes so they can be addressed quickly.
Supports data-driven innovation thanks to the fact that operational analytics can uncover patterns and insights that may not be possible to see using more traditional methods.
Upgrades the customer experience through the ability to track customer behavior and respond appropriately in real time.

Operational Analytics Benefits

Directly applying analytics in operations allows your organization to take advantage of several crucial benefits. In the main, these are all about improving daily performance using data. They include:

Improved Forecast Accuracy

As operational analytics enables you to analyze huge datasets and identify complex relationships, it supports accurate predictive analytics. As a result, you’ll see a considerable improvement in forecast accuracy for everyday operational metrics, such as customer demand and inventory volume fluctuations.

A key reason for this is that using this technique leads to a reduction in forecasting errors. That’s because modern operational analytics software is usually AI-driven, meaning it deploys iterative machine learning algorithms to acknowledge previous mistakes and avoid them in the future.

Proactive Decision-Making

Adopting operational data analytics also empowers your teams to make better time-critical decisions. Since the system collects data in real time from a variety of sources, it gives your employees the chance to react fast and address issues before they escalate into serious problems.

In addition, the insights you derive using operational analytics help you allocate resources more efficiently. This generates a kind of virtuous circle of better decision-making in advance, resulting in an upsurge in your ability to optimize productivity and eliminate waste.

Better Cross-Functional Alignment

The crucial point to note about operational analytics is that it plugs all of your employees into the same operational matrix. In other words, it provides them with a deeper understanding of how your systems operate, no matter which department they work in.

This means everyone can coordinate their actions more effectively across the board. In turn, this leads to much smoother collaboration between teams, all of which become consistently aligned with one another in principle and practice.

How Does Operational Analytics Work?

Operational analytics begins with data. The first step is to gather historical and real-time data from financial systems such as accounting and budgeting platforms, as well as from several other core business areas like your CRM, sales, or supply chain records. Then you integrate them all together to create a cohesive, central repository of information.

Once you have the data all in one place, you analyze it using a variety of techniques, such as dashboards, reports, and predictive modeling software. Analysts can study the data in granular detail to help them uncover the drivers behind the numbers, alongside a broad array of other useful insights.

For instance, they might run scenario simulations to help with planning or feed the data into a financial and operational reporting platform to build forecasts used to support strategic decision-making.

Who Uses Operational Analytics in Finance?

Many different types of operational analytics applications are useful in Finance. Just some of the Finance professionals who can benefit from using this approach are:

Financial Analysts: Creating financial models, analyzing past performance, and forecasting future trends constitute the bread and butter of financial analysis.
FP&A Teams: Operational analytics allows your FP&A team to transform your organization’s strategic plans into actionable financial plans and budgets.
Treasury Teams: To ensure a company has enough liquidity to meet its obligations and take advantage of opportunities, treasury teams can monitor cash flow in real time.
Controllers and Accountants: Studying operational data may alert accountants to potential weaknesses in internal controls, meaning they can address the issue before it causes financial problems.
Finance Leaders and CFOs: Leadership needs a good understanding of operational data so they can take data-driven decisions that secure a successful future for the business.
Business Unit Managers: Managers can analyze operational data to make better decisions about resource allocation with a view to improving efficiency and driving revenue growth within their unit.

Operational Analytics Use Cases in Finance

It’s worth spending a little time exploring the possible use cases of operational analytics in Finance in further detail. That’s because it allows you to get a firmer grasp of how it can be applied to everyday business tasks. Here are a few examples:

Revenue Forecasting

Revenue forecasting relies on bringing together a complex patchwork of information sources. The more information you have at your fingertips, the more accurate your forecasts will be.

This means marrying a blend of historical data analysis, trend identification, correlation analysis, scenario planning, and predictive modeling. Operational analytics unlocks the door to all of these approaches and provides a framework for combining them.

Workforce Cost Management

The largest cost most businesses have is the cost of labor. It’s therefore imperative to manage your workforce costs well since inefficiencies in this area can undermine your entire budget.

With operational analytics, you can analyze employee work patterns and create adaptable schedules that optimize productivity. You can also carry out compensation analysis to ensure your policies deliver fair pay to employees while keeping overall costs affordable.

Expense Analysis

You can use operational analytics to track and categorize all expenses and hopefully identify areas where costs can be reduced. You can also look at individual cost drivers to identify any practical actions you can take to optimize expenses.

Another useful approach is to compare planned expenses with actual ones. This can be quite an eye-opening exercise, particularly if you find large discrepancies that must be investigated further. Again, this is much easier to do with an effective operational analytics process in place.

Profitability by Product or Channel

Understanding which areas of your business are more profitable is essential to fine-tuning your overall strategy. It allows you to allocate more resources where they’re required and identify those channels that are underperforming.

For example, a retail bank could use operational analytics to determine the profitability of different types of loans according to how they were sold (e.g., online vs. in-branch). It could then use this information to make decisions about which products to advertise more aggressively.

Scenario Modeling

To plan for the future, many businesses use scenario modeling to stress-test their operations. This involves testing different scenarios based on assumptions about future market conditions to assess the potential impact on key business metrics like revenue and profitability.

Applying operational analytics enriches the modeling process as it simplifies the task of integrating current market conditions into the model. For instance, suppose an investment bank wants to investigate the potential impact of an interest rate hike on its bond portfolio.

As it can leverage operational analytics to integrate real-time bond yield fluctuations into the model, it can produce a much more dynamic and accurate scenario.

Budget vs. Actual Analysis

The real-time capability unleashed by operational analytics also contributes to much more efficient budget analysis. Instead of waiting until the end of each reporting period to consolidate the figures, you can do it at any time of your choosing.

At any point, you can use financial reporting and analytics software and other operational analytics solutions to track actual revenue and expenses against the budget and hone in on the reasons for any differences. This means you can pivot fast if necessary, rather than waiting until the end of the month.

Capital Expenditure Planning

Another area where analyzing the numbers in detail is crucial is when making decisions about future capital expenditure. These amount to significant investments, as a rule, so it’s crucial to get them right.

Operational analytics allows you to monitor actual vs. planned spending, timelines, and resource utilization for projects in progress as well as completed ones. This helps identify potential areas for improvement in future planning.

Supply Chain Cost Analysis

A comprehensive supply chain cost analysis encompasses several factors. These should include a breakdown of cost drivers, supplier performance, logistics, and inventory levels.

With the kind of real-time analysis supported by operational analytics, your business can investigate various components such as the cost of transportation, warehousing, or procurement. This allows you to identify areas where it may be possible to optimize your processes and cut costs.

Customer Profitability Analysis

In the end, it all comes down to profitability. Operational analytics allows you to study your customer data in detail on an ongoing basis, feeding the insights you’ve gleaned into the process of improving the customer experience in real time.

You can identify high-value customer segments based on purchasing behavior while also investigating factors most likely to drive a client to churn. This means you can maximize customer profitability by using tailored marketing and targeted retention strategies.

Operational Analytics Example in Finance

Let’s take a closer look now at how a company might use operational analytics to improve a common business process. For this example, we’ve chosen cash flow optimization as it’s something all businesses have to deal with regularly.

Cash Flow Optimization

The main strength of operational analytics is that it helps companies build highly dynamic procedures that pack a punch in terms of operational efficiency.

Consider the situation of a financial institution looking to review its cash flow position. Traditionally, this would have been done periodically, using regularly updated revenue and expense data to create short-term and long-term forecasts.

Operational analytics allows the company to leverage data to monitor payment trends on an ongoing basis. This means they can more accurately predict the volume of late payments and deploy proactive measures to improve collection rates.

The company can also track transactions in real time, using anomaly-detection software to identify potentially fraudulent activity before it becomes a problem.

Following these steps lets the institution make the most of the available data to minimize cash flow gaps and make its processes more efficient.

How Can Your Team Implement Operational Analytics?

To implement operational analytics well, your team needs to follow a number of steps, all of which are aimed at making the most of your business-critical data and maximizing efficiency in your processes.

1. Acquire the Necessary Solutions

First, you should ensure you have the right software in place before you begin. This includes:

ETL Software: This is for the data-collection stage. ETL platforms extract, transform, and load data of different types, such as customer data from your CRM or budget data from your financial records.
Business Intelligence Software: You’ll need BI software to help you visualize the data and create actionable reports.
Somewhere to Store the Data: Data warehouses or data lakes act as your central repository of truth.
Data Modeling Software: Finally, you’ll require specialist software to work with the data and derive those valuable insights that help support crucial decision-making.

2. Leverage In-Memory Technologies

The next point is a technical one. If you store data in traditional databases and then load it into your analytics platform to work with it, you’ll encounter a latency problem.

It’s much more efficient to run your algorithms directly on in-memory technologies. In other words, you work with the data where it is instead of moving it somewhere else first. Use in-memory solutions to optimize your analytics process.

3. Implement Decision Services

Decision services automate the decision-making process using a mixture of predictive analytics, optimization technologies, and business rules. The idea is to develop a procedure that’s separate from your business processes so that any of them can use it.

Note that for this to be effective, however, the decision services have to be able to communicate seamlessly with all your other platforms, such as your data warehouse and BI software.

4. Foster Unified Data Definitions Across Teams

One problem that can crop up, particularly in larger enterprises, is that not all teams understand data in the same way, which can cause hiccups when implementing analytics.

To guard against this possibility, ensure all your teams are fully aligned in their grasp of the data and related terminology. Make sure every team runs modeling, testing, or reporting procedures by following exactly the same set of steps.

What Features Do the Top Operational Analytics Tools Include?

There are plenty of operational analytics platforms available, but they’re not all created equal. If you’re considering adopting a new solution, there are many elements you should look out for, including:

Data Integration Options: The solution should connect to multiple data sources to give you a comprehensive view of your operations. For example, OneStream provides financial data quality software that allows direct integration to any open GL/ERP system, including Oracle, SAP, and Microsoft Dynamics.
Real-Time Data Processing: Any decent operational analytics service will enable your team to make immediate, on-the-spot analysis of incoming data.
AI and Machine Learning: AI-driven platforms are more efficient because they can easily identify patterns in large datasets, which facilitates more accurate analysis.
Advanced Visualization Functionality: You should be able to create customized charts, graphs, and dashboards to help make complex data easier to understand. You can use the financial signaling solution from OneStream to build self-service visualizations with complete audit trails down to transaction-level details on all your data sources.

Tap Into Operational Analytics and Elevate Your Finance Performance

OneStream can help your business leverage the power of operational analytics by unifying your financial and operational data within one single, highly secure platform.

This enables your teams to drill down into the details of every transaction and create customized interactive dashboards to help produce top-quality analysis.

What’s more, you can run predictive models without relying on separate BI software, meaning you’ll be able to take data-driven decisions and forge a more effective strategy for long-term success.

If you’d like to learn more about the OneStream platform, why not reach out today to arrange a demo and see for yourself what it can do for your business?

Did you know OneStream also offers a weekly webinar every Friday for 1 hour on a specific topic? Check out our resources library.

FAQs About Operational Analytics

How Are Models Developed in Operational Analytics?

Business analysts use techniques such as data mining, machine learning, and AI-driven automation to build models for operational analytics purposes. Specialized professionals like data scientists may also use quantitative approaches, such as cluster analysis, cohort analysis, and regression analysis.

Which Industries Benefit From Operational Analytics?

Pretty much any industry where data plays a key role in operations can benefit. It’s often used to optimize processes and underpin decision-making in spheres as diverse as healthcare, manufacturing, Finance, telecommunications, and retail.

How Do You Create an Operational Analytics Strategy?

The main thing is to take a systematic approach. Start by assessing your current capabilities and defining clear goals. Then make sure you have the right software in place so that it’s straightforward to integrate all your data.

Finally, foster a data-driven culture in your organization, ensuring that all your teams are fully aligned in terms of the processes they use and their understanding of the role of data in your operations.

The 2025 Comprehensive Guide to Operational Analytics in Finance

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