What Is Operational Analytics?

Companies today are increasingly collecting large volumes of data from cloud services, mobile applications, and connected devices. Yet many struggle with deploying data. This prevents them from enjoying the full benefits of their digital investments.  

According to one recent study, just 39.3 percent of companies manage data as an asset. What’s more, only 24 percent have created a data-driven environment. 

Succeeding in the digital economy requires looking beyond collecting data and using operational analytics to produce actionable insights and to guide daily decisions.  

Let’s take a closer look at what operational analytics is all about, why it's important, and how you can use it to create a data-centric organization.  

What is operational analytics?

Operational analytics is really about putting data to work by making it directly accessible in the tools that users regularly log into. Instead of having to open a BI tool or spreadsheet, a sales rep could find key prospect data directly in Salesforce, while a marketer could tap into critical customer data right in HubSpot.

By deploying operational analytics, companies typically have a much easier time making informed decisions throughout the day. This is especially true when it comes to identifying emerging opportunities for business growth. 

Operational analytics vs. business analytics 

If you’re new to operational analytics, you may be wondering how it differs from business analytics.  

Business analytics often involves analyzing historical data and using information to create future projections about company and market trends. Business analytics helps with tasks like planning new products, bringing new products to market, expanding into new geographical areas, and measuring growth over time.  

Operational analytics is all about making data available and using insights for driving profits and forming operational strategies. When it comes to operational analytics, the goal is usually boosting productivity and keeping workflows moving.  

At the end of the day, organizational analytics and business analytics work best together. The key is merging them into one overarching strategy and using them to enhance output and guide technological development.  

Common use cases for operational analytics

Operational analytics supports many critical business functions. With this in mind, here are some ways that different departments in your organization may benefit from using operational analytics: 


For IT, operational analytics involves collecting and viewing real-time performance metrics across servers, networking components, cloud systems, and applications. Techs then use this information to maintain uptime and reduce operational costs.  

Logistics and supply chain management 

Supply chains are complex and fragile. Issues like product scarcity and warehouse staffing shortages and delivery disruptions like traffic and weather events wreak havoc on supply chains. This can lead to back orders and unhappy customers and partners.  

Operational analytics systems improve supply chain logistics, providing deeper visibility and facilitating faster product movement. 


Operational analytics helps sales teams gauge consumer interest, enabling timely offers based on current market trends. For example, a sales team might analyze regional transactions and compile daily reports based on various products, store locations, and economic patterns. 


In marketing, operational analytics systems track performance and efficiency for various campaigns. For example, teams may track daily key performance indicators like click through rates, conversion, and revenue per client.  

These metrics help marketing teams see which strategies are working. With that information, they can figure out the best way forward. 


Manufacturing teams often use operational analytics for monitoring machines, vehicles, and production lines. They provide critical safety and quality metrics, resulting in healthier and more efficient working environments while reducing accidents and work stoppages. 

Top 4 benefits of operational analytics

Deploying operational analytics can ultimately create a significant competitive edge, leading to the following transformative benefits. 

1. Faster decision-making

Having easy access to data in the tools you regularly use ultimately helps businesses move faster and more intelligently, providing hard metrics to support difficult decisions.  

2. Improved customer experience

Enabling great customer experiences requires capturing data and using it to understand specific needs.  

Operational analytics systems help companies act with greater responsiveness, precision, and empathy when dealing with customers. This results in heightened experiences, stronger loyalty, and better reviews. 

3. Enhanced employee experience 

Talented workers don’t want to spend their time on monotonous tasks like data entry and they don’t want to have to log into three different platforms to manage their days. Companies that cling to outdated business processes risk losing talented workers to digitally savvy competitors.  

Leading companies streamline employees work by using operational analytics in conjunction with workflow automation strategies so it’s easier and faster to find the info you need when you need it. And less busywork makes it easier to attract and retain top talent.  

4. Higher profits

Companies typically have a very short window of time to act on sales opportunities. Imagine having a customer on the phone who wants to buy a new product or service. This type of interaction is precious and requires rapid attention. It also requires quick access to data.  

Having data at your fingertips lets you capitalize on opportunities when they arise. With the right data on hand, you can deliver targeted offers that resonate with customers. 

Get more out of your data with Panoply

Before you can pipe analyzed data into tools like HubSpot, Salesforce, and Zendesk, you first need to be able to access the raw data for analysis—and that's where Panoply comes in. 

Panoply offers a robust cloud data platform that combines no-code ETL and data warehousing functionality. With Panoply, you can access analysis-ready data in just a few clicks. And best of all, you don’t need to be a database administrator or data scientist to use it.  

Panoply also connects with a wide variety of data sources, including Salesforce, HubSpot, PostgreSQL, MongoDB, Google Analytics, and Amazon S3. As such, you don’t have to worry about migrating workloads or changing your data management approach. To learn more, get a personalized demo today!

Working with your data shouldn't be a headache—learn how Panoply can help!
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