Operational Data vs. Analytical Data

To formulate the correct business decisions, business requires high-quality data that is integrated in a variety of formats from different sources. Two of the main categories of data are analytical and operational. In this post we’ll explore what each of these types of data are, how they are used, and some of the tools and methods to get the most out of each. 

Operational data

First let’s look at a short definition of each type of data. Operational workloads support real time processes, incorporating transactional information such as inventory control, order processing, and financial transactions. This data keeps the current state and serves the applications that run the business. It’s constantly changing, and is critical for immediate decision-making and task execution. Operational data can be structured or unstructured, and is usually stored in transactional databases or Enterprise Resource Planning (ERP) systems. These data services also collect data from mobile devices, machine sensors (IoT) and many other systems. 

To support operational and transactional workloads, Online Transaction Processing (OLTP) databases that are optimized for high-speed data operations, such as MySQL, SQL Server and Oracle Database are often used. Another solution is an Operational Data Hub, as described below. 

Operational data provides a live feed of the state of the business, flagging anomalies and optimizing responses in real time. 

Here’s a few common use cases for operational data: 

• Fraud detection and risk management: Financial institutions can quickly detect and prevent fraudulent activity and prevent loss 
• Streamline operations: Identify bottlenecks and inefficiencies in production lines and supply chains 
• Enhance customer service: Track resolution times and personalize interactions for faster, more satisfying experiences 
• Predictive maintenance: Analyze sensor data to anticipate equipment failures and take effective measures, preventing costly downtime

To learn more, download the handbook: Understanding Your Data Stack: Operational vs. Analytical Workloads, click here.

Analytical data

In contrast, Analytical Data refers to mining and processing of historical data to reveal patterns, trends, and insights that aid strategic decision-making. By understanding past performance and identifying market trends, businesses use insights from analytics to formulate long-term strategies. Using reporting and business intelligence (BI) tools they can gain an understanding of past and present trends, identify patterns, and predict future outcomes. This data usually resides in large data repositories and necessitates sophisticated tools for cleaning, processing, and transforming it into actionable insights. In addition, analytical data is used to train machine learning (ML) models. 

Analytical data is usually stored in data lakes and data warehouses that are designed to contain huge volumes of data. BI solutions conduct analysis and reporting, and offer dashboards that display data in various formats. 

Analytical data observes broad trends over time, revealing patterns and long-term growth trajectories. 

These are examples of common use cases for analytical data: 

• Optimize pricing strategies: Analyze competitor pricing and customer price sensitivity to maximize profitability
• Predictive Analytics: Forecast future trends based on historical data, such as predicting customer churn or stock prices
• Predict customer behavior: Identify trends and anticipate future needs to influence product development and marketing campaigns

Operational data vs. analytical data – not always mutually exclusive

Although analytical data and operational data differ in many ways, they can complement each other and the lines are beginning to blur in some areas. Traditionally, analytical data analyzes historical sales patterns, forecasts future demand, and optimizes pricing strategies. These insights can be used to optimize inventory, customer care and other operational systems. Real-time data analytics speeds this process; not relying on batch processing but instead instantaneously gathering, processing, and interpreting data as it is generated, enabling organizations to react quickly to emerging trends, enhance operational efficiency, and address critical issues. Real-time data analytics tools are vital for financial trading, supply chain management and healthcare monitoring, where split second decisioning may be required. A retailer would use operational data from its point-of-sale systems to track sales as they occur, along with real-time inventory levels, shipping information and customer interactions. Using this approach, businesses can gain a competitive edge, capitalizing on the most current and relevant information available. 

Fraud detection is an example where both analytical and operational data can complement each other. Insights from analytical data identify unusual patterns in financial transactions to detect fraudulent activities. Organizations can combine real time operational data and historical insights, to quickly identify fraudulent transactions and take the necessary steps to quell these activities. In manufacturing, operational data such as real-time machine sensor data, when matched with analytical data such as historical maintenance records can be used to predict and prevent potential equipment failures.

How an Operational Data Hub powers real time workloads 

An Operational Data Hub is specifically designed to support real-time operational and transactional workloads. Smart DIH, an out-of-the-box operational data hub utilizes innovative data technologies including event-based architecture, low code data API service delivery, real-time streaming and processing as well as change data capture (CDC). It supports real-time operational and transactional workloads by aggregating multiple back-end systems into a low-latency, scalable, high performance data layer. Since Smart DIH is built around a distributed architecture, it integrates data from multiple SoRs to optimize operational workloads and also provides enriched data through mirroring for analytical workloads. 

Analytical data and operational data – looking forward

Both AI and ML have much to offer for analytical and operational data workloads. AI and ML can enhance operational data analysis by vastly increasing the speed of the data analysis as well as improving the quality of the data synthesis. Gen AI algorithms​​ help detect anomalies in patterns from customer preferences to fraudulent transactions — quickly and at scale, with fewer false positives. As with enterprise cognitive computing, operational data analysis uses AI and ML to enhance human decision-makers and analysts instead of replacing them. 

Natural language processing (NLP) enables easier and faster interactions between customers and businesses in chatbots; real time operational data boosts the effectiveness of these interactions as bots can access the most up to date customer profiles and shipping, inventory and pricing information. Operational data can be used to generate more efficient inventory schemes, stronger contract management practices, and it allows businesses to be nimbler. Businesses are no longer held captive to traditional seasonal patterns and intuition. Instead, they can use their AI and ML applications to optimize their services. Augmented Analytics, is a new approach in data analytics that uses NLP, Machine Learning, and Artificial Intelligence to automate and enhance data analytics, data sharing, business intelligence, and insight discovery. AI will create data visualizations that enable human users to easily find data relationships by closely observing these visualizations.

Last Words

While analytical data reveals patterns, trends, and insights that aid in strategic decision-making, operational data includes real-time, transactional information that is critical for immediate decision-making and execution of tasks. An operational data hub such as Smart DIH enables organizations to support real-time operational workloads by aggregating multiple back-end systems into a low-latency, scalable, high performance data layer, designed specifically for the needs of real-time digital services, and also provides enriched data for analytical workloads through mirroring.