Why Gartner says operational intelligence is no longer optional

Businesses are under pressure to make decisions at lightning speed using the freshest data available. This need has given rise to operational intelligence (OI), which blends real-time analytics into operational processes. Gartner has identified operational intelligence as a high-impact capability that is rapidly entering the mainstream. In other words, organizations can no longer treat operational intelligence as a luxury; it has become an essential strategy for staying competitive.

What is operational intelligence?

Operational intelligence refers to performing analytical processing within transaction-like workloads. Instead of analyzing data hours or days after events occur, OI lets systems analyze and act on data in the moment as part of business transactions. “Operational intelligence provides analytical processing within transaction-like workloads,” Gartner analyst Aaron Rosenbaum wrote in the report, Gartner Hype Cycle for Data Management. “It enables transactional business processes to be steered in real time, assisted by analytics, AI, and ML.” In essence, analytics and decision logic are embedded into operational systems, reducing the delays between data generation and insight.

This approach contrasts with traditional business intelligence, which historically looked at lagging reports for strategic decisions. OI focuses on current, live data for immediate actions, such as flagging a fraudulent transaction during a purchase rather than reporting on fraud weeks later. By unifying transactional and analytical capabilities, operational intelligence helps each business event take advantage of up-to-the-second actionable insights.

Why operational intelligence is essential in today’s business

By including intelligence into live processes, companies respond dynamically to opportunities and threats. OI “enables business users to make more informed operational and tactical decisions in real time,” Gartner said. Businesses deploying OI react to events immediately, such as catching fraud as it happens, adjusting prices on the fly, rerouting supply chain flows, or personalizing customer experiences in real time. This level of agility is critical for survival.

Industry experts go so far as to say that for enterprises aiming to grow, adopting operational intelligence is no longer optional, but a necessity. It provides real-time actionable insights for faster data-driven decisions and swift action when conditions change. In competitive markets that require agility, organizations that lag in real-time intelligence risk falling behind their competitors. Below are some of the benefits that make OI indispensable today:

Faster, smarter decision-making in real time

Operational intelligence supports immediate, data-driven decisions guided by artificial intelligence and analytics. Front-line staff and automated systems alike act on insights in real time rather than waiting for end-of-day reports. OI “opens up the possibility of driving prescriptive decisions through automated decision making aided by analytics,” Gartner said, moving beyond decisions based on intuition alone. With continuously updated data, companies make more accurate choices on the fly, improving outcomes from customer service to operational efficiency.

Embedded analytics with no delays

By embedding analytic processing into business transactions, OI eliminates delays caused by shuttling data to separate warehouses or business intelligence tools. Insights are generated in-stream, without the need for lengthy extraction, transformation, and loading processes. As Gartner highlights, analytics in OI are “embedded within the business process, eliminating delays from separate extraction, transformation, and loading activities.” This means decisions are based on the freshest data, and organizations seize opportunities or tackle problems the moment they arise. The result is responsiveness that traditional batch analytics cannot match.

Proactive problem-solving and risk reduction

OI helps organizations identify emerging issues and outliers in real time to respond. Patterns that indicate fraud, security threats, or operational bottlenecks trigger instant alerts or an automation for countermeasures. In financial services, for example, firms use OI to spot anomalies such as fraudulent transactions and stop them as they occur, rather than after the fact. This proactive posture reduces damage and loss. Across industries, operational intelligence provides early warning signals from predicted equipment failures in a factor to customer churn risks flagged in a telecom network, so teams address problems before they escalate. Gartner notes that organizations can “stave off risks proactively” by taking advantage of OI’s continuous monitoring and analysis.

Constant improvement and cost savings

Because OI integrates analytics into day-to-day operations, it continuously finds ways to improve efficiency and resource use. Businesses gain constantly updated forecasts and simulations of future outcomes to guide planning. They experiment with real-time “what-if” analysis to see the impact of a change in pricing, inventory, or strategy. Over time, this leads to a smarter allocation of budget and effort. Moreover, consolidating transactional and analytical workloads reduces infrastructure complexity and cost. Running a unified real-time data platform means less duplication of data and fewer siloed systems to maintain. Gartner research notes that operational intelligence “can reduce governance efforts, time and infrastructure costs” compared with maintaining separate analytical systems. In short, OI not only drives revenue growth through better informed decisions but also trims waste and IT overhead.

Real-world examples  for operational intelligence

Operational intelligence is being applied across industries to solve a wide range of real-time business challenges. Some of the most effective examples include:

Real-time fraud detection and risk prevention

In sectors such as banking, payments, and e-commerce, OI is improving risk management. Financial institutions use operational intelligence to monitor transactions as they happen and catch fraud or errors in real time. For example, banks detect and block suspicious account activity in milliseconds rather than discover fraud after the damage is done. Similarly, credit card processors use OI algorithms to identify anomalous purchase patterns and stop fraudulent charges in real time. This not only prevents losses but also improves customer trust. Beyond fraud, OI helps with credit risk scoring, insurance underwriting, and other scenarios where decisions must balance speed with accuracy. By analyzing live data against predictive models, companies make split-second risk decisions confidently. 

Dynamic pricing and customer engagement

Operational intelligence helps retailers, travel companies, and online services adjust to customer demand and behavior on the fly. One common application is dynamic pricing, which means using real-time inputs to update prices or offers. For instance, a hospitality provider might raise or lower room rates based on current demand, inventory, or even competitor pricing, improving revenue and occupancy. Gartner notes that OI use cases include “dynamic repricing” for retail and e-commerce scenarios. 

In practice, an e-commerce platform might use OI to track website traffic or inventory levels and run promotions or price changes during peak periods. In addition, companies use OI to personalize the customer experience in real time. Streaming analytics on user behavior produces tailored recommendations, such as content or product suggestions, while the customer is still on-site. 

For example, an online retailer using OI might detect a customer hesitating at checkout and offer a targeted discount to close the sale. By continuously analyzing customer interactions and context, OI helps brands respond to each customer’s needs in the moment, improving engagement and satisfaction. One case in point: using operational intelligence, an e-commerce site detects order delays and notifies the customer proactively, while a hotel chain adjusts room rates dynamically as demand shifts. These capabilities build loyalty through responsive, context-aware service.

IoT monitoring and operational efficiency

Many organizations use operational intelligence to improve physical operations via Internet of Things (IoT) data. In manufacturing and logistics, for example, sensor-equipped machines, vehicles, and devices stream a constant flow of data that is analyzed in real time. OI platforms digest this torrent of information to provide a live view of business operations and automation for adjustments. 

A logistics company might use operational intelligence to monitor fleet vehicles’ locations and conditions, then reroute deliveries to avoid traffic delays or mechanical issues. Similarly, factories use OI for predictive maintenance: Equipment sensor data is analyzed continuously to detect early warning signs of a malfunction so it can be repaired before a breakdown halts production. 

Gartner notes that ideal OI use cases often involve “business observation data” such as IoT telemetry and real-time log streams, where immediate data analytics are required. By applying OI to these data streams, organizations improve uptime, safety, and efficiency. For instance, utilities use operational intelligence to balance grid loads in real time, and healthcare providers monitor patient vital signs live to alert clinicians of any sudden changes. In all cases, the ability to respond quickly to events in the physical world saves money, improves outcomes, and even saves lives.

How technology supports operational intelligence

Operational intelligence requires the right data architecture and tools. Traditionally, online transaction processing databases were kept separate from analytics platforms because intensive analytic queries slowed down operational systems. In fact, performance and scalability limitations historically prevented advanced analytics from running concurrently with transaction processing. Today, those barriers are being overcome by a new generation of data technology purposely built for real-time, mixed workloads. Technology that provides this includes:

Hybrid transactional/analytical processing (HTAP) databases

Today’s data platforms are designed to handle both high-volume transactions and complex analytical queries on the same system. This is typically accomplished through in-memory computing, distributed clustering, and optimized data structures that serve fast lookups and aggregations simultaneously. Gartner has observed the growth of in-memory database management systems, and even traditional databases adding in-memory options, which support operational intelligence in organizations. By keeping hot data in memory and scaling horizontally, these systems deliver sub-second analytic results on fresh transactional data without slowing down front-end applications.

Integrated AI/ML capabilities

A major OI factor is embedding machine learning models and AI algorithms into operational workflows. Instead of offline data analytics, models score events and make predictions in real time, such as scoring a transaction for fraud risk or predicting a machine’s failure probability as sensor data streams in. Databases are evolving to accommodate this by allowing ML algorithms to run within the data platform. Gartner points to features such as vector similarity search and generative AI inside databases, which broaden the functionality of OI solutions. In practical terms, this means an OI system could, say, perform a real-time similarity search on a customer's behavior vector to find personalized product recommendations during their session. Such in-database machine learning and artificial intelligence capabilities cut down latency because data doesn’t have to leave the operational system to be processed.

Unified real-time data platforms

Vendors are now offering unified platforms that combine multiple data models and workload types to support operational intelligence use cases. These systems ingest events from IoT devices, transaction streams, logs, and more, then store and process them in a way that supports real-time querying and data analysis. 

Aerospike’s real-time database is one such platform noted in the industry. It handles diverse workloads across key–value, document, graph, and SQL data models in one system for efficient queries across mixed data sets while maintaining high throughput up to petabytes of data. With a unified platform, organizations avoid the need to maintain separate databases for different data types or separate analytic clusters for heavy queries. This simplification not only improves performance by removing data transfer bottlenecks but also lowers cost and complexity, because one platform can do the work of several. As a result, companies focus on developing intelligent applications rather than managing infrastructure between siloed systems. The inclusion of Aerospike as a sample vendor in Gartner’s data management Hype Cycle underscores how unified platforms deliver operational intelligence at scale.

Taken together, these technological advances mean that implementing operational intelligence is more feasible than ever. The tools to blend transactions and predictive analytics in real time are mature and readily available. An organization that takes advantage of them will compete better because it will have access to all its data at once.

Operational intelligence going forward

Operational intelligence is no longer a forward-looking idea on the horizon. It’s here now, and it’s rapidly becoming a standard practice for data-driven enterprises. Gartner’s recognition of OI as “early mainstream” with high business benefit is a clear signal that leading organizations are already investing in these capabilities. 

For businesses that haven’t yet started, the time to begin is now. Experts advise taking a pragmatic approach: Educate business and IT leaders about OI’s value, identify concrete process improvements or new services OI could support, and then pilot OI in a focused project to demonstrate impact. It’s wise to start with use cases that truly need real-time insight, such as IoT operations or customer-facing services, and to partner with technology providers that support real-time processing natively. Early wins will build the case for broader adoption, and given the fast pace of innovation, even a small pilot inspires change.

The bottom line is that operational intelligence has moved from nice-to-have to must-have. Businesses that use live data for real-time insights will be more agile, efficient, and innovative, while those that don’t will increasingly find themselves a step behind. As Gartner’s analysis and numerous real-world examples show, steering business operations in real time helps almost every industry.

Ready to look at operational intelligence in your organization? Now is the time to act. Contact Aerospike to discover how our real-time data platform serves as the foundation for your company’s operational intelligence and helps your enterprise make smarter, informed decisions faster.