Operational intelligence (OI) is the process of collecting and analyzing real-time operations data to monitor system health and pre-emptively reduce issues. Traditional OI was primarily concerned with IT operations: data and metrics related to servers, networks, application deployments, configurations, and IT security. With the introduction of the Internet of Things (IoT) and smart sensors, OI now includes the real-time monitoring of real-world operations such as pipelines, machinery, and energy equipment. In both cases, OI uses real-time data collection and analysis to proactively discover operational trends, anticipate issues, and help frontline workers make the best decisions for troubleshooting and maintenance.
What is industrial operational intelligence?
Industrial operational intelligence is a term used in organizations that manage physical industrial infrastructure and machinery, such as power stations, logistics networks, and mining. In these business environments:
- The infrastructure is more niche
- The range of IoT devices is far wider
- There might be a lengthy and complex supply chain
- Machine data analysis is far more complex
These organizations usually require a powerful, industry-specific solution or custom-configured software to handle the architecture, data capabilities, and workflows of their operations. Custom solutions integrate specific IoT device networks with specialized analytics software. For example, an energy operation uses sensors to measure windmill performance metrics and make real-time decisions to maintain, switch off, or fix the windmills. Data is also used for predictive planning of new infrastructure based on anticipated demand.
What are the benefits of operational intelligence?
OI is a highly useful solution for modern enterprises or smaller businesses with complex, interconnected system architectures. The following are some benefits of OI.
Real-time operations monitoring
You can use modern OI systems to monitor the state and operational health of systems and their communications in real-time. In the past, it was up to IT teams to retrospectively examine historical data logs and snapshots to determine system and communication status. It often led to lengthy wait times in data analysis, reporting, and business decision-making. Today, you can develop queries that run on real-time operational data to provide up-to-date visualizations and reporting.
Fault identification
With OI tools, you can map the regular flow of operational data for enhanced system visibility. It gives a big-picture view of how data moves between different system components so that you can identify any deviations from normal system operations. More importantly, you can build out intelligent workflows in the OI system that detect faults and automatically trigger remediation actions. After you set up alerting, you can search through logs to identify root causes and resolve performance bottlenecks or fault patterns.
Strategic decision-making
OI enables the monitoring of business processes and systems to discover suboptimal usage, configurations, and cost inefficiencies. You can make informed decisions about changing the state of your business’s system architecture. Some OI solutions can also simulate the impact of system changes across the operational chain for improved insights and decision-making support.
Risk reduction
Awareness and visualization of the current state of business operations automatically reduces risks across the board. With OI, you have an integrated view of all operations data and key performance indicators, so you can make sure that operations are aligned with business goals. There is less opportunity for unforeseen risk to cause a sudden catastrophic impact to business.
How does operational intelligence work?
OI systems combine data-driven technology with business strategy. The following is an overview of the process.
Data collection
OI workflows begin by gathering data. This can include real-time data streams such as logs, metrics, and performance data, or user behavior data. Examples of data sources include:
- IT infrastructure, such as servers, databases, and networks
- Desktops and mobile devices
- IoT devices, such as sensors and smart meters
- Security platforms
- Clickstreams
- Applications
The data collection prioritizes capturing every aspect of system operations, from usage metrics and user interaction to machine performance and environment data.
Data processing and analysis
After data is collected, the system processes it by using various computational techniques. For example, complex event processing identifies and analyzes patterns of events across multiple streams of data. Data processing also includes filtering, aggregating, and transforming data to prepare it for analysis.
Visualization and reporting
To make the insights accessible, operational systems provide visualization features, such as dashboards and reports. You can observe trends, patterns, and anomalies in your operations in an intuitive format, often in real time. The OI solution also generates alerts and helps you prioritize tasks that need actioning when certain predefined criteria are met: for example, when operational metrics cross a threshold. The system can also trigger automated actions such as shutting down services, isolating operations, or adding new services if required.
Automated adaptation
Many OI systems incorporate machine learning (ML) algorithms that improve over time. They learn from the outcomes of past decisions by continuously refining the criteria for alerts and the actions taken in response to specific patterns or anomalies. This adaptive aspect helps progressively enhance the effectiveness of the system.
The following diagram shows an example of OI in a factory, from data collection by on-premises IoT devices, to data ingestion and processing in the cloud, to a user interface for the production manager.
What are the key technologies in operational intelligence?
OI uses several technologies, many of which overlap with other intelligence analytics systems. The following is a broad overview.
Operational intelligence software
OI software provides a self-service toolkit for data exploration and search, alerting, dashboards, reporting, and monitoring business processes. There are different software solutions that offer a range of available data sources, tools, actions, workflows, and integrations. You need to select the solution that best fits your business needs.
Stream processing technologies
A significant percentage of operational data is streaming data, or data emitted at a high volume in a continuous, incremental manner. Stream processing technologies can buffer, process, transform, and store streaming data at high speed while continuously moving it towards analytics. They include complex event-processing technologies that can identify patterns and relationships across multiple streams of real-time data.
Automation and orchestration
Automation technologies integrate into OI systems to trigger actions based on the insights derived from data analysis. Orchestration tools are required to respond to events by deploying resources, adjusting configurations, or triggering processes without human intervention.
Analytics technologies
OI systems integrate with existing business analytics to ensure that insights and actions are delivered in the operational context of an organization. Artificial intelligence (AI) and ML algorithms are used to predict trends, prescribe actions, and automate decision-making. Data visualization tools provide dynamic dashboards and reporting capabilities that transform complex data sets into graphical representations that everyone can understand.
What is the difference between operational intelligence and business intelligence?
Business intelligence (BI) refers to analytics that support better business outcomes. When data analytics emerged as a field, it focused on historical business data to support future decision-making. Data analytics has expanded to include real-time data analysis across various domains. When it supports decision-making in operations, it’s called operational intelligence.
A key difference is that OI includes proactive monitoring and taking immediate actions to fix runtime operational issues. BI has a more long-term and retrospective focus, and doesn’t have the alerting and troubleshooting aspect.
However, OI and BI are related because OI plays a crucial role in advancing BI. You typically have BI and OI solutions working alongside each other. Many business goals such as optimizing business sales, building a better product-market fit, and understanding user behaviors benefit from operational data analytics. You can feed data and analysis from OI software into BI tools to get a broader picture of your business.
What are the challenges in operational intelligence?
The challenge in analyzing massive amounts of operational data still lies in having clean, well-structured data to gain insights. Data must be clean, tagged, and organized, and historical data must be stored correctly so that you can make sense of the underlying OI solution. The following are some other challenges.
Steep learning curve
The barriers to analyzing data have decreased from when you worked with basic databases and command line interfaces (CLI). However, manipulating and combining data—and making the right queries and analysis—remains a challenge. An understanding of statistical analysis and how to develop complex queries is critical for just-in-time management. It can take time to build the required skill set in your team.
Data and system security
Data governance and management remain a concern throughout the operational workflow. Although OI dashboards and reporting can be useful to a wide variety of users throughout your business, you must implement appropriate measures to prevent unauthorized access. Metadata about operations is also confidential and must be sufficiently protected.
