Operational intelligence

Last updated December 06, 2024

Operational intelligence (OI) is a category of real-time dynamic, business analytics that delivers visibility and insight into data, streaming events and business operations. OI solutions run queries against streaming data feeds and event data to deliver analytic results as operational instructions. OI provides organizations the ability to make decisions and immediately act on these analytic insights, through manual or automated actions.

Purpose

The purpose of OI is to monitor business activities and identify and detect situations relating to inefficiencies, opportunities, and threats and provide operational solutions. Some definitions define operational intelligence as an event-centric approach to delivering information that empowers people to make better decisions, based on complete and actual information.

In addition, these metrics act as the starting point for further analysis (drilling down into details, performing root cause analysis — tying anomalies to specific transactions and the business activity).

Sophisticated OI systems also provide the ability to associate metadata with metrics, process steps, channels, etc. With this, it becomes easy to get related information, e.g., "retrieve the contact information of the person that manages the application that executed the step in the business transaction that took 60% more time than the norm," or "view the acceptance/rejection trend for the customer who was denied approval in this transaction," or "Launch the application that this process steps interacted with."

Features

Different operational intelligence solutions may use many different technologies and be implemented in different ways. This section lists the common features of an operational intelligence solution:

Real-time monitoring
Real-time situation detection
Real-time dashboards for different user roles
Correlation of events
Industry-specific dashboards
Multidimensional analysis
- Root cause analysis
- Time Series and trend analysis
Big data Analytics: Operational Intelligence is well suited to address the inherent challenges of Big Data. Operational Intelligence continuously monitors and analyzes the variety of high velocity, high volume Big Data sources. Often performed in memory, OI platforms and solutions then present the incremental calculations and changes, in real-time, to the end-user.

Technology components

Operational intelligence solutions share many features, and therefore many also share technology components. This is a list of some of the commonly found technology components, and the features they enable:

Business activity monitoring (BAM) – Dashboard customization and personalization
Complex event processing (CEP) – Advanced, continuous analysis of real-time information and historical data
Business process management (BPM) – To perform model-driven execution of policies and processes defined as Business Process Model and Notation (BPMN) models
Metadata framework to model and link events to resources
Multi-channel publishing and notification
Dimensional database
Root cause analysis
Multi-protocol event collection

Operational intelligence is a relatively new market segment (compared to the more mature business intelligence and business process management segments). In addition to companies that produce dedicated and focused products in this area, there are numerous companies in adjacent areas that provide solutions with some OI components.

Operational intelligence integrates information, supporting smarter decision making in time to maximize impact. By correlating a variety of events and data from both streaming feeds and historical data silos, operational intelligence helps organizations gain real-time visibility of information, in context, through dashboards, real-time insight into business performance, health and status so that immediate action based on business policies and processes can be taken. Operational intelligence applies the benefits of real-time analytics, alerts, and actions to a broad spectrum of use cases across and beyond the enterprise.

One specific technology segment is AIDC (Automatic Identification and Data Capture) represented by barcodes, RFID and voice recognition. Another specific technology is the OKAPI platform. It is an Operational Excellence software platform that uses artificial intelligence and machine learning to provide companies with SMART KPIs. The platform then uses data visualization to track the progress of hitting KPIs.

Comparison with other technologies or solutions

Business intelligence

OI is often linked to or compared with business intelligence (BI) or real time business intelligence, in the sense that both help makes sense out of large amounts of information. But there are some basic differences: OI is primarily activity-centric, whereas BI is primarily data-centric. As with most technologies, each of these could be sub-optimally coerced to perform the other's task. OI is, by definition, real-time, unlike BI or “On-Demand” BI, which are traditionally after-the-fact and report-based approaches to identifying patterns. Real-time BI (i.e., On-Demand BI) relies on the database as the sole source of events.

OI provides continuous, real-time analytics on data at rest and data in-flight, whereas BI typically looks only at historical data at rest. OI and BI can be complementary. OI is best used for short-term planning, such as deciding on the “next best action,” while BI is best used for longer-term planning (over the next days to weeks). BI requires a more reactive approach, often reacting to events that have already taken place.

If all that is needed is a glimpse at historical performance over a very specific period of time, existing BI solutions should meet the requirement. However, historical data needs to be analyzed with events that are happening now or to reduce the time between when intelligence is received and when action is taken, then Operational Intelligence is the more appropriate approach.

Systems management

System Management mainly refers to the availability and capability monitoring of IT infrastructure. Availability monitoring refers to monitoring the status of IT infrastructure components such as servers, routers, networks, etc. This usually entails pinging or polling the component and waiting to receive a response. Capability monitoring usually refers to synthetic transactions where user activity is mimicked by a special software program, and the responses received are checked for correctness.

Complex event processing

There is a strong relationship between complex event processing companies and operational intelligence, especially since CEP is regarded by many OI companies as a core component of their OI solutions. CEP companies tend to focus solely on the development of a CEP framework for other companies to use within their organisations as a pure CEP engine.

Business activity monitoring

Business activity monitoring (BAM) is software that aids in monitoring business processes, as those processes are implemented in computer systems. BAM is an enterprise solution primarily intended to provide a real-time summary of business processes to operations managers and upper management. The main difference between BAM and OI appears to be in the implementation details — real-time situation detection appears in BAM and OI and is often implemented using CEP. Furthermore, BAM focuses on high-level process models whereas OI instead relies on correlation to infer a relationship between different events.

Business process management

A business process management suite is the runtime environment where one can perform model-driven execution of policies and processes defined as BPMN models. As part of an operational intelligence suite, a BPM suite can provide the capability to define and manage policies across the enterprise, apply the policies to events, and then take action according to the predefined policies. A BPM suite also provides the capability to define policies as if/then statements and apply them to events.

Related Research Articles

Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

Event processing is a method of tracking and analyzing (processing) streams of information (data) about things that happen (events), and deriving a conclusion from them. Complex event processing (CEP) consists of a set of concepts and techniques developed in the early 1990s for processing real-time events and extracting information from event streams as they arrive. The goal of complex event processing is to identify meaningful events in real-time situations and respond to them as quickly as possible.

Business activity monitoring (BAM) is a category of software intended for use in monitoring and tracking business activities.

Systems management refers to enterprise-wide administration of distributed systems including computer systems. Systems management is strongly influenced by network management initiatives in telecommunications. The application performance management (APM) technologies are now a subset of Systems management. Maximum productivity can be achieved more efficiently through event correlation, system automation and predictive analysis which is now all part of APM.

Business intelligence software is a type of application software designed to retrieve, analyze, transform and report data for business intelligence (BI). The applications generally read data that has been previously stored, often - though not necessarily - in a data warehouse or data mart.

In computer information systems, a dashboard is a type of graphical user interface which often provides at-a-glance views of data relevant to a particular objective or process through a combination of visualizations and summary information. In other usage, "dashboard" is another name for "progress report" or "report" and is considered a form of data visualization.

Real-time business intelligence (RTBI) is a concept describing the process of delivering business intelligence (BI) or information about business operations as they occur. Real time means near to zero latency and access to information whenever it is required.

Microsoft Office PerformancePoint Server is a business intelligence software product released in 2007 by Microsoft. The product was generally an integration of the acquisitions from ProClarity - the Planning Server and Monitoring Server - into Microsoft's SharePoint server product line. Although discontinued in 2009, the dashboard, scorecard, and analytics capabilities of PerformancePoint Server were incorporated into SharePoint 2010 and later versions.

Business process discovery (BPD) related to business process management and process mining is a set of techniques that manually or automatically construct a representation of an organisations' current business processes and their major process variations. These techniques use data recorded in the existing organisational methods of work, documentations, and technology systems that run business processes within an organisation. The type of data required for process discovery is called an event log. Any record of data that contains the case id, activity name, and timestamp. Such a record qualifies for an event log and can be used to discover the underlying process model. The event log can contain additional information related to the process, such as the resources executing the activity, the type or nature of the events, or any other relevant details. Process discovery aims to obtain a process model that describes the event log as closely as possible. The process model acts as a graphical representation of the process. The event logs used for discovery could contain noise, irregular information, and inconsistent/incorrect timestamps. Process discovery is challenging due to such noisy event logs and because the event log contains only a part of the actual process hidden behind the system. The discovery algorithms should solely depend on a small percentage of data provided by the event logs to develop the closest possible model to the actual behaviour.

InetSoft Technology Corporation is a privately owned multinational computer software company that develops free and commercial web-based business intelligence applications. The company was founded in 1996, and currently has over 120 employees between its corporate headquarters in Piscataway, New Jersey, and development offices in Beijing and Xi'an, China.

Truviso is a continuous analytics, venture-backed, startup headquartered in Foster City, California developing and supporting its solution leveraging PostgreSQL, to deliver a proprietary analytics solutions for net-centric customers. Truviso was acquired by Cisco Systems, Inc. on May 4, 2012.

Business process management (BPM) is the discipline in which people use various methods to discover, model, analyze, measure, improve, optimize, and automate business processes. Any combination of methods used to manage a company's business processes is BPM. Processes can be structured and repeatable or unstructured and variable. Though not required, enabling technologies are often used with BPM.

Mobile Business Intelligence is defined as “Mobile BI is a system comprising both technical and organizational elements that present historical and/or real-time information to its users for analysis on mobile devices such as smartphones and tablets, to enable effective decision-making and management support, for the overall purpose of increasing firm performance.”. Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes.

IBM Cognos Analytics with Watson is a web-based integrated business intelligence suite by IBM. It provides a toolset for reporting, analytics, scorecarding, and monitoring of events and metrics. The software consists of several components designed to meet the different information requirements in a company. IBM Cognos Analytics has components such as IBM Cognos Framework Manager, IBM Cognos Cube Designer, IBM Cognos Transformer.

The term is used for two different things:

In computer science, in-memory processing (PIM) is a computer architecture in which data operations are available directly on the data memory, rather than having to be transferred to CPU registers first. This may improve the power usage and performance of moving data between the processor and the main memory.
In software engineering, in-memory processing is a software architecture where a database is kept entirely in random-access memory (RAM) or flash memory so that usual accesses, in particular read or query operations, do not require access to disk storage. This may allow faster data operations such as "joins", and faster reporting and decision-making in business.

Agile business intelligence (ABI) refers to the use of Agile software development for business intelligence (BI) projects. ABI attempts to enable the BI team, business people, or stakeholders to make business decisions more quickly.

In business analysis, the Decision Model and Notation (DMN) is a standard published by the Object Management Group. It is a standard approach for describing and modeling repeatable decisions within organizations to ensure that decision models are interchangeable across organizations.

Embedded analytics enables organisations to integrate analytics capabilities into their own, often software as a service, applications, portals, or websites. This differs from embedded software and web analytics.

Logi Analytics, Inc. is a computer software company headquartered in McLean, Virginia, United States with offices in the UK and Ireland. It offers interactive data visualization products for business intelligence and business analytics. On April 7, 2021, Logi Analytics, Inc. was acquired by insightsoftware.

Apama is a complex event processing (CEP) and event stream processing (ESP) engine, developed by Software AG. Apama serves as a platform for performing streaming analytics over a range of high volume/low latency inputs and applications, such as IoT devices, financial exchanges, fraud detection, social media and similar. Users can define data patterns to listen for and actions to take when these patterns are found, which are defined in the provided domain-specific language called the Event Processing Language (EPL). The core Apama engine is written in C++; the process can also optionally contain a JVM for interacting with user created Java code. Apama focuses on high throughput, low latency and memory efficient performance; used in both Intel benchmarks and smaller machines such as the Raspberry Pi, routers and other Edge/IoT devices. It is particularly noteworthy within the CEP space as being one of the earliest projects, a long term market leader, and innovator of many patents.