Operational Intelligence: Definition and Examples

The ability to make decisions quickly often means the difference between success and failure in business. However, fast decisions aren’t always the best ones, as making the best choices requires data-driven information.

Operational intelligence (OI) boosts efficiency by driving effective decision-making in real-time. This article explores the ways in which OI is shaping the future of businesses across many industries.

What is operational intelligence?

OI is the analysis of data collected or generated in real time by an organization’s IT infrastructure. It also includes presenting the results of that analysis to users in an understandable format that allows them to quickly make effective decisions based on those results.

Operational intelligence vs. business intelligence

Business leaders must clearly understand the differences between OI and business intelligence (BI) to contextualize these processes and take action on the insights they provide. Both types of intelligence involve driving action through informed decision-making, but there are key differences in the ways these two types of analysis accomplish this goal.

OI focuses on systems by collecting and analyzing data in real time to identify bottlenecks that could impair the operations of those systems. It also assists front-line workers in making better decisions for dealing with those issues.

In contrast, BI maintains a more narrow focus on increasing revenue or profit. This process usually involves taking a snapshot of data at a particular point in time, which users can then review to better understand how they can improve their operations.

Operational intelligence vs. business process management

OI solutions may include many specific features and technologies. Business process management (BPM) is one such component, which enables the execution of model-driven processes and policies. These models are often known as business process model and notation (BPMN) models.

What are the challenges in achieving operational intelligence?

Implementing OI isn’t always as straightforward as other types of digital upgrades. There are three main challenges in this endeavor, include resistance to change, the tradeoff between quality and speed of data analysis, and the ability to prioritize processes.

Resistance to Change

Not everyone in an organization will be enthusiastic about using OI tools at first, no matter how forward-thinking they may consider themselves. The reasons for this resistance can include a general lack of the necessary technical skills, an outdated corporate culture, and the complexity of reorganizing processes and roles to implement these tools.

A promising solution for overcoming this challenge is to promote digital literacy and innovative thinking among the organization with thorough, targeted employee training. The beauty of operational intelligence is that, once implemented, it becomes clear to all that the new processes far exceed the old ones in terms of speed and operational efficiency.

Quality vs. speed

Achieving the delicate balance between the quality and speed of data analysis is one of the most challenging aspects of implementing OI. Organizations with strict data quality requirements will find it especially time-consuming to prepare data for the analysis needed to obtain meaningful insights with OI.

Developers must find the right compromise between these factors based on the organization’s immediate requirements, long-term goals and operating environment.

Prioritizing data

When a new digital tool is introduced, the possibilities it offers can be overwhelming. Competing urgent demands, limited resources, and upper management expectations only add to a business’ struggle to identify the highest priority data for its needs.

When in doubt, stay as true to the original set of objectives as possible. Once the new system is customized and running, monitor the quality of the data analysis in respect to those objectives. Most OIs were developed with the knowledge that we don’t always get things exactly right from the beginning; processes can usually be altered and added.

Reading recommendation: What is Operational Excellence?

What features drive operational intelligence?

OI requires organizations to either integrate several technologies together or implement a single tool with multiple features. These features can be classified into the general categories of data collection, analysis and visualization.

Data Collection

OI requires organizations to monitor server and network events in real time. That does not necessarily mean the process occurs instantly, however. “Real time” generally refers to the time needed to harvest information, clean it, and make it available for decision-making. This can take less than one second, or as much as a minute.

The constant stream of data resulting from OI data collection allows analysts to access the most current information about what’s happening on the server or network.

Data Analysis

The capability of breaking down data silos is one of the core benefits of analysis in OI. Today’s large organizations routinely run multiple web-based applications, generating many IT incidents. Each of these applications is a separate data source that analysts must investigate to determine if it’s still functioning normally.

OI solutions provide the ability to correlate events from these sources, eliminating the need investigate each of these sources individually. These tools break down data silos by gathering data from multiple sources in a way that allows software or humans to analyze this data all at once rather than separately.

This process is the core component of data analysis in OI, which leverages techniques like artificial intelligence (AI) and machine learning (ML). These features allow OI to analyze data more efficiently than traditional approaches, thus facilitating the process of obtaining actionable insights from raw data.

Data Visualization

Modern OI solutions are highly effective at pulling data from multiple sources and have the capacity to process millions of data points each day. They generally use dashboards to present that data and make it actionable.

Analysts can configure these dashboards to display data in a variety of ways, allowing them to be customized based on the analyst’s particular role in meeting their organizations’ needs.

Read too: Process Excellence: The Complete Guide

How is operational intelligence implemented?

The process of implementing OI may be divided into the following six phases.

Develop objectives

OI has broad applications, but it’s still essential to identify the areas where it will have the greatest impact. This process involves using the organization’s key pain points to determine how OI can perform timely, actionable analysis that can mitigate them.
Build a team

Assemble a team that can address challenges by selecting, building and operating the OI solution. A C-level executive like a CDO, CFO, CIO, CMO or CTO typically leads this phase of implementation based on the particular challenges the project is intended to address.
For example, an OI initiative designed to improve network uptime is often led by a CTO, whereas one that monitors retail traffic would be more likely to use a CMO.

Assemble a team that can address challenges by selecting, building and operating the OI solution. A C-level executive like a CDO, CFO, CIO, CMO or CTO typically leads this phase of implementation based on the particular challenges the project is intended to address.

For example, an OI initiative designed to improve network uptime is often led by a CTO, whereas one that monitors retail traffic would be more likely to use a CMO.
Analyze the operational data

An effective OI implementation requires a thorough understanding of the data it will analyze. The raw data must be sufficient for this process and accessible by the solution for it be effective.

This phase primarily consists of an audit of the data to determine what data is being generated and how it’s being stored. The current analysis that the organization is performing is also a key element of the data audit.
Improve the data

An organization’s data is rarely ready for OI without improvement, whether the volume is insufficient, the quality adequate, or the data is simply out-of-date. Cleaning up the data before launching an OI solution is essential for avoiding the analytical errors that will inevitably result from bad data.

This process tends to be complex because it typically requires team members to upgrade data feeds and redesign the architecture of some systems. In addition, it may require new data sensors or changes to the procedures for logging transactions.
Create metrics

The development of key performance indicators (KPIs) for the OI solution usually occurs at the same time as data improvement. KPIs should provide a quantitative measurement for the problems this solution is intended to improve.

Common goals of OI include reducing downtime, reducing customer wait times or increasing sales. All of these improvements are relatively easy to quantify by selecting the appropriate KPIs.
Start small

Starting small is generally good advice for any major IT undertaking, but it’s especially beneficial for implementing OI. A pilot project that focuses on a single KPI is a best practice that will allow you to incrementally add problems to solve, along with their related metrics.

For example, an OI solution that was originally designed to reduce downtime can later begin monitoring customer reviews. Another capability to add might be identifying the cause of application crashes, which is a common cause of downtime. This practice allows the OI solution to prove its value by building on its successes.

Read too: 6 Steps to Improve Processes Efficiency & Achieve Business Goals

Operational intelligence examples and use cases

Organizations can deploy OI to solve general business problems, but they can also derive value for their industry. The following use cases provide practical examples of the specific benefits OI can provide for certain sectors or departments.

Finance

Financial organizations can use OI platforms to obtain alerts and insights on time-critical issues, such as currency rates and stock prices. This capability is especially important for detecting potential fraud.

Human resources (HR)

HR managers use OI to analyze the performance of staff members, optimize their workload, and monitor recruitment conversion.

IT

IT leaders use OI to monitor their infrastructure, especially for the purpose of reacting to possible problems like system failures and attacks.

Logistics

OI provides constant visibility into logistical components like delivery times, expiration dates, inventory levels, and supply chains.

Manufacturing

OI is an essential tool for monitoring production lines, especially in the capacity of machine function. This capability helps improve the performance, maintenance, and management of these assets.

Retail

Retailers use OI to track product demand, expiration dates, and staff allocation.

Sales and Marketing

OI can analyze the results of marketing campaigns, allowing marketers to fine-tune their targeting methods.

Read too: Mastering Operations Strategy: A Guide to Streamlining Business Operations

Implement operational intelligence with Pipefy’s help

OI focuses on maintaining the health of IT systems, which requires the implementation of multiple technologies working together to provide real-time monitoring, data analysis and dashboards. Pipefy’s Business Process Automation (BPA) software delivers a comprehensive solution for driving effective business decisions.