Operational intelligence is a collection of business analytics systems designed to aid decision-making in real time. OI gathers various data feeds that represent ongoing business operations and related external factors, then analyzes and digests these feeds as the data arrives.
This data could include information about sales at the company’s retail outposts, utilization of a company’s vehicles or even broad environmental information such as real-time ambient temperature — essentially, whatever data is useful to a company’s decision-making process.
The data sources in an OI implementation can be quite varied and diverse, but they're largely drawn from a company’s most important business processes. In a typical scenario, this information is presented in a dashboard format, with data to showcase the most important information, annotated with alerts calling attention to key outliers or trends.
Depending on your needs, data may be drawn from a CRM tool, stock market transactions or real-time sales reports. OI is also commonly used in IT operations to monitor operational metrics around networks and servers, security threats, application deployments and more.
Newer technical developments have allowed for more granular detail to be incorporated into OI solutions. OI data may be drawn directly from IoT sensors embedded into machines on the factory floor, or from measurements within a company’s telecommunications infrastructure. By correlating key data points from various sources, an OI dashboard can be configured to help plan when to spin up additional production lines or deploy standby technicians to hotspots.
Machine-generated IT data from across the organization is a source of deep insight.
In this fashion, OI solutions can get incredibly detailed and complex — delivering increasingly actionable and useful business insights — as myriad data sources are incorporated into the system.
What Is Operational Intelligence: Contents
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What is the relationship between operational intelligence and business intelligence (BI)?
The concepts behind business intelligence emerged in the 1990s to become the well-defined tools that businesses of all sizes rely on today. But recent years have seen the emergence of more advanced technologies that have promoted the development of operational intelligence systems. Operational intelligence is often described as the next generation of business intelligence, a reference to the clear lineage shared by the two schools of data analytics.
The primary differentiator between the two technologies is timeliness. In simple terms, business intelligence relies on historical data such as server logs, past financial reports and industry analysis. It was conceived to digest vast amounts of information into smaller, actionable chunks. Technologies like data mining were developed as a way to glean operational and business insights from large data stores, but this type of analysis took time. That meant large enterprises could only run periodically, delivering occasional snapshots rather than a continuous picture.
On the other hand, operational intelligence tools are designed to be run in real time, using information as it’s recorded to constantly improve analytics. Businesses are no longer tied to archived logs and static information. With OI, they can gather real-time insights and capture intel as it develops, providing useful and timely business insights.
Is operational intelligence better than business intelligence?
OI is not necessarily better than BI, but it does offer some distinct advantages. The timeliness of OI’s real-time insights lets businesses take immediate action regarding opportunities or threats. A business intelligence tool may be less insightful or relevant if it’s fed quarterly sales reports and annually released industry statistics; resulting insights will be at least a few months out of date by the time the end user sees them.
Many users may feel unsatisfied with business intelligence tools that tell them what they’ve already known for weeks or months. As a result, they may turn to OI solutions that can provide real-time visibility. But that’s not to say that historical data and analysis lack value, as this can have a significant bearing on an organization’s future. BI and OI often work together, pairing BI’s broad historical analysis with OI’s real-time visibility for a more complete, strategic view of the enterprise and the market.
What is real-time business intelligence?
Real-time business intelligence (RTBI) predates OI. The concept was originally developed to give more timeliness to BI solutions. While BI is backward-looking by design, the idea behind RTBI is to give a business intelligence solution a more current data source to rely upon. RTBI still uses a historical data source to generate its insights, but it looks at a database that’s up-to-date rather than logs that are several months old. In a typical RTBI setting, any secondary information (such as industry reports) are rejected as a data source if they aren’t current.
What are the key features of operational intelligence?
Key features of operational intelligence solutions include real-time monitoring; dashboards and visualizations; real-time alerting systems; industry-specific analysis; on-demand report generation; big data and machine learning capabilities; automatic remediation operations; and infinite scalability.
Dashboards and visualizations provide quick insights into operational status and data trends.
Real-time monitoring: This is the very core of what defines OI. Every OI solution will monitor its data sources in real time. Whether that data is drawn from manufacturing floor machine sensors, a retail sales feed or alerts generated when an application deployed to customers begins to crash, the key feature of OI is that analysis and alerts are provided as they happen, often within seconds of the event data being generated.
Dashboards and visualizations: Another essential feature of OI is its ability to digest complex information and present it in an easily understandable format. Dashboards are the common mechanism for this, presenting information in a graphical form that takes a mountain of data and makes sense out of it. In a capable OI system, dashboards are also customizable based on the user. A financial auditor and a product developer may both rely on OI information, but will have vastly different decisions to make from it. The ability to customize the way the dashboard and data visualizations look, and what data they rely on, is an essential feature.
Real-time alerting systems: Operational intelligence is also designed to alert the user when key events occur. The user can set specific conditions and thresholds for which a notification is generated. This alert is then populated on the dashboard and/or pushed to the user via email or a mobile device notification, allowing for a proactive response.
Industry-specific analytics: OI solutions are appropriate for a vast array of industries, from manufacturing to retail to financial services, but the needs of those users will be variable. A telecommunications company will have different challenges than a national retail chain or a healthcare provider. Dashboards can be configured based on the company’s industry, making the most important and relevant information visible to the end user.
On-demand report generation: A live dashboard is useful for responding to situations in the moment, as are reports for presenting information to others and building a broader picture of the present environment. The best OI solutions offer reporting that is accessible to everyday users, not just expert data scientists.
- Big data and machine learning capabilities: OI leverages artificial intelligence, enabling advanced models and algorithms to make sense of vast data stores. A capable OI solution must be able to index hundreds of terabytes of data each day, processing and analyzing it to continuously predict potential outcomes and expose new market opportunities.
- Automatic remediation operations: What happens when your OI solution determines there’s a problem? It can either alert you or take action to repair the problem itself. Automatic remediation is a groundbreaking features of OI, with powerful scripting allowing certain operations to be automatically repaired via algorithmic operations.
- Infinite scalability: Data storage and processing needs are expanding exponentially, and an OI solution needs to be able to keep up with this to be of any use. Properly designed OI technologies should be able to scale without limit by simply adding computing power on the fly via a cloud-based infrastructure.
What industries benefit most from operational intelligence?
Operational intelligence has wide use across a large number of industries where maintaining peak service levels is key. Here are some of the industries where OI is finding the greatest level of impact.
- Financial services: OI is being used in finance to monitor markets and ensure uptime of critical financial systems where any amount of downtime could cost millions.
- Telecommunications: OI can monitor network health and spot errors before they become major outages. OI is also a vital tool in detecting network security breaches.
- Manufacturing: Factory uptime is critical in any manufacturing environment. By monitoring machine sensors and other data generated on the factory floor, OI allows a manufacturing facility to be supervised through every stage of a product’s creation and delivery.
- Retail: Whether it is ensuring ecommerce website uptime or analyzing sales patterns to locate trends, OI has exceptional utility in trend-driven retail environments.
- Transportation: Airports and other travel hubs are using OI to manage passenger and vehicle flow, minimizing incidents and ensuring a smooth travel experience for everyone.
- Medical: OI tools can be used to monitor a variety of healthcare operations, including hospital patient intake/triage and pharmaceutical inventory management.
How do you get started with operational intelligence?
Start your operational intelligence initiative with these seven steps, beginning with objectives and working through to initial pilot:
- Understand your objectives: While OI has broad applicability, you have to identify where it will have the greatest impact. Identify problems that OI can solve by unearthing key pain points in the organization, then ask how OI’s delivery of more timely, actionable data analysis can help solve them.
- Build a team: Once your challenges are identified, it’s time to start putting together a team that will select, build and operate the OI solution. This is often spearheaded by someone in the executive suite (CIO, CTO or CDO, CFO or CMO), depending on the particulars of the problem you’re trying to solve. For an OI initiative aimed at improving network uptime, the CTO might be the best sponsor, while an initiative aimed at monitoring retail traffic patterns could fall to a CMO.
- Take stock of your operational data: OI requires data to be effective. This means you need to understand what your data looks like before you try to shoehorn it into an OI solution. OI will fail immediately if your raw data feeds are not sufficient, or if operational data is not accessible. Audit your data stores to determine what is being generated, where it’s being stored and how it is currently being analyzed.
- Improve your data: Chances are you’ll find that some of your data is either insufficient in volume, low in quality, out of date or all of the above. Clean up your data feeds before you launch an OI initiative; otherwise you risk bad data, leading to bad analysis and bad decisions. Data cleanup is likely to be a complex endeavor that involves the upgrade of data feeds or rethinking how certain systems are architected. That might require new machine data sensors or changing the way key transactions are logged.
- Set up metrics: At the same time as step four, you’ll want to identify in quantitative terms specific KPIs for what your OI solution is being designed to improve. That could be a reduction of downtime from 0.1 percent to 0.01 percent, reduction of customer wait times by an average of two minutes, or an increase of sales by five percent. Whatever constitutes measurable, meaningful progress.
- Select an OI solution: Now it’s finally time to identify an OI solution or tool. We’ll talk about key things to look for in an OI vendor in the following section.
- Start small and build from there: As with any large tech undertaking, you’ll want to learn to walk before you run. Take a single KPI and launch an OI pilot. From there, add related problems and associated metrics to the solution. An OI solution initially tasked with reducing app downtime might later start monitoring customer reviews of that app, or help determine the cause of app crashes. Build on your successes as the OI solution proves its value.
How do you choose the best operational intelligence solution/tools?
You choose the right OI solution by considering your specific industry and need. While every implementation will be different, here are six key considerations:
- Are the insights truly generated in real-time? Some solutions may be OI in name only and may not offer genuine real-time analysis.
- Is the solution cloud-based or on-premises? Cloud-based solutions will generally allow much greater scalability.
- Is the solution usable by line of business professionals, not just data scientists? Your OI tool has a much greater chance of success if it’s widely adoptable across the organization.
- Does the solution offer customized dashboards relevant to your industry and your particular use case?
- Does the solution complement existing intelligence tools and other technologies your already have in operation?
- Is the solution ready to connect with your machine data (with the needed drivers, APIs and other connecting technologies available) out of the box?
OI can fundamentally change the way you do business
When analytics and insights move from backward-looking to real-time, you can really change the way decisions are made and the way your analytics team contributes to business results. OI can turn machine data and other inputs into tangible insights that improve your business’s productivity, security and profitability.
Check out Splunk’s white paper, “The Path to Operational Intelligence,” to learn more about how your existing data can be used to turn outdated, reactive problem solving methods into real time, data-driven insights. The first steps are easier than you think.
