5 Key Considerations for Operational Intelligence

operational Intelligence

Technology advancements now make it possible for organizations to stream data and have access to real-time content including click-stream, sensor and general business transactions. Here, RTInsights expert contributor Lyndsay Wise explains how organizations can leverage data analytics to enable real-time data streaming and operational intelligence.

Even though it is possible for organizations to leverage data analytics to enable real-time data streaming and operational intelligence, it doesn’t mean that every organization needs to leverage operational analytics simply because the technology exists. Identifying the justifications behind operational intelligence is an important first step for organizations before they decide to embark on transitioning towards operational business intelligence. After all, in many cases, infrastructure considerations and changes will be required before businesses can adopt an operational intelligence framework.

Why Consider Operational Intelligence?

Advanced database technologies change the way data is delivered and stored, and enables organizations to leverage several types of analytics. More businesses are taking advantage of operational intelligence to create a full view of their operations and daily transactions. The insights gleaned help them make sure that business is running smoothly. At the same time, operational insights can be used for projections to support a complete analytics cycle-historical, operational and predictive.

One of the challenges to achieving this level of information accessibility remains the infrastructure required to deliver the type of complex analytics required. Depending upon what an organization hopes to achieve, their traditional business intelligence (BI) platform may differ from real-time streaming and operational analytics needs. To ensure successful implementation and delivery, organizations need to evaluate their requirements before trying to implement all of their BI-related goals. The following considerations provide the initial justifications for an operational intelligence initiative and will help organizations hone in on their need for operational intelligence.

Purpose of Analytics

Any analytics project requires an understanding of the business challenges that exist which make its use necessary. Organizations should identify why they are leveraging analytics and what problems they hope to solve. Looking at sales analytics and optimizing marketing campaigns will require a different approach than providing customer-facing analytics or maintaining a certain level of product quality throughout the manufacturing process. By understanding the goal of a project, companies can look at the type of data required, how it should be leveraged and what the desired outcomes are.

The type of analytics will also help identify the latency required, which will be addressed in the next section. Tying analytics needs to infrastructure becomes important because not all BI platforms are built the same. Therefore, it becomes important to understand what analytics will be used for to identify the type of infrastructure required to support it. Not all solutions can stream data in real-time. Some provide change data capture (CDC) while others provide updates at regular intervals. Understanding the purpose of analytics will help ensure the platform selected can support the end goal and provide the visibility required to answer the business challenges being addressed.

Timeliness of Data

Part of the concept of operational intelligence involves ensuring data latency requirements meet the needs of the business. Some operational intelligence projects require real-time data loading such as financial transactions or trades. Retail transactions might require near real-time depending upon the types of analytics required. Developing realistic expectations surrounding latency is important because not all operational analytics require real-time data. Depending upon the resources of the organization, different infrastructures are realistic (e.g., whether cloud or on-premises) and some businesses might not be able to support the type of infrastructure required or they may have to make adjustments to the way in which data is stored and accessed.

Current Business Intelligence Infrastructure

The current BI infrastructure will determine how analytics can be leveraged. For operational analytics to work, the right infrastructure needs to exist. Therefore, understanding what exists and the possibilities of use means that it becomes possible to evaluate what can be leveraged by using the current infrastructure and to recognize any gaps that may exist. These gaps will help the organization identify what needs to be developed or acquired and the right type of platform to support operational intelligence.

Many organizations make the mistake of trying to leverage current infrastructures for operational needs, thinking they can add product capabilities without realizing that a front-end solution will only be as efficient as the platform supporting it. What this means on a simple level is, if data cannot be streamed, operational intelligence cannot be leveraged. Evaluating gaps will provide the details needed to identify whether or not a current data provider or IT infrastructure can support operational intelligence, whether or not vendors can provide add-ons to help a company achieve their goals or, alternatively, what investments an organization needs to make to ensure operational success.

Current Benchmarks

Another way to determine operational analytical needs is to benchmark other deployments within the same industry to identify what competitors are doing, how they are building up their infrastructures and what types of analytics they are using. Understanding what the competition is doing can provide a way to hone in on the most important aspects of analytics, which metrics to identify and what data assets to leverage. In many cases, there will be organizations that are early adopters of technology and they can provide the basis for operational intelligence justification. The balance for organizations is to identify ways that will make them retain competitive edge and not simply follow the initiatives of others to keep up with the analytics usage of others.

Strategic Business Intelligence/Analytics Goals

Evaluating overall BI goals will provide a starting point for operational BI justification. In many cases, organizations need to gain a better understanding of their customers. This can be through social media, sales trends and demographics. All of these use cases can be leveraged through more traditional BI adoption methods. Strategic goals include those that will provide competitive advantage, and part of that means leveraging different types of analytics to not only identify trends or sales performance but also operational insights, which can be used to increase customer satisfaction and plan for the future.


Understanding what is occurring within the organization to increase operational efficiencies and identify current opportunities and challenges are some of the benefits of operational intelligence adoption. Although operational analytics might not apply to all organizations, traditional BI applications alone are no longer enough to maintain data visibility and competitive advantage. Organizations need to leverage their data in a variety of ways, and one way involves identifying whether or not leveraging real-time data provides competitive edge and, if so, how to best leverage data to do so. These considerations we’ve discussed represent the starting point for organizations trying to decide whether or not transitioning towards operational intelligence is in their best interest and, if it is, what areas to look at to support its use.

Lyndsay Wise

About Lyndsay Wise

Lyndsay is the president and founder of WiseAnalytics. With 15 years of IT experience in business systems analysis, software selection, and implementation of enterprise applications, she provides consulting services for small and mid-sized companies and conducts research into leading technologies, market trends, BI products and vendors, mid-market needs, and data visualization. She is the author of Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize ROI.

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