CxO Decision Brief: Real-Time Data Processing and Analytics

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Solution Overview

Cogynt is an integrated continuous intelligence platform for real-time data and behavioral analytics. It combines diverse, high-volume stream data processing with an expert-based AI system to enable swift, informed decisions with minimal engineering effort. No-code model authoring and hierarchical complex event processing (HCEP) allow the transformation of raw data into predictive insights and actionable intelligence.

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Real-time data processing provides immediate insight and speeds decision-making. Cogynt cuts the time and resources needed to deliver continuous intelligence solutions and eases management of complex data stream analysis to provide early identification and response to targeted issues or opportunities.

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Adoption of data streaming analytics is essential for maintaining a competitive edge. It helps protect against real-time fraud in financial services, fortifies mission preparedness in government and military, ensures quality in telecommunications, enhances customer experience in retail, prevents losses in manufacturing through predictive maintenance, and enables quick, informed decisions in healthcare.

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Strategies for stream data acquisition, intelligence visualization and use is key for seamless integration and desired results. Subject matter experts in low-code tools and data handling need upskilling. Restructuring may be required to integrate new dynamics between analytics teams and subject matter experts.

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Smaller IT teams that lack technical resources may need extensive support to avoid challenges. Cogynt requires cloud resource and container management expertise, data acquisition, and infrastructure.


This GigaOm CxO Decision Brief commissioned by Cogility.

Cogility’s Cogynt is a continuous intelligence software platform for real-time data stream processing and behavioral analytics. The integrated platform approach addresses the inefficiencies and high costs associated with conventional, ad hoc tooling to build in-house real-time situational awareness and decision support applications.

By combining stream data processing from high volume and diverse sources and employing a patented expert-system-based AI (one of the earliest true AI systems designed to mimic the decisions made by a human expert) in an integrated package, Cogynt allows organizations to deliver continuous intelligence solutions faster and with minimal engineering overhead. The no-code model authoring allows subject matter experts to improve continuous intelligence product efficacy and delivery. Its standout feature, hierarchical complex event processing (HCEP), transforms raw data streams and complex behavioral analytics into predictive and actionable intelligence, setting it apart in the sector.

Cogynt’s key components include:

  • Flexible data integration engine: Aggregates data from diverse sources for real-time analytics.
  • Hierarchical complex event processor (HCEP): Levels of stateful, computational pattern-matching logic to determine high-confidence intelligence with full traceability.
  • No-code authoring: GUI experts, business analysts, and data scientists visually create models of computational logic that streamline development and are self-documenting.
  • Visualization and reporting tools: Provides a user-friendly interface for visualizing complex data and generating BI dashboards and reports.
  • AI and machine learning: Enhances data processing with sophisticated insights.

2. Urgency and Risk

The need for real-time data streaming analytics services is critical. They are being integrated more broadly across various industries, including financial services, retail, healthcare, government, military, and manufacturing, as organizations recognize the value of real-time data insights for operational efficiency and improved customer experience. Using data stream processing, advanced analytics, and AI/ML capabilities in business operations requires a robust, integrated platform like Cogility’s Cogynt.


Immediate consideration is essential to avoid falling behind in a rapidly evolving market landscape. Specific use cases highlighting this urgency include:

  • Financial services: Fraud detection and prevention require real-time data to protect assets and customers. Delaying deployment could lead to significant financial losses and reputational damage.
  • Telecommunications: Continuous network performance monitoring is vital for high-quality service. Without real-time data, issues might not be promptly resolved, leading to customer dissatisfaction.
  • Government and military: Real-time situational intelligence, whether for field operations or proactive insider threat management, is a mission imperative and enables decision advantage.
  • Healthcare: Real-time patient monitoring is critical for quick and informed decisions, especially in emergencies. Delays can compromise patient outcomes.
  • Retail: Personalized customer experiences depend on real-time data. Missing out on these opportunities can result in lower engagement and sales.
  • Manufacturing: Predictive maintenance and quality control rely on real-time data to minimize downtime and defects. Delays can disrupt production and increase costs.


Smaller organizations might not have the technical resources to support Cogynt’s deployment and integration. The platform requires technical expertise in cloud resource and container management, data acquisition, and infrastructure readiness, which can pose challenges for smaller IT teams without extensive support.

3. Benefits

Implementing Cogility’s Cogynt offers several strategic benefits:

Compared to In-House Continuous Intelligence and Decision Support Applications

  • Reduced cost and complexity: Cogynt eliminates the need for large specialized engineering teams by offering a ready-to-use platform that integrates seamlessly with existing data sources and systems, substantially lowering operating expenses.
  • Enhanced agility and efficiency: The integrated platform capabilities and no-code authoring facilitate rapid deployment, model creation, iteration, and ongoing improvement in continuous intelligence solution deliveries, unlike ad hoc, one-off application builds.
  • Lower support demands: With its intuitive design, flexible data ingestion and governance, and comprehensive support, Cogynt reduces the reliance on extensive technical resource involvement for ongoing maintenance and troubleshooting.

Compared to Pure GEN AI/ML Models

  • Traceability and auditability: Cogynt provides full traceability, essential for making informed, high-consequence decisions and for model improvement. These features address the accountability gaps of pure GEN AI/ML models.
  • Structured decision-making: The platform ensures a structured, self-documenting model and a repeatable approach to real-time intelligence, which is crucial for consistent decision-making and reliability, unlike the often erratic outputs of unstructured AI/ML models.
  • Mitigated risks of bias and errors: Cogynt incorporates safeguards against AI biases and hallucination effects, promoting more accurate and fair decision-making outcomes.

General Benefits

  • Enhanced decision-making: Real-time data processing allows for immediate insights and quicker decision-making.
  • Operational efficiency: The platform reduces the time and resources required for data analysis, leading to significant operational efficiencies.
  • Faster continuous intelligence delivery: Supports the ability to develop and manage continuous intelligence products.
  • Risk mitigation: Enables early identification and response to potential threats (or advantages) through advanced pattern matching.
  • Scalability: Capable of processing and analyzing large volumes of diverse data, Cogynt adapts to business growth and needs without compromising performance.

4. Best Practices

To ensure successful adoption of Cogynt, follow these best practices:

  • Substantive training: Provide training for all users to ensure they can leverage the platform’s capabilities effectively. Train users on both technical and strategic aspects of the platform.
  • Incremental implementation: Before full-scale deployment, start with pilot projects to refine processes and understand the platform’s impact—target high-impact areas to demonstrate value.
  • Regular updates: Maintain regular communication and updates to stakeholders throughout the deployment process to ensure alignment and address any issues promptly.
  • Project scoping: Empower subject matter experts (SMEs) by allowing them to define the scope of the desired intelligence intricately. This involves dissecting the modeling process by working in reverse to meticulously outline a map of event patterns, starting from actionable intelligence and progressing downwards to lower levels of event patterns, culminating in the raw data level.
    • In addition, data acquisition and governance requirements must be ascertained in advance.
  • Upfront build vs buy analysis: Do the math on up-front engineering and maintenance costs and hiring in-house or outsourced expertise. Verify if engineering event stream-processing solutions and other required analytic and BI components are among core competencies that can be developed in-house or would require heavy consultation. Determine the operating requirements and cost to ensure agile, iterative improvement. Determine if a real-time event processing solution can be architected in-house without creating a tech-debt-heavy solution that unintentionally results in a batch or static data querying solution.
  • Formalize an operational workflow with executive buy-in: The operations team must identify how to analyze and utilize predictive insights to enhance organizational situational awareness and decision-making, proactively manage risks, and seize opportunities. They must also establish clear methods for consuming and sharing predictive and actionable intelligence.

5. Organizational Impact

Adopting Cogynt will transform organizational operations by enhancing real-time data analysis and decision-making capabilities. This shift will require training for subject matter experts, analysts, and data scientists and may necessitate modification to governance and process management to integrate the new system fully. IT departments will experience a shift in operational models, focusing more on real-time data processing and less on retrospective analysis.

People Impact

For those organizations that are nascent in stream data processing technologies, adopting Cogynt will necessitate training for all data processing and analysis users. This includes change management strategies to integrate the platform seamlessly into existing workflows. Help desk support must be prepared to address issues during the initial adoption phase.

  • Staffing levels: Adopting the platform should increase the efficiency of the analytics program at all levels.
  • Skills acquisition: Training programs to upskill your subject matter experts in low code tools and working more directly with the platform and data.
  • Team structure: Possible restructuring to integrate new dynamics between the analytics teams and the SMEs who will work directly with the low-code platform.

Investment Outlook

Cogynt’s tightly integrated platform lowers the barrier to entry for subject matter experts to help develop continuous intelligence solutions leveraging streaming data analytics. While Cogility does not specifically target small and medium-sized businesses, the integrated platform’s accessibility and ease of use make it a viable option for organizations of all sizes.

Cogynt is available as a Kubernetes cluster hosted within the Cogility cloud, the customer’s cloud, or within one provided by a system integrator, under annual subscription licensing. The intuitive interface reduces the learning curve, enabling a wide range of users to produce continuous intelligence solutions and derive insights without needing to be analytics experts.

Aspect Cogynt DIY Approach
Integration A tightly integrated platform simplifies complexity and ensures seamless operation. Requires selecting and integrating multiple tools, which can lead to compatibility and silo issues.
Cost-Efficiency The lower total cost of ownership due to the subscription model covering updates, maintenance, and support. It may have lower initial costs but higher long-term expenses due to maintenance and operational inefficiencies.
Time to Value Quick deployment with minimal setup; intuitive interface reduces the learning curve. A time-consuming setup and a steep learning curve can delay operational readiness and value realization.
Ease of Use An intuitive interface enables users without deep technical expertise to create models, as well as to derive and share insights. Requires higher technical expertise to manage and utilize various integrated tools effectively.
Scalability Built-in scalability to accommodate growth in data and complexity without additional overhead. Scaling involves integrating new tools or upgrading existing ones, adding complexity.
Future Proofing Continuously updated with the latest technologies and practices in data analytics. Risk of technological obsolescence unless continuously updated and maintained.
Source: GigaOm 2024

Licensing Details

  • Licensing model: Annual subscription.
  • Deployment options: Vendor-hosted or self-managed Kubernetes cluster.
  • Packaging: This includes data integration, real-time processing, modeling, advanced analytics, environment, auditing, and automated deployment capabilities.
  • Pricing considerations: Platform fee plus tiered pricing based on capacity recorded entities/records processing. Customer VCP deployment must also account for customer or CSP infrastructure expenditure.
  • Open-source: Utilizes proven and broadly used software from The Apache Software Foundation that includes Apache Kafka, Apache Flink, Apache Pinot, and Apache SuperSet.
  • Training considerations: Comes with documentation, self-paced training, and optional instructor-led training workshop programs.

6. Analyst’s Take

Over the next three years, Cogility plans to enhance Cogynt’s AI capabilities and expand integration with additional data sources. Customers should anticipate continuous improvements and new features that align with the evolving landscape of real-time data analytics.

Overall, Cogility Cogynt stands out in the data stream processing platform sector for its robust real-time data processing, no-code authoring, and analytics capabilities to produce continuous intelligence. The platform’s flexible integration with diverse data sources and expert-based AI systems makes it a compelling choice for organizations looking to enhance their continuous intelligence capabilities and decision-making processes. While it is highly effective for large enterprises and specialized use cases, it is not designed for smaller organizations with limited resources and budgets. Cogynt is well-aligned with market needs and poised to deliver significant value in high-stakes environments.

7. Report Methodology

This GigaOm CxO Decision Brief analyzes a specific technology and related solution to provide executive decision-makers with the information they need to drive successful IT strategies that align with the business. The report is focused on large impact zones that are often overlooked in technical research, yielding enhanced insight and mitigating risk. We work closely with vendors to identify the value and benefits of specific solutions and to lay out best practices that enable organizations to drive a successful decision process.

8. About GigaOm

GigaOm provides technical, operational, and business advice for IT’s strategic digital enterprise and business initiatives. Enterprise business leaders, CIOs, and technology organizations partner with GigaOm for practical, actionable, strategic, and visionary advice for modernizing and transforming their business. GigaOm’s advice empowers enterprises to successfully compete in an increasingly complicated business atmosphere that requires a solid understanding of constantly changing customer demands.

GigaOm works directly with enterprises both inside and outside of the IT organization to apply proven research and methodologies designed to avoid pitfalls and roadblocks while balancing risk and innovation. Research methodologies include but are not limited to adoption and benchmarking surveys, use cases, interviews, ROI/TCO, market landscapes, strategic trends, and technical benchmarks. Our analysts possess 20+ years of experience advising a spectrum of clients from early adopters to mainstream enterprises.

GigaOm’s perspective is that of the unbiased enterprise practitioner. Through this perspective, GigaOm connects with engaged and loyal subscribers on a deep and meaningful level.

9. Copyright

© Knowingly, Inc. 2024 "CxO Decision Brief: Real-Time Data Processing and Analytics" is a trademark of Knowingly, Inc. For permission to reproduce this report, please contact