Know and Act in Real-Time
Transform continuous data streams into immediate insights and actions.
In an increasingly connected and dynamic world, the difference between success and failure can be a matter of seconds. Real-time Analytics enables companies to analyze data at the moment of its creation and react immediately – without the traditional delays of batch processing and data warehouse processes. With our customized real-time analysis solutions, you unlock hidden opportunities for process optimization, customer experience, and risk management.
Our Real-time Analytics services encompass the entire process from identifying relevant use cases through conception and implementation of technical architecture to integration into your business processes and continuous optimization. We support you in gaining real-time insights and enabling immediate actions.
4 service modules
Bookable individually or as an end-to-end programme.
Development and implementation of flexible stream processing architectures for continuous processing and analysis of data streams in real-time.
Development of intelligent systems for detecting complex event patterns in real-time data streams and triggering corresponding actions.
Implementation of real-time dashboards and operational control instruments that continuously provide current insights into your business-critical processes and KPIs.
Development of automated response mechanisms that trigger immediate actions based on real-time analyses and accelerate or fully automate decision-making processes.
5 phases
We follow a structured yet agile approach in developing and implementing Real-time Analytics solutions. Our methodology ensures that your real-time analysis systems are both technically powerful and business-valuable, and smoothly integrated into your operational processes.
Your contact
Asan Stefanski
Head of Digital Transformation
11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI
In today's digital economy, speed is a decisive competitive factor. Real-time Analytics enables companies to continuously monitor the pulse of their business and act immediately when it matters. However, the true added value only emerges when real-time insights are smoothly integrated into automated decision processes and operational workflows.
The key to success with Real-time Analytics lies in precisely defining the events and patterns that are actually relevant to your business. Avoid monitoring and processing all available data, and instead focus on critical indicators and thresholds. Companies that follow this focused approach achieve up to 4 times higher ROI while simultaneously reducing technical complexity and costs.
5 QUESTIONS, BRIEFLY ANSWERED
Real-time analytics processes data within milliseconds to seconds of creation. Traditional BI works with batch processes that load data hourly or daily. The critical difference is latency: real-time data analysis uses stream processing (e.g. Apache Kafka, Flink), while BI relies on ETL pipelines with data warehouses. For operational decisions like fraud detection or machine monitoring, real-time processing is essential.
The most common technologies are Apache Kafka for event streaming, Apache Flink and Spark Structured Streaming for processing, and ClickHouse or Apache Druid for real-time queries. Cloud platforms offer managed services like AWS Kinesis, Azure Event Hubs, and Google Pub/Sub. Architecture choice depends on data volume, latency requirements, and existing infrastructure.
Financial services use real-time data analysis for fraud detection and risk monitoring. Manufacturing enables predictive maintenance and quality control. Retail uses real-time analytics for pricing optimization and inventory management. Logistics companies monitor supply chains in real time. In IoT, sensor data is continuously analyzed for anomaly detection.
Costs vary by scope and complexity. A proof of concept with one use case typically starts in the range of EUR 20,000 to 50,000. A production-ready streaming platform with multiple data sources and real-time dashboards ranges between EUR 100,000 and 300,000. Managed cloud services reduce operational overhead but incur ongoing usage costs. ADVISORI recommends a phased approach: start with one concrete use case and scale incrementally.
Integration is achieved through event streaming platforms like Apache Kafka, which serve as a central data hub. Existing databases, ERP systems, and APIs are connected via connectors. Change Data Capture (CDC) enables real-time replication from relational databases. The streaming layer complements existing data warehouses without replacing them. ADVISORI takes a phased integration approach that accounts for existing infrastructure.
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