The goal of analytics is to extract valuable insights from large volumes of data, empowering businesses with smarter decision-making and automation. It has applications across industries and domains—from predictive maintenance to fraud detection, from optimising decision-making processes to intelligence-driven investigations.
Since every organisation has its own data structure and industry-specific challenges, analytical solutions are often complex and highly customised. These projects aim to seamlessly integrate data-driven strategies into business operations, turning raw information into actionable intelligence.
The Lifecycle of an Analytics Project
- Understanding Data & Business Context: Every analytics project begins with gaining a deep understanding of the data and business objectives. This phase defines the project’s goals and establishes the business case. Depending on the complexity, a Proof of Concept (PoC) may be introduced—a small-scale test project designed to validate the feasibility of the solution.
- Data Integration & Management: Successful analytics projects rely on well-prepared, high-quality data. This phase involves collecting, integrating, and cleansing data to ensure accuracy and reliability. Key aspects include quality control, data loading automation, and real-time or batch-based data processing.
- Data Discovery & Feature Engineering: Analysts conduct exploratory data analysis to identify key characteristics and patterns within the data. During the Feature Engineering phase, new, valuable data attributes are derived from raw information, forming the foundation for business models.
- Modeling & Business Automation: Using the data models developed in the project, businesses can build decision-support systems. These models may include forecasting models, which use historical data to predict future trends and events, enabling proactive decision-making.
- Implementation & Business Integration: The final step is embedding the models and insights into business operations. This ensures that analytics-driven insights directly support operational decision-making and automated workflows, maximising efficiency and impact.
Additional elements of analytics projects may include data visualisation, dashboard creation, network analysis, decision-support automation, and case management—particularly in fraud detection solutions.
By applying an agile approach, we actively involve clients in the project, as their domain expertise plays a key role in achieving more accurate and effective results.
Successful projects rely on close collaboration with the client, as they have the deepest understanding of their company, industry, and data—making their involvement essential throughout the process.
Key Types of Analytical Solutions
- Predictive Maintenance: Utilising IoT data to predict machinery failures, enabling timely intervention and cost reduction. These analytics systems are most commonly used in manufacturing and industrial sectors, where equipment downtime has significant financial and operational impacts.
- Fraud Detection and Investigation: Designed to identify various types of fraud, such as banking, insurance, and procurement fraud. These systems are crucial for financial institutions and insurance companies, where large-scale data analysis, network modelling, and risk assessment help detect high-risk customers and transactions.
- Business Intelligence: Covering everything from data warehousing to reporting and dashboard creation, BI solutions help businesses analyse and track performance. BI systems are often enhanced with decision-support tools and predictive models (e.g., revenue forecasting), making them particularly valuable for large retail enterprises.
- Intelligence and Investigation Management: Systems designed for organisations conducting investigations (e.g., police), aimed at enhancing public safety and improving the efficiency of law enforcement processes. Data-analysing models help in crime detection and risk minimisation.
The success of analytics projects relies on close collaboration with the client, as domain expertise is essential for designing tailored solutions.
Analytics is deeply interconnected with software development, as processing data and integrating models often requires developing interfaces and performing various programming tasks.
The implementation of analytical solutions and the maintenance of necessary development environments are integral to the operation of complex, data-driven business systems.
What Are the Core Areas of Analytics at Quadron?
At Quadron, analytics is structured around the following core areas:
Intelligence & Investigation Management, IoT, Fraud Detection & Investigation, Forecasting & Decision Automation, Predictive Maintenance, and Business Intelligence. These pillars form the foundation of our analytics approach.
The essence of analytics and artificial intelligence (AI) is automation—taking over tasks that once required human intervention. For instance, in bank loan approval processes, AI can now automate decision-making, removing the need for constant human supervision.
The real value of analytical solutions lies in their ability to extract meaningful, business-critical insights from vast amounts of data and build automated processes around them. While the specific questions analytics must address may vary by industry and use case, the common goal remains the same: leveraging data to deliver impactful, business-driven results.
Analytical systems enable businesses to ask broad, complex questions based on data, particularly in intricate environments. The ability to collect and analyse data at such depth allows these systems to go beyond predicting failures, offering insights into other quality-related issues as well.
Unlike “off-the-shelf” IT solutions such as firewalls or basic IT services, analytics and application development projects involve a higher level of complexity. This results in longer sales cycles and in-depth planning. Developing a customised solution concept can take months, as each system must be tailored to specific business objectives, data requirements, and integration needs.
These projects demand significant investment, but they create long-term value by enhancing decision-making, optimising processes, and driving business growth.
In every case, we analyse vast amounts of data, powered by robust technology and infrastructure. Deploying such systems requires considerable investment—a single server can cost tens of millions of forints, while the necessary software can reach hundreds of millions.
Beyond the financial investment, implementation is a complex process, making these large-scale, high-impact projects that demand careful planning, execution, and integration.
Analytics Subfields
Intelligence & Investigation Management
In the field of Intelligence & Investigation Management, the development of Intelligence Use Cases is currently the most resource-intensive focus. Our goal is to enhance public safety, improve Hungary’s international standing, detect risks and threats, and identify criminal incidents and suspicious activities. Additionally, increasing the efficiency of investigative processes is a key objective.
Our solutions encompass relationship network analysis, entity extraction and resolution, analytical model development, case management, data visualisation, and automated reporting. These capabilities are applied in intelligence, counterterrorism, law enforcement, and border control, where large volumes of unstructured data are processed to extract relevant insights, build networks, trigger alerts, and generate interactive dashboards.
The core mission of Intelligence & Investigation Management is to reduce risks affecting national security and law enforcement. These systems enable the detection of crimes that traditional investigative methods might overlook, thereby preventing significant damage and threats.
Although the impact of such solutions may not always be easily quantified—unlike fraud losses in the insurance sector—Intelligence & Investigation Management systems play a crucial role in strengthening public safety and mitigating risks, ultimately safeguarding societal and economic interests in the long run.
Business Intelligence
Business Intelligence encompasses key tasks such as data warehousing, data integration, data transformation, data management, reporting, dashboard creation, and data preparation. These processes ensure that available data is efficiently collected, processed, and visualised, enhancing decision-making and supporting business intelligence services.
This field is highly versatile, working with a wide range of data types, including sales, lending, marketing, HR, and manufacturing data. Traditional Business Intelligence focuses on analysing past and present data, while Advanced Analytics takes a forward-looking approach, predicting future trends and changes.
Artificial intelligence is an integral part of advanced analytics. AI enables automation in areas previously reliant on human decision-making, such as credit approval processes. By accelerating and optimising decision-making, AI has a significant impact on business operations. This is why it is essential to present AI as part of the broader analytics framework.
IoT, Predictive Maintenance
IoT focuses on the collection, analysis, and monitoring of data, the remote supervision and control of machines and devices.This field involves handling large volumes of sensor data and big data, which we analyse using advanced analytics tools.
Predictive Maintenance, built on IoT-generated data, is designed to predict equipment failures and their expected timing. This helps clients transition from reactive to predictive maintenance, leading to long-term cost reductions.
While IoT encompasses data integration, analysis, and monitoring, Predictive Maintenance is a specialised advanced analytics solution based on IoT. Although closely related, Predictive Maintenance has distinct benefits and functionalities that go beyond standard IoT services.
Predictive Maintenance analyses IoT data to anticipate when and where equipment failures will occur, enabling proactive intervention before breakdowns happen. The goal is to shift clients away from reactive maintenance, where repairs are made only after failures, to predictive strategies that minimise unexpected downtime. This approach helps businesses reduce long-term maintenance and equipment replacement costs, preventing unplanned outages and costly repairs.
The primary clients for Predictive Maintenance are industrial and manufacturing companies that operate high-value equipment, such as pumps and turbines. For example, in an oil refinery, an unexpected pump failure can result in significant financial losses per hour. Predictive insights prevent such disruptions, ensuring continuous operations and substantial cost savings.
Fraud Detection and Investigation
Fraud Detection and Investigation is similar to Intelligence and Investigation Management, but here the focus is on identifying various types of fraud. In lending, for example, we detect application fraud, in insurance we identify claims fraud, and in procurement, we uncover procurement fraud—among many other fraud types. The tools used include relationship network analysis, modelling, and advanced analytics techniques.
These solutions aim to reduce financial losses caused by fraud, generating significant cost savings. For example, if an insurance company experiences an annual fraud-related loss of 1.2 billion HUF, implementing a fraud detection system costing 400 million HUF could lead to a full return on investment within the first year, as the system can identify fraudulent activities and automate parts of the investigation process.
The primary clients for fraud detection solutions are financial institutions, where fraud prevention is mission-critical, making these systems a high-value investment for the industry.
Forecasting and Decision Automation
Forecasting is designed to predict future trends and events based on available data, such as revenue forecasting. This involves time-series data analysis, helping businesses understand how key metrics will evolve over time and how to strategically plan for the future based on data-driven insights.
Decision Automation focuses on automating decisions that were previously made by humans. This essentially means applying artificial intelligence to tasks where past experience and historical data can be leveraged to make automated, fast, and efficient decisions—enhancing productivity, accuracy, and scalability.
Application Development
Application Development covers the execution of custom development projects, as well as identifying and resolving development issues. Each project is supported by specialised teams, composed of experts tailored to the specific field and requirements.
Additionally, we provide insight into our technology stack, ensuring that we use the most suitable tools and frameworks to deliver high-performance, scalable, and efficient applications.
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Whether you need advanced analytics, AI-driven decision automation, or custom application development, Quadron has the expertise to turn your data into actionable insights. Let’s build the future together.