Data Visualization for Decision Makers: From Complex Data to Controllable Insights

Today’s IT landscapes are composed of a multitude of distributed applications, databases, and interfaces. The central challenge lies in the automated processing, secure orchestration, and controlled provisioning of data. These data sets form the operational core of processes, applications, and control logic. It is essential that their processing is reproducible, scalable, and auditable. Automated workflows are therefore a foundational technical requirement of modern architectures.

Complexity in Integration Architectures: From Point-to-Point to Orchestrated Workflows

While simple system landscapes often rely on direct point-to-point communication, scalable environments demand centrally managed integration and automation mechanisms. Workflow engines like Apache Airflow, Camunda, or Logic Apps enable rule-based, versioned process definitions that not only map data flows but also ensure their validity, completeness, and sequence.

A typical automated workflow performs the following tasks:

• Triggered by events, such as new data in the data warehouse (central data platform for analytics and reporting) or changes in the CRM system
• Data validation and transformation
• Routing to various target systems
• Triggering visualizations and alerts
• Logging and audit trails for traceability

This creates deterministic data behavior – a crucial difference from manually maintained or ad-hoc integrated data flows. This shows that automation doesn’t stop at data integration – it paves the way toward a structured decision architecture.

Automation as a Decision Architecture

According to Gartner, by 2028, around 25% of CIOs and CDAOs will pursue a decision-centric approach. In this context, visualization functions as a control point within an automated decision architecture. Dashboards serve as interactive endpoints where data quality, completeness, and relevance are reviewed.

To support this, business intelligence integration must ensure that data from DWH, ERP, CRM, and external APIs is available in near real-time, models and visualizations are updated automatically, each metric is traceable to its source, and governance rules are enforced. Only then can a reliable decision layer be created to guide both operational and strategic actions.

Governance Through Automation

In distributed architectures, data governance is primarily a technical challenge. Without automation, reproducibility, logging, and secure versioning are missing. Workflow engines provide the foundation here: every transformation, routing, and KPI change is documented and validated. Combined with policy-as-code, role-based access control, zero-trust mechanisms, and quarantine logic for faulty data, this creates a system-level enforcement of governance – beyond organizational rules.

Architecture Meets Organization

Automation alone is not enough. For decision-making processes to be effectively operationalized, organizational structures must also adapt. This includes role-based dashboards for various management levels, BI systems as interactive layers between operational data and management, continuous monitoring in operations, and targeted development of data literacy. Only the combination of architecture and organization enables automated and explainable decisions.

Data Visualization and Workflows – The Basis for Reliable Decisions

In heterogeneous IT landscapes, automated workflows are essential to make data flows reproducible and controllable. This is the only way to ensure that information is delivered in a timely, valid, and traceable manner – independent of manual input. Without this automation, visualizations remain reactive, KPIs are vulnerable to manipulation, and decisions become uncertain.

Gartner predicts that by 2025, around 95% of all data-driven decisions will be partially or fully automated. For this transformation to succeed, data sources must be reliable, processes controllable, and visualizations actionable. The solutions we implement at CONVOTIS prove: workflow automation is the technical prerequisite for digital controllability and long-term competitiveness.