Business Intelligence Consulting: From KPI Dashboards to True Management Capability
Companies today have more data than ever before. Yet the central question often remains unanswered: What does this data actually tell us, and what decisions can be derived from it?
Business Intelligence answers these questions by linking company data to the underlying processes, rather than simply visualizing KPIs in a dashboard. It not only highlights when something is off track, but also explains why—and identifies the specific areas where you need to take action to correct these deviations. This transforms insights into data-driven decisions that can be implemented within the process and whose impact can be measured using the same data source.
What is Business Intelligence?
Business Intelligence (BI) refers to the ability to consolidate corporate data from various systems into a consistent database, make it visible in KPI dashboards, and use it to inform decisions. To achieve this, Business Intelligence goes beyond simple reporting: it combines data analysis, process mining, business process management (BPM), and no-code/low-code applications. The key question is always why something is happening—and which action will have the greatest impact.
What Business Intelligence Does – and How It Differs from Process Mining and Business Process Management
Business Intelligence answers the question of what happens within your internal business processes. To do this, it aggregates data from ERP, MES, CRM, and other systems into reliable metrics and provides end-to-end process transparency regarding the current state. This is the first step—but not the most important one. After all, transparency regarding numbers alone does not explain the causes behind them.
This is where process mining comes in: It reconstructs the actual process flow from the event data in your systems (process modeling) and identifies deviations in your processes (process monitoring).
Business Process Management (BPM) closes the loop: It translates the insights gained into formally defined, standardized, and continuously managed workflows and firmly embeds improvements within your organization.
How is process mining defined?
Process mining is a data-driven analytical technique that reconstructs the actual flow of a business process from event data (event logs) in operational systems such as ERP, MES, or CRM. Rather than relying on assumed target workflows, it draws on real data and reveals where a process deviates from its ideal state—for example, due to loops, wait times, or bottlenecks. Based on this data, effective improvements can then be identified.
Why Business Intelligence Is Key to Your Competitiveness
Currently, several market trends are reinforcing one another, making a robust data foundation a prerequisite for your ability to manage the business:
As diverse as these drivers may be, they all require the same thing: a reliable data foundation. This is exactly what we create with business intelligence, enabling data-driven decisions based on robust KPIs—that is, on numbers, data, and facts. This reduces the complexity of decision-making processes and leads to better and faster decisions. Because the same data foundation also reveals the impact of the measures taken, it is then possible to use data to verify whether a decision actually achieves the desired result. This advantage is crucial for your company’s long-term competitiveness, especially in volatile markets.
How to Tell If Your Data Set Is Inadequate
Manual reporting, data silos, and a lack of process transparency
In many manufacturing companies, the necessary data is generally available. However, it is scattered across various systems, in inconsistent formats, and cannot be used consistently. An inadequate data foundation can be identified by the following signs:
- Distributed Data Without a Common Basis
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Information is stored in separate systems (data silos); there is no consistent, shared database (single source of truth). Each department works with different figures.
- Manual reporting ties up significant resources
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Data is manually exported before each meeting, consolidated in Excel, and reformatted. There is hardly any time left for the actual management process—that is, deriving actions and decisions from the key metrics that have been identified.
- Lack of end-to-end process transparency
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If data is disjointed at system and departmental boundaries, it is not possible to have a comprehensive view of the process chain. It is then impossible to determine, based on data, where bottlenecks lie and which process steps increase lead time.
- Doubts About the Data Set
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Conflicting reports undermine confidence in key performance indicators and, as a result, the acceptance of data-driven decisions.
- Lack of a Foundation for Automation and AI
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Automation and AI initiatives require interconnected data that is available in real time (e.g., from machine connectivity). Without this foundation, the necessary AI readiness cannot be achieved.
Taken together, these signs rarely indicate isolated technical issues. Instead, they point to a data environment that is reaching its limits when it comes to today’s demands for control and speed.
This is exactly the problem we solve for you—with an approach that consistently combines transparency with root cause analysis and implementation.
The Ingenics Consulting Approach:
Here's How We Profitably Combine Business Intelligence, Process Mining, and BPM
We start where the economic leverage is greatest: with the business processes that carry the most operational and financial weight—order processing, production, supply chain, procurement, service, or administration. The advantage of this process-oriented approach is that every measure contributes to a specific business outcome, ensuring that investments flow to where they achieve the greatest measurable impact.
Our approach delivers results in these processes through the deliberate combination of three complementary disciplines: Business Intelligence shows what is happening, Process Mining explains why it is happening, and Business Process Management develops improvements based on this and embeds them permanently.
The BPM 4.0 maturity model we developed serves as a framework, mapping the current state of your business intelligence across process maturity, digitization, data utilization, automation, and data governance.
Following this assessment, we identify specific opportunities for improvement and automation and support their implementation end-to-end—in a vendor-neutral manner and tailored to the systems you already use.
The value of business intelligence is not determined by the number of KPI dashboards, but by a single question: Does knowing these metrics lead to better decisions—and to actions that make a difference?
Florian Christoph
Director Digital Solutions, Ingenics Consulting
From Power BI to Process Mining: Our Business Intelligence Consulting Services
Successfully implemented business intelligence consists of several interconnected areas—from connecting data sources to performing analyses with process mining tools to automated application in day-to-day operations. Ingenics Consulting covers this entire chain for you. Depending on your company’s level of maturity, we’ll start exactly where you can achieve the greatest impact.
It’s worth getting started: Companies that consistently leverage their data make more informed decisions, reduce process costs, and achieve predictable and measurable improvements in productivity, turnaround time, and quality. Exactly where you should begin depends on your current situation—we’ll work with you to identify the right starting point.
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Data Integration
We connect relevant data sources via an integration layer. A data integration layer such as iFlow connects operational systems—including ERP, CRM, WMS, and MES—to an analytics platform (e.g., Microsoft Fabric). This creates the foundation for a robust data set and consistent analyses.
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IT/OT Convergence
Through the machine data interface, we integrate plant technology and IT systems, thereby seamlessly connecting data, processes, and systems. This makes it possible to analyze plant data in conjunction with ERP and MES data—for example, for scrap and rework analyses or maintenance management.
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Reporting & KPI-Dashboards (Power BI)
Standardized dashboards—such as those in Power BI—replace recurring Excel reports. From KPI dashboards to management dashboards and executive dashboards, we consolidate all key performance indicators relevant to business management into a single, up-to-date overview and automate your reporting.
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Data Analytics & Data Strategy
With advanced and predictive analytics, we lay the foundation for forward-looking decisions. A robust data strategy and data governance ensure that the data foundation remains consistent, reliable, and AI-ready.
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Process Mining
Using the event data from your systems, we reconstruct the actual process flow, identify deviations and bottlenecks, and establish an objective basis for efficiency measures and process optimization.
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Automation Opportunities & RPA
Robotic Process Automation (RPA) and rule-based workflows handle manual intermediate steps such as data extraction and task assignment. This reduces the effort involved and minimizes sources of error in recurring processes.
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No-/Low-Code-Apps (Power Platform):
The Microsoft Power Platform enables the creation of business-specific apps for inventory management, audits, error tracking, and inspection planning. Through citizen development, we empower your business units to develop these simple applications on their own.
Business intelligence doesn't have to be a major project. Often, a single, well-chosen process is enough to produce visible results in just a few weeks. This initial success then helps spread awareness of the topic throughout the entire company.
Florian Christoph
Director Digital Solutions, Ingenics Consulting
Business Intelligence: Real-World Examples from Manufacturing
Process Mining in Supplier Management
(Tier 1 OEM supplier, automotive industry)
Key Performance Indicators (KPIs) Identified:
- 10.722 cases analyzed
- 297.187 activities evaluated
- 3.7 days average lead time
- Up to 29 repetitions per process step
Over a period of four months, the MES data from a production line was systematically analyzed. The result was a clear, data-driven foundation for efficiency measures, which included, among other things, the revision of error codes and a redesign of the rework process.
BPM 4.0 Transformation in Change Management
Automotive OEM
Processes and Optimizations:
- Approx. 14.500 change requests per year
- Automatic prediction of logistics cost relevance using self-learning AI
- Automatic assignment of contact persons via app
- Fully automated routing of change requests via RPA
- Automatic assignment of qualified planners via RPA
This example illustrates the range of applications for business intelligence within the context of a BPM 4.0 transformation. This is complemented by more than 20 Power BI dashboards from production—for example, for daily scrap and rework analysis, maintenance management, or structured root cause analysis of machine downtime.
Both examples have one key thing in common:
Only by establishing an end-to-end connection between data, root causes, and implementation can internal business processes be sustainably optimized.
Business Intelligence and Operations Strategy
Business Intelligence derives its direction from the overarching Operations Strategy. This strategy defines which processes are strategically relevant and what goals apply in terms of cost, quality, speed, and flexibility. Business Intelligence provides the data foundation for this, thereby enabling the control capabilities needed to achieve these objectives.
Simply put: Business Intelligence makes business processes measurable; Operations Strategy makes them effective.
Only the combination of both disciplines creates the foundation for data-driven decisions, optimizations, and continuous improvement. That is why we do not implement Business Intelligence as an isolated IT project in your company, but rather as an integral part of your operational management.
We establish the necessary procedural and system-level foundation through process and system harmonization, which ensures the sustainability of your Business Intelligence by aligning processes, systems, and data models.
Additional services related to system consolidation:
Strategy & Digital Transformation
Here, we define your company's digital vision. Business intelligence, in this context, is the ability to make and guide decisions based on data.
IT-Architecture & Application Landscape
Establish the technical foundation—with consistent interfaces and data flows—on which business intelligence can actually draw consistent data.
Operational Excellence
Translates insights derived from data into lasting gains in efficiency and quality.
FAQ - Frequently Asked Questions About Process and System Harmonization
What is the difference between business intelligence and process mining?
Business Intelligence aggregates data from multiple systems into key metrics and dashboards, showing what is actually happening. Process Mining uses event data from ERP, MES, or CRM systems to reconstruct the actual process as it unfolds and explains why and where deviations occur. In other words, Business Intelligence reveals that a lead time is too long—Process Mining explains why.
Where's the best place to start with business intelligence?
Ideally, start where the leverage is greatest: It makes sense, for example, to begin with a business-critical process such as order processing, production, the supply chain, or maintenance. The first step is to make this process transparent so that specific value levers can be identified. The data set grows step by step, and your team gains confidence in data-driven management before larger investments are required.
Is our current data quality sufficient for business intelligence and AI?
Generally speaking, yes. Concerns that one’s own data is of poor quality are rarely an obstacle, because data quality actually improves as we make process data visible. To this end, we prioritize the most important data fields and gradually improve the quality of their process data. This is especially true for AI: A reliable, interconnected database is the fundamental prerequisite for AI readiness.
Do we need a fully developed data warehouse or a comprehensive data strategy first?
No, focusing on a few business-critical processes and the data that’s truly necessary for them is actually far more effective to start with. On a smaller scale, we can identify the relevant value drivers in a short amount of time and then build the data foundation in a targeted manner where it offers the greatest benefit to your company. The overarching data architecture—for example, with a data integration layer like iFlow and an analytics platform like Microsoft Fabric—grows step by step in parallel.
How can we reduce the amount of manual reporting work?
Reporting can be automated at three levels:
- Data sources are connected via an integration layer, allowing key metrics to update automatically.
- Automated and standardized KPI dashboards (e.g., in Power BI) replace recurring Excel reports.
- No-code and low-code applications and automation tools, such as the Microsoft Power Platform, handle manual intermediate steps.
This not only reduces the effort required for data preparation but also frees up more time for analysis and management.
Is business intelligence relevant only to large corporations?
No, because medium-sized manufacturing companies in particular often suffer from Excel-heavy reporting and data silos, yet have fewer resources to address these issues. A pragmatic approach with a clear use case and quickly visible benefits therefore often has a greater impact in medium-sized companies than in large corporations.