From KPI Dashboards to Real Performance Control with Business Intelligence Consulting
Companies today have more data than ever before. Yet one key question often remains unanswered: What does this data tell us, and what decisions can we make based on it?
Business intelligence answers these questions by connecting company data to the processes behind it, rather than simply visualizing KPIs in a dashboard. It shows not only that something is off, but why, and where exactly you need to fix it. This turns insights into data-driven decisions that can be implemented and measured using the same data foundation to determine whether they deliver the intended results.
What is Business Intelligence?
Business intelligence (BI) is the ability to bring company data from different systems together into one consistent foundation, make it visible in KPI dashboards, and put it to use for decisions. Business intelligence goes beyond plain reporting. It combines data analytics, process mining, business process management (business process management, BPM), and no-code/low-code applications. The question that matters most is why something is happening and which action will have the greatest impact.
What Business Intelligence Delivers, and How It Differs from Process Mining and Business Process Management
Business intelligence answers the question of what is happening inside your company's processes. It condenses data from ERP, MES, CRM, and other systems into reliable KPIs and creates end-to-end process transparency into the current state. That is an important first step, but the numbers alone do not explain why the results are occurring.
This is where process mining comes in. It reconstructs how your processes run from your systems' event data (process modeling) and surfaces the deviations within them (process monitoring).
Business Process Management (BPM) closes the loop. It turns these insights into binding, standardized workflows under continuous control, and locks the improvements firmly into your organization.
How Is Process Mining Defined?
Process mining is a data-driven analysis technique that reconstructs how a business process actually runs, using the event data (event logs) from operational systems such as ERP, MES, or CRM. Instead of relying on assumed target workflows, it works from real data and shows exactly where a process deviates from its ideal, whether through loops, wait times, or bottlenecks. This data then forms the basis for effective improvements.
Why Business Intelligence Matters for Your Competitiveness
Several market trends are reinforcing one another, making a reliable data foundation essential for effectively managing your business.
KPIs, in other words, in hard numbers and facts
While these drivers may differ, they all point to the same need: a reliable data foundation. Business intelligence provides this foundation, enabling data-driven decisions based on clearly defined KPIs and reliable facts. This reduces complexity in decision-making and leads to better, faster decisions. Because the same data foundation also reveals the impact of each decision, you can use data to determine whether it delivered the intended result. In volatile markets especially, this is an advantage that determines your company's long-term competitiveness.
How to Recognize
an Insufficient Data Foundation: Manual Reporting, Data Silos, and Missing Process Transparency
In many manufacturing companies, the necessary data already exists. The challenge is that it is often scattered across different systems, stored in inconsistent formats, and difficult to use end to end. Here is how to recognize an insufficient data foundation.
- Scattered Data with No Shared Foundation
-
Information sits in separate systems (data silos), and there is no consistent, shared data foundation (single source of truth). Every department is working from different numbers.
- Manual Reporting Ties Up Significant Capacity
-
Before every meeting, data gets exported by hand, consolidated in Excel, and reformatted from scratch. This leaves minimal time for what matters, turning figures into decisions and action.
- Missing End-to-End Process Transparency
-
When data breaks off at system and department boundaries, you lose any end-to-end view of the process chain. This makes it difficult to use data to pinpoint bottlenecks or identify which process steps are driving longer lead times.
- Lack of Trust in the Data
-
Conflicting reports reduce confidence in the numbers and, as a result, in data-driven decisions.
- No Foundation for Automation and AI
-
Automation and AI initiatives need connected data available in real time, for example from machine integration. Without this foundation, the necessary level of AI readiness cannot be achieved.
These signs are rarely isolated technical issues. Instead, they indicate that the existing data landscape is reaching its limits in meeting today’s demands for control and speed.
This is where our approach comes in, connecting transparency, root-cause analysis, and implementation in a consistent way.
The Ingenics Consulting Approach:
Combining Business Intelligence, Process Mining, and BPM for Real Impact
We start where the greatest impact can be achieved, focusing on the business processes with the most operational and financial significance, whether in order processing, production, supply chain, purchasing, service, or administration. This process-driven approach has a clear advantage. Every measure is tied to a specific business outcome, ensuring investments are focused where they can deliver the greatest measurable impact.
Within these processes, our approach works by combining three complementary disciplines. Business intelligence shows what is happening, process mining explains why it is happening, and business process management translates those insights into improvements that can be sustained over time.
Our BPM 4.0 maturity model provides the framework for assessing your business intelligence maturity across key dimensions, including process maturity, digitalization, data utilization, automation, and data governance.
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.
Companies that put their data to consistent use make smarter decisions, cut process costs, and turn progress in productivity, lead time, and quality into something plannable and measurable. Where to start depends on your current position, and we work with you to identify the right starting point.
-
Data Integration
We connect your relevant data sources through an integration layer. A data integration layer such as iFlow links operational systems like ERP, CRM, WMS, and MES to an analytics platform, for example Microsoft Fabric. That creates the foundation for a reliable database and consistent analysis.
-
IT/OT Convergence
Through machine data connectivity, we link operational technology with IT systems, networking data, processes, and systems seamlessly. That makes equipment data analyzable alongside ERP and MES data, for example for scrap and rework analysis or maintenance control.
-
Reporting & KPI Dashboards (Power BI)
Standardized dashboards, for example in Power BI, replace recurring Excel reports. From KPI and management dashboards to the executive dashboard, we bring the metrics that matter for managing your business into one up-to-date view and automate reporting.
-
Data Analytics & Data Strategy
With advanced and predictive analytics, we establish the groundwork for forward-looking decisions. A solid data strategy and data governance keep your data foundation consistent, reliable, and AI-ready (AI readiness).
-
Process Mining
From your systems' event data, we reconstruct how your processes run, uncover deviations and bottlenecks, and build an objective basis for efficiency measures and process optimization.
-
Automation Potential & RPA
Robotic process automation (RPA) and rule-based workflows take over manual steps such as extracting data and assigning tasks. This reduces effort and sources of error out of recurring workflows.
-
No-/Low-Code-Apps (Power Platform):
The Microsoft Power Platform allows us to build practical apps for warehouse management, audits, defect logging, or inspection planning. Through citizen development, we enable your business units to build these simple applications themselves.
Business Intelligence: Real-World Examples from Production
Reference Project 1: Process Mining in Supplier Management
(OEM Tier 1 Supplier, Automotive Industry)
KPIs Identified:
- 10,722 cases analyzed
- 297,187 activities evaluated
- 3.7 days average lead time
- Up to 29 repetitions per process step
Over four months, we systematically analyzed the MES data from one production line. The result was a clear, data-driven basis for efficiency measures, including a revision of error codes and a redesign of the rework process.
Reference Project 2: BPM 4.0 Transformation in Change Management
Industrial Sector
Processes and Optimizations:
- Approx. 14,500 change requests per year
- Automatic prediction of logistics cost relevance through self-learning AI
- Automatic assignment of contacts via app
- Fully automated routing of change requests through RPA
- Automatic assignment of qualified planners through RPA
This example shows just how broadly business intelligence can be applied within a BPM 4.0 transformation. It is complemented by more than 20 Power BI dashboards from production, covering things like day-by-day scrap and rework analysis, maintenance control, and structured root-cause analysis of machine downtime.
One Thing Both Examples Have in Common
Only the end-to-end connection between data, root causes, and implementation makes lasting optimization of your internal processes possible.
Business Intelligence and Operations Strategy
Business intelligence takes its direction from the overarching operations strategy. That strategy defines which processes matter strategically and what targets apply for cost, quality, speed, and flexibility. Business intelligence provides the data foundation for this, giving you the control needed to achieve those targets.
Simply put, business intelligence makes company processes measurable, while operations strategy enables them to perform at their best.
Combining both disciplines creates the foundation for data-driven decision-making, optimization, and continuous improvement. That is why we do not approach business intelligence as an isolated IT project, but as an integral part of how you manage your operations.
We build the process and system foundation you need through process and system harmonization, giving your business intelligence a solid base of aligned processes, systems, and data models.
Business Intelligence in the Context of Other Services:
Digital Transformation Strategy and Target Picture
This is where we define your company's digital target picture. Business intelligence is the ability, within it, to make and steer decisions based on data.
IT-Architecture and Application Landscape
This builds the technical foundation, with end-to-end interfaces and data flows, that business intelligence needs to draw on consistent data in the first place.
Operational Excellence
This turns the insight gained from data into lasting efficiency and quality gains.
FAQ - Frequently Asked Questions About Process and System Harmonization
What is the difference between business intelligence and process mining?
Business Intelligence pulls together data from multiple systems into KPIs and dashboards and shows what is happening in the current state. Process mining reconstructs how a process actually runs from the event data in ERP, MES, or CRM systems, and explains why and where it is deviating. In short, Business Intelligence shows you that a lead time is too long, and process mining shows you why.
Where is the best place to start with business intelligence?
The best place is wherever the leverage is greatest. A good starting point is often a business-critical process such as order processing, production, supply chain, or maintenance. The first step is making that process transparent so you can identify concrete value levers. From there, your data foundation grows step by step, and your team builds confidence in data-based steering before you commit to bigger investments.
Is our current data quality good enough for business intelligence and ai?
Usually, yes. The worry that your own data is too poor rarely turns out to be a real obstacle, because data quality actually improves once we make your process data visible. We prioritize the data fields that matter most and improve the quality of your process data step by step. This matters especially for AI. A reliable, connected data foundation is the basic requirement for AI readiness.
Do we need a complete data warehouse or a full data strategy first?
No, focusing on a handful of business-critical processes and the data you genuinely need for them gets you there much faster in the beginning. Working at a smaller scale, we can identify the relevant value levers quickly and then build out your data foundation exactly where it delivers the most value for your company. The broader data architecture, for example a data integration layer like iFlow paired with an analytics platform like Microsoft Fabric, grows alongside it step by step.
How do we reduce the effort spent on manual reporting?
Reporting can be automated on three levels.
- Data sources are connected through an integration layer, so KPIs update themselves automatically.
- Automated, standardized KPI dashboards (e.g., in Power BI) replace recurring Excel reports.
- No-code/low-code applications and automation tools such as the Microsoft Power Platform take over manual intermediate steps.
That does not just cut the effort spent preparing data, it also frees up more time for analysis and steering.
Is business intelligence only relevant for large corporations?
No, quite the opposite. Mid-sized manufacturing companies often suffer especially badly from Excel-heavy reporting and data silos, yet have fewer resources to fix it. That is exactly why a pragmatic start, with a clear use case and quickly visible benefits, often has more impact in mid-sized companies than in large corporations.