Industry: Automotive Retail
Size: Multi-location dealership group (20+ locations)
Department: Customer Experience & Service Operations
Stakeholders: Customer Experience Manager, Service Directors, Dealership General Managers

The Challenge

Fragmented data was hiding critical customer experience issues.

The dealership group had invested in automating their customer satisfaction survey process through SurveyMonkey, sending post-sale and post-service surveys to thousands of customers monthly. But despite the automation, the data remained virtually unusable.

The core problems:

1. No Consolidated View

Customer satisfaction data was siloed across individual survey containers—one for each dealership location. To understand overall performance, managers had to manually log into 10+ separate containers, export data, and attempt to piece together a coherent picture in Excel.

2. Inability to Identify At-Risk Customers

When customers left negative feedback (detractor NPS scores), there was no efficient way to:

• Identify which specific customer was unhappy
• Connect their feedback to the actual transaction (invoice number, vehicle, service appointment)
• Understand which part of the experience failed them

The data existed, but extracting it required tedious manual work that rarely happened in time to recover the relationship.

3. No Accountability or Pattern Recognition

Leadership had no visibility into:

• Which sales advisors or service consultants were consistently creating poor experiences
• Which process steps (financing, delivery, service check-in, etc.) were friction points
• How individual locations compared to each other

The Business Impact:

• Reactive (not proactive) customer recovery: By the time issues were identified, customers had already left negative reviews or switched dealerships
• Wasted administrative time: Customer experience managers spent 5-8 hours/week manually compiling reports
• Missed coaching opportunities: Sales and service leaders couldn’t identify training needs or performance issues until quarterly reviews
• Inconsistent service quality: No data-driven way to replicate what high-performing advisors were doing right

The Solution

A unified customer experience intelligence system.

We built a Power BI solution that automated the entire customer satisfaction analytics process and transformed how the dealership group understood and responded to customer feedback.

What We Built:

1. Automated Data Integration

• Connected Power BI directly to SurveyMonkey’s API
• Pulled responses from all dealership survey containers into a single data model
• Linked survey responses to transaction data (invoice numbers, VINs, service RO numbers) from the dealership management system
• Automated daily refresh to ensure data was always current

2. Customer-Centric Dashboard

Created a primary view focused on the most critical question: “Which customers need attention right now?”

The dashboard automatically surfaced:

• All detractor responses (NPS 0-6) from the past 30 days
• Customer name, contact info, and associated transaction details
• Specific feedback text highlighting what went wrong
• Assignment to responsible advisor/consultant for follow-up

3. Root Cause Analysis Views

Built drill-down capabilities to identify patterns:

• By Process Stage: Which step in the sale/service journey was causing issues? (delivery, financing, service wait time, vehicle quality, etc.)
• By Individual: Performance scorecards for each sales advisor and service consultant showing their NPS distribution and detractor rate
• By Location: Comparative views showing which dealerships were excelling vs. struggling
• By Time: Trend analysis to spot emerging issues before they became systemic

4. Self-Service Insights

Designed visualizations with decision-oriented titles that eliminated guesswork. For example:

• Instead of: “NPS Score by Category”
• Table showed: “Service Wait Time Is Our #1 Detractor Driver (42% of Negative Feedback)”

Stakeholders could glance at a chart and immediately understand the insight without needing to interpret the data themselves.

The Results

From reactive crisis management to proactive experience design.

Immediate Operational Wins:

• 5-8 hours/week saved per customer experience manager (eliminated manual report compilation)
• Same-day customer recovery enabled—detractors identified and contacted within 24 hours instead of weeks
• Clear accountability for advisors—performance coaching shifted from subjective to data-driven

Strategic Business Impact:

• Improved NPS by 12 points in the first 6 months by systematically addressing the top 3 friction points identified in the data
• Reduced customer churn in service department—repeat visit rate increased as service issues were caught and resolved faster
• Replicated best practices—high-performing advisors’ approaches were identified and trained across the network

Cultural Shift:

The solution changed how the organization thought about customer feedback:

• From: “We send surveys because we’re supposed to”
• To: “Customer feedback is our early warning system and competitive advantage”

Dealership managers began proactively checking the dashboard daily instead of waiting for monthly reports. Service directors started weekly coaching sessions using individual advisor scorecards. The executive team gained confidence that customer experience was being actively managed, not just measured.

Key Takeaways

1. Automation Without Insight Is Just Faster Noise
   The survey process was already automated, but the data was still unusable. Real value came from making insights accessible and actionable.
2. Context Transforms Data Into Decisions
   Linking survey responses to transaction details (who, what, when) turned vague feedback into specific, addressable problems.
3. Visualization Design Determines Adoption
   Self-explanatory charts with clear, decision-oriented titles meant stakeholders could act without needing a data analyst to interpret for them.
4. Accountability Requires Transparency
   Individual performance visibility (when done constructively) drove dramatic improvements because people could see their impact and take ownership.

What This Means for Your Business

If your organization is:

• Collecting customer feedback but struggling to act on it
• Logging into multiple systems to understand a single question
• Making customer experience decisions based on gut feel rather than data
• Unable to identify which people or processes are creating friction

…you’re likely leaving both revenue and reputation on the table.

The right BI solution doesn’t just report what happened—it tells you why it happened and who was involved.

Industry: Automotive Retail
Size: Multi-location dealership group (20+ locations)
Department: Finance, Operations, Inventory Management
Stakeholders: CFO, Financial Controllers, Dealership General Managers, Inventory Managers

The Challenge

Millions in capital tied up – with no clear picture of why

The dealership group was carrying significant vehicle inventory across its network—representing millions of euros in capital investment. Yet finance and operations leaders had no efficient way to answer fundamental questions that directly impacted cash flow and profitability.

The core problems:

1. No Real-Time Inventory Visibility

To understand current stock levels, inventory managers had to:

• Manually check each dealership’s local system
• Export data to Excel and compile it themselves
• Repeat this process daily if they wanted up-to-date information

There was no centralized view showing “How many vehicles do we have right now, and what are they worth?” across the entire network.

2. Zero Historical Context

Beyond the current snapshot, there was no systematic tracking of:

• How inventory levels changed over time
• Seasonal patterns or trends
• How quickly vehicles were turning (days on lot)
• Whether inventory was growing or shrinking

Leadership was making purchasing and pricing decisions without understanding if current inventory levels were normal, concerning, or optimal.

3. Trapped Capital, Hidden Costs

Without visibility into inventory performance:

• Finance couldn’t accurately forecast cash flow needs
• Excess inventory tied up capital that could be deployed elsewhere
• Slow-moving units accumulated holding costs (financing interest, depreciation)
• No way to identify which vehicle types or locations were underperforming

The Business Impact:

• Inefficient capital allocation: Dealerships were over-stocked in some segments, under-stocked in others—but leadership had no data to rebalance
• Delayed decision-making: By the time monthly reports were compiled, market conditions had already shifted
• Wasted management time: Finance controllers spent 3-5 hours/week manually building inventory reports
• Missed optimization opportunities: No way to identify best practices from high-performing locations and replicate them across the network

The Solution

A live inventory intelligence dashboard

We built a Power BI solution that transformed vehicle inventory from a black box into a strategic asset—giving stakeholders real-time visibility and historical context to optimize capital deployment.

What We Built:

1. Automated, Centralized Data Integration

• Connected Power BI to the dealership management system’s inventory database
• Built automated daily refresh to capture current stock levels across all locations
• Calculated real-time metrics:
  • Total units on hand (by location, make, model, type)
  • Total capital tied up in inventory
  • Average days on lot per vehicle
  • Inventory turnover rates

2. Current State Dashboard

Created a primary view answering: “Where do we stand right now?”

The dashboard showed:

• Network-wide inventory summary: Total units, total value, breakdown by location and vehicle type
• Capital deployment view: How much cash is tied up in inventory, with drill-down by dealership
• Aging analysis: Which vehicles have been on the lot 30/60/90+ days (slow movers requiring action)
• Stock level alerts: Automated flagging of locations with unusually high or low inventory vs. historical norms

3. Historical Trend Analysis

Built time-series views to show:

• Inventory levels over time: Daily/weekly/monthly trends to identify patterns and seasonality
• Turnover performance: How quickly vehicles were selling compared to prior periods
• Year-over-year comparison: Current inventory levels vs. same period last year
• Location benchmarking: Which dealerships were managing inventory efficiently vs. struggling

This gave leadership the context to answer: “Is this normal? Are we improving? Where should we focus?”

4. Decision-Oriented Visualizations

Designed every chart to eliminate interpretation work. For example:

Instead of a generic line chart titled “Inventory Over Time,” we created:

• “Inventory Has Increased 18% vs. Last Quarter—Capital Tied Up Is at All-Time High”

Instead of a table showing days-on-lot by vehicle:

• “23 Units Over 90 Days—$1.2M in Slow-Moving Inventory Requiring Action”

Stakeholders could glance at a chart title and immediately know if action was needed – without having to analyze the underlying data themselves.

The Results

From reactive inventory management to proactive capital optimization

Immediate Operational Wins:

• 3-5 hours/week saved per finance controller (eliminated manual report compilation)
• Daily visibility into inventory and capital deployment (vs. monthly lag)
• Instant identification of slow-moving units requiring price adjustments or transfers

Strategic Business Impact:

• Reduced average inventory holding by 12% in first 6 months by identifying and clearing slow movers systematically
• Freed up $2.3M in working capital that was redeployed to higher-margin opportunities
• Improved inventory turnover rate by 15%—vehicles selling faster, reducing holding costs
• Eliminated stockouts in high-demand segments by using trend data to inform purchasing decisions

Financial Impact:

• Reduced floor plan interest costs by carrying less excess inventory
• Minimized depreciation losses by identifying aging units before they became write-downs
• Optimized cash flow forecasting – finance team could predict capital needs with 2-3 week lead time instead of guessing

Cultural Shift:

The solution changed how the organization approached inventory:

• From: “Inventory is an operational issue for dealerships to manage”
• To: “Inventory is a strategic capital allocation decision requiring network-wide coordination”

Dealership GMs began using the dashboard to proactively request vehicle transfers from overstocked locations. The CFO gained confidence in cash flow projections because inventory trends were visible and predictable. Operations leaders started weekly inventory reviews using the data to optimize purchasing and pricing strategies.

Key Takeaways

1. Real-Time Data Enables Proactive Management
   Monthly reports told leadership what had happened. Daily dashboards let them intervene before problems became expensive.
2. Historical Context Turns Numbers Into Strategy
   Knowing “we have 250 vehicles in stock” is useless. Knowing “we have 250 vehicles—up 18% from last quarter and 23 have been here 90+ days” drives action.
3. Centralized Visibility Reveals Network-Wide Optimization Opportunities
   When each dealership only sees their own data, excess inventory at Location A and stockouts at Location B both persist. A network view enables rebalancing.
4. Capital Efficiency Is a Competitive Advantage
   Every dollar tied up in excess inventory is a dollar not available for growth, marketing, or higher-margin opportunities. Better inventory management directly impacts profitability.

What This Means for Your Business

If your organization is:

• Carrying significant inventory without real-time visibility into stock levels or capital tied up
• Making purchasing decisions based on gut feel or outdated monthly reports
• Unable to identify slow-moving inventory until it’s already a problem
• Managing inventory location-by-location without network-wide coordination

…you’re likely over-investing in inventory and under-utilizing your capital.

The right BI solution doesn’t just show you what inventory you have – it shows you how it’s performing and where optimization opportunities exist.

Industry: Automotive Retail
Size: Multi-location dealership group (20+ locations)
Department: Executive Leadership, Finance, Operations
Stakeholders: CEO, CFO, Business Unit Directors, Financial Controllers

The Challenge

Leadership was flying blind between monthly board meetings.

The executive team was managing a complex, multi-location business unit spanning over multiple countries—but had no real-time visibility into how the business was actually performing. Strategic decisions were being made in the dark, with weeks of delay between events and awareness.

The core problems:

1. No Centralized Source of Truth
Business performance data was scattered across three completely separate systems:

• MIS (Management Integration System): Operational data (vehicles sold, service hours, customer transactions)
• IFRS accounting system: Financial data (revenue, COGS, OPEX, margins)
• Excel files on SharePoint: Budget forecasts, market data, and ad-hoc analysis

To answer a simple question like “Are we on track to hit our Q2 targets?” required:

• Manually extracting data from each system
• Copying/pasting into Excel
• Reconciling inconsistencies
• Building custom calculations
• Distributing the report via email

This process took 2-3 days and was only done monthly—meaning leadership was always looking at 4-6 week old information.

2. Zero “Live” Performance Feedback
Between monthly board meetings, executives had no way to know:

• Whether revenue was tracking to plan
• If margins were deteriorating
• How individual locations were performing
• Whether interventions were working

Questions like “How did we do yesterday?” or “Are we having a good month so far?” had no answer. Leadership was reactive, not proactive—learning about problems only after they’d compounded for weeks.

3. Inability to Compare and Contextualize
Even when monthly reports were compiled:

• No year-over-year trend analysis
• No comparison to budget or forecast
• No moving averages to smooth out seasonality
• No ability to drill down from business unit → country → location → department

Strategic planning happened in a vacuum, without historical context or performance benchmarking.

The Business Impact:

• Delayed decision-making: By the time leadership identified a problem, the month was over and corrective action had to wait
• Missed revenue opportunities: Strong performance in one area couldn’t be replicated elsewhere because patterns weren’t visible
• Inefficient resource allocation: Budget and staffing decisions were made based on outdated or incomplete information
• Executive frustration: Leadership meetings devolved into “wait for the finance report” instead of strategic discussions
• Wasted finance team capacity: Controllers spent 2-3 days/month compiling reports instead of analyzing and advising

The Solution

A unified, real-time executive performance dashboard

I built a comprehensive Power BI solution that integrated three disparate data sources into a single, live view of business unit performance—giving leadership the clarity and confidence to make data-driven decisions daily, not monthly.

What We Built:

1. Automated Multi-Source Data Integration

This was the technical foundation that made everything else possible:

Data Sources Connected:

• MIS system: Operational metrics (vehicles sold, service hours, customer counts)
• IFRS accounting system: Financial metrics (revenue, COGS, OPEX, PBT, EBIT, EBITDA)
• SharePoint Excel files: Budget/forecast data, automatically refreshed as files were updated

Data Processing Architecture:

• Built Power BI dataflows in the cloud to extract and transform data from each source
• Created aggregation dataflows that reduced raw transaction tables (10M+ rows) down to pre-summarized tables (~300K rows)
• This optimization reduced report load time from 45+ seconds to under 3 seconds—making the solution actually usable for executives

Refresh Schedule:

• Automated daily refresh at 6 AM—ensuring data was current when leadership started their day
• SharePoint files monitored for updates and pulled in automatically

2. Executive Performance Dashboard

Designed for C-suite and business unit directors to answer: “How are we performing right now?”

Primary KPIs displayed:

• Revenue (actual vs. plan vs. prior year)
• COGS and gross margin %
• OPEX and operating margin %
• PBT (Profit Before Tax), EBIT, EBITDA
• Vehicles sold (new, used, fleet)
• Service hours sold (warranty, retail, internal)

Key Features:

• Traffic light indicators: Green/yellow/red status for each KPI vs. plan
• Variance analysis: Automatic calculation of “we’re +5% vs. plan” or “we’re -12% vs. last year”
• Drill-down hierarchy: Business unit → country → location → department—click to investigate
• Trend sparklines: Quick visual of whether KPIs are improving or declining over past 3-6 months

3. Time Intelligence & Comparative Analysis

Built sophisticated date calculations to provide context:

Year-over-Year Comparison:

• Current month vs. same month last year
• YTD vs. prior year YTD
• Growth rates automatically calculated and visualized

Plan/Forecast Tracking:

• Actual vs. budget for each KPI
• Forecast attainment % (are we on track to hit annual targets?)
• Variance alerts when actuals deviate >10% from plan

Moving Averages:

• 3-month and 6-month moving averages to smooth seasonality
• Enabled leadership to distinguish “normal fluctuation” from “concerning trend”

Custom Time Periods:

• Last 7 days, last 30 days, last quarter, last 12 months
• Dynamic date filters so executives could explore any time range

4. Location & Performance Benchmarking

Created comparative views showing:

• Ranking tables: Which locations were top/bottom performers for each KPI
• Peer comparison: How each location compared to network average
• Contribution analysis: Which locations drove the most revenue, profit, volume

This enabled healthy internal competition and identification of best practices to replicate.

5. Self-Service Insights

Every visualization was designed to eliminate interpretation:

Instead of: “Revenue by Month (Bar Chart)”
I wrote: “Revenue Up 8% YoY But Tracking 5% Behind Plan—Action Needed in Q3”

Instead of: “EBITDA Margin % (Line Chart)”
I wrote: “EBITDA Margin Declining for 3 Consecutive Months—OPEX Growing Faster Than Revenue”

Executives could glance at any page and immediately understand the situation and whether action was required.

The Results

From monthly reporting delays to daily strategic agility

Immediate Operational Wins:

• 2-3 days/month saved for finance team (eliminated manual report compilation)
• Daily visibility into all critical KPIs (vs. 4-6 week lag)
• 3-second load time for reports (vs. 45+ seconds with raw data)—executives actually used it
• Single source of truth—eliminated conflicting numbers and version control issues

Strategic Business Impact:

• Faster course correction: Problems identified within days instead of weeks—leadership could intervene while there was still time to recover the month
• Data-driven resource allocation: Budget adjustments and staffing decisions made with current performance data instead of outdated assumptions
• Replication of best practices: High-performing locations’ strategies identified and deployed across network
• Improved forecast accuracy: Historical trends and moving averages enabled better prediction of seasonal fluctuations

Financial Impact:

• Margin preservation: Early detection of OPEX creep allowed for cost control measures before margins deteriorated significantly
• Revenue optimization: Identified underperforming segments and locations early enough to implement corrective pricing/marketing strategies
• Better capital planning: Real-time profitability data improved accuracy of cash flow forecasts and investment decisions

Cultural Shift:

The solution fundamentally changed how leadership operated:

Before:

• Monthly board meetings: “Here’s what happened 3-4 weeks ago”
• Leadership operating on intuition and anecdotes
• Finance team as report compilers

After:

• Daily/weekly data check-ins: “Here’s where we are today—what do we need to do?”
• Leadership operating on real-time data and trends
• Finance team as strategic advisors (freed from manual reporting)

The CEO started each week reviewing the dashboard. Business unit directors began using it in their Monday morning team calls. Controllers shifted from “preparing reports” to “analyzing variances and recommending actions.”

Leadership meetings transformed from “what happened?” discussions into “what should we do about it?” strategic sessions.

Technical Innovation Highlight

The Performance Optimization Challenge:

The original dataset contained 20M+ rows of transaction-level data. Loading this directly into Power BI created a 2GB+ file that:

• Took 30+ seconds to load visual
• Crashed on fairly great laptops
• Was unusable for executives who needed fast answers

The Solution:

We built a two-tier dataflow architecture:

Tier 1—Raw Data Extraction:

• Dataflows that pulled complete transaction history from source systems
• Stored in Power BI cloud in optimized format

Tier 2—Aggregation Dataflows:

• Pre-summarized data at monthly level
• Reduced from 10M rows to ~300K rows
• Lost zero analytical capability (all required KPIs still calculable)

Result:

• Report file size: <200MB (90% reduction)
• Load time: <3 seconds (93% improvement)
• Executive adoption: 100% (vs. ~20% when it was slow)

This technical work was invisible to users but critical to solution success — no matter how good the insights, if the tool is slow, it won’t be used.

Power BI offers many benefits for businesses. It is powerful and can handle large amounts of data. It can be used to create visually immersive reports and dashboards that give businesses a near-live 360-degree view of their entire business in a single dashboard.

Power BI is an ideal solution for small businesses because it is powerful to handle large amounts of data and can be used to create visually immersive reports and dashboards that give small businesses a near-live 360-degree view of their entire business in a single dashboard. Additionally, Power BI is cloud-based, which means that small businesses do not need to invest in expensive hardware or software to use it.

Microsoft is a leader of Magic Quadrant for Analytics and Business Intelligence Platform.

Where can help an external consultant

External consultants can help in your Power BI projects in your organization. They can help you identify the right data sources, design effective reports and dashboards, identify areas where Power BI can be used more effectively within your organization. They bring expertise and experience to your Power BI project and can design and implement your solution more efficiently and effectively. Consultants can help you follow best practices for Power BI development and avoid common pitfalls while ensuring that your solution is scalable and maintainable.

Although Microsoft aimed to make certain basic reports without any special training during the design of Power BI Desktop, the precise and quick implementation of solution answers effectively to the business needs and the appearance of further data connections and integrations requires professionals with experience and knowledge, especially in the following areas:

• Data modeling: Data modeling is the process connecting data sources and designing the structure of your data in Power BI. This requires expertise in database design and data analysis.
• DAX coding: DAX (Data Analysis Expressions) is a formula language used in Power BI to create custom calculations and aggregations. This requires expertise in programming and data analysis.
• Visualization design: Visualization design is the process of creating effective and engaging visualizations in Power BI. This requires expertise in graphic design and data analysis.
• Performance optimization: Performance optimization is the process of improving the speed and efficiency of your Power BI solution. This requires expertise in database design, query optimization, and data analysis.

Typical use cases

Sales and marketing analytics: Power BI can help you analyze your sales and marketing data to identify trends, opportunities, and areas for improvement. You can use Power BI to create dashboards and reports that show your sales performance, customer behavior, and more.

Financial analytics: Power BI can help you analyze your financial data to gain insights into your company’s financial performance. You can use Power BI to create dashboards and reports that show your revenue, expenses, cash flow, and more.

Human resources analytics: You can use Power BI to track the performance of your human resources teams. For example, you can create dashboards and reports that show employee turnover rates, headcount, performance metrics, and more.

Supply chain analytics: Power BI can help you analyze your supply chain data to identify bottlenecks, inefficiencies, and areas for improvement. You can use Power BI to create dashboards and reports that show your inventory levels, order fulfillment rates, shipping times, and more.

Healthcare analytics: Power BI can help you analyze your healthcare data to gain insights into patient outcomes, treatment effectiveness, and more. You can use Power BI to create dashboards and reports that show your patient satisfaction scores, readmission rates, mortality rates, and more

Education analytics: Power BI can help you analyze your education data to gain insights into student performance, enrollment trends, and more. You can use Power BI to create dashboards and reports that show your student retention rates, graduation rates, test scores, and more.

Retail analytics: Power BI can help you analyze your retail data to gain insights into customer behavior, inventory levels, and more. You can use Power BI to create dashboards and reports that show your sales performance, customer demographics, and more.

Manufacturing analytics: Power BI can help you analyze your manufacturing data to identify bottlenecks, inefficiencies, and areas for improvement. You can use Power BI to create dashboards and reports that show your production rates, quality metrics, and more.

IT analytics Power BI: can help you analyze your IT data to gain insights into your company’s technology infrastructure. You can use Power BI to create dashboards and reports that show your network performance, server uptime, security metrics, and more.

Social media analytics: Power BI can help you analyze your social media data to gain insights into customer sentiment, engagement rates, and more. You can use Power BI to create dashboards and reports that show your social media performance across different platforms.

Service maintenance analytics: You can use Power BI to track the performance of your service maintenance teams. For example, you can create dashboards and reports that show the number of service requests completed, the average time to complete a request, and more.

IT consultant analytics: You can use Power BI to track the performance of your IT consultants. For example, you can create dashboards and reports that show the utilization rate of resources, the number of tickets closed, the average time to resolve a ticket, and more.

Examples of Power BI reports:

[1] The on-premises version of Power BI is called Power BI Report Server. It is a server that allows you to host and share Power BI reports within your organization’s firewall. Power BI Report Server is ideal for organizations that need to keep their data on-premises due to regulatory or compliance requirements. There are some disadvantages to PBI service, like limited data sources, collaboration, scalability and PBI service entering cost is lower (Report Server needs Power BI Premium plan).

Data, in theory, is like Mathematics. It’s pure, unbiased, unquestionable, non-negotiable, and simple. It’s neutral. But in practice, data is what you use it for and how you use it. The complexity, the diversity, the structure, and usability of it depends on its sources, its users, and the output formats that you choose.

Working together with our client, we saw how different needs, different professional experiences and expectations, different views and different datasets from various sources can increase the complexity and make reporting challenging. Our client’s various business units, in different countries, with managers coming from completely different backgrounds used their data in distinctive ways – serving their specific needs.

In business, just as in life, the challenge is usually to simplify things as much as possible, to synchronize processes, to avoid clutter, and reduce superfluous activities to reach maximum efficiency.

Our client has offices in various countries, including Austria, Germany, Switzerland, and Hungary, and works across three different business units – at different stages of their lifecycle. Their reporting was already well-advanced, with clear expectations and reports that serve business planning and forecasting: to find the harmonized view of the various data sources. Having a sales-oriented mindset, the focus was on revenue and pipeline data, with a lower priority on costs.

What we achieved

With our project the aim was to create a consolidated profit and loss report that delivers a near-real-time solution for all business units, all countries with different legal and taxation needs – serving the reporting needs of the CEO, the management, and the mid-management equally.

Challenges

Our biggest challenge was to find the right way of consolidating the data that can serve all the different professional needs (different countries, different legal entities, different business units with different business goals) and cater to the familiarity of previous experiences.

The expected reporting solution had to serve multiple purposes.

First, it had to serve all the potential different levels, from the CEO to management, going various levels down to the business units. This meant to provide the option to show the bigger picture view to display trends, while also offering the possibility to drill down to specific granularities. Beyond the business planning, the reporting structure had to support the work of controlling, the CFO, and the COO – and to serve as the base for the quarterly performance review of the management and the teams.

Secondly, we had to create a structure that can show the changes in past, and that can help forecast the future (typically until the end of actual fiscal year); that also allows to compare achieved and forecasted performance to business plan (created before every fiscal year). For comparability and more precise planning, we needed to facilitate highlighting changes and deviations between different reporting periods.

And lastly, our client being an already established company, the report structure, and the report categories within had to be aligned to the company’s established standard of P&L report, with given categories via a complex rule set.

We have worked with various data sources, 3 instances of ERP, CRM and HRMS systems, the data from accounting and payroll systems of 5 legal entities in different countries around Europe, and the offline dataset of detailed yearly business planning.

Implementation

In the project we used the following technologies:

• Microsoft Power BI
• Microsoft Power Automate
• Microsoft SQL Server
• Data connections: SQL Server, Firebird, REST API, Excel, SharePoint lists

What we implemented, as the result of the project, was a customized, filterable aggregated report-system with deep drill down functions for measuring, forecasting, and comparing with the business plan for the following categories:

• direct business units’ revenue (software/service),
• revenue related cost (software/service),
• staff costs (delivery, sales management and for corporate positions),
• indirect cost
  • cost of administration and finance,
  • cost of operating HR,
  • cost of infrastructure and IT,
  • cost of management,
  • cost of marketing,
  • depreciation and amortization,
  • taxes and
• profitability on different levels: direct margin on business units, EBIDTA, EBIT, profit after tax.

All items contain (1) actual data, from invoices and other accounting items, (2) forecast from orders (agreed delivery in contracts), and (3) forecast from sales pipeline weighted with probability. The trends are displayed over periods.

All report pages can be filtered by period (month/quarter/year), business unit and lower units and by different categories. Beyond this, all specific pages can be filtered by projects, customers, vendors, products, and services type.

As a unique feature, reports can be compared in detail with previous versions. The changes are highlighted week-by-week, so the group management and the business management can see the items responsible for the change in revenue and/or cost.

Data updates are made in an identical structure staging environment where the controlling team and the CFO review and approve changes before publishing them to the management.

Result

As a result of the implementation, our client now has a continuously updated report for overall financial performance, as well as for detailed metrics. The current reporting system is accepted as single source of truth within the company’s leadership and used for the regular management review to set and measure individual and overall business goals.

This project proved how data can serve as the go-to-solution in multiple areas of a given company, even if the diversity creates a complexity that seems impossible to handle.

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