BI dashboard KPIs are the few indicators management uses to run the business. A good set is small (usually 6–10 on one screen), tied to current goals, and each KPI has a written definition, an owner and a threshold. A dashboard gets used when those KPIs are calculated automatically from ERP, CRM and production data, using one rule for all.
What really happens in the management meeting
It is the first Monday of the month. Finance brings a revenue figure from the ERP, sales brings a different one from the CRM, and the gap comes down to how returns and uninvoiced orders are counted. The production manager's OEE table is last week's spreadsheet. The first half hour goes on "which number is right?", and the decision is pushed to the next meeting.
Three problems usually sit behind this:
- Too many indicators. Forty charts added "just in case" make it unclear what to look at. A dashboard that shows everything highlights nothing.
- No shared definitions. Terms such as "revenue", "active customer" or "on-time delivery" have no single written definition, so every department does its own calculation.
- Manual feeding. The data depends on someone exporting and merging it in Excel. When that person is on leave the report is late; when a formula slips, the number is wrong. We list the typical warning signs in outgrowing spreadsheets for business tracking.
That is why a BI project should start not with choosing software but with the question "what does management need answered every week?" We walk through the move away from spreadsheets in from Excel reports to BI.
The cost of not fixing it
In Türkiye, business intelligence is still the exception rather than the rule:
According to TurkStat, in 2025 28.3% of Turkish enterprises with 10+ employees used ERP software, 12.0% used CRM and only 6.5% used business intelligence (BI) software. (TurkStat ICT Usage Survey in Enterprises 2025)
The gap widens with company size: BI was used by 35.1% of enterprises with 250+ employees but by only 4.9% of those with 10-49 (TurkStat 2025). In other words, many companies with an ERP have their data recorded, but it does not come back to management as decision support — data sitting in the ERP is still copied into spreadsheets to be reported.
The cost of deciding on wrong or late numbers is hard to measure, but it is not small. Data quality expert Thomas C. Redman estimates the cost of bad data at 15% to 25% of revenue for most companies (MIT Sloan Management Review, "Seizing Opportunity in Data Quality", 2017). It is an expert estimate, but the direction is clear: a company answering the same question with two different numbers spends its time reconciling rather than deciding.
There is a people angle too. TurkStat reports that in 2026 only 15.2% of Turkish enterprises employed ICT specialists, and 31.7% of those that tried to recruit them in 2025 reported difficulties (TurkStat ICT Usage Survey in Enterprises 2026). If reporting depends on one person's spreadsheet skills, reporting stops when that person leaves.
A framework for choosing management KPIs
These steps summarise the order we follow, from choosing an indicator to putting it on screen:
- Start from goals, not from data. Ask "what do we want to achieve this year?" rather than "what data do we have?". If the goal is higher gross profitability, the KPI is not revenue but gross margin by product and customer. We cover turning goals into indicators in how to set KPIs.
- Limit the number. The executive summary should fit on one screen; 6–10 indicators are enough for most companies. Detail belongs in sub-dashboards and drill-downs.
- Define every KPI in writing. Formula, data source, what is included and excluded (returns, discounts, cancellations), refresh frequency and unit go on a single definition card.
- Assign an owner and thresholds. Every KPI needs someone responsible and green / amber / red thresholds. Without a threshold, interpretation is left to whoever is looking.
- Balance leading and lagging indicators. Revenue is lagging — it tells you what happened once the month is over. Sales pipeline value, order intake or planned-versus-actual output warn you in advance.
- Connect directly to the source. Calculate the KPI from data pulled automatically from ERP, CRM and MES, not from a hand-built file.
- Let people drill down to the document. When a manager sees a deviation, one click should take them to the customer, product, invoice or work order. A number you cannot drill into starts the argument all over again.
- Review quarterly. When goals change, KPIs change. Remove the charts nobody looks at.
Example KPIs by function
| Area | Lagging KPI (outcome) | Leading KPI (signal) | Typical data source |
|---|---|---|---|
| Sales | Revenue, gross margin | Pipeline value, quote-to-order conversion | ERP, CRM |
| Production | OEE, scrap rate | Planned vs actual output, downtime reasons | MES, SCADA |
| Inventory and supply | Stock turnover | Items below minimum level, supplier delivery performance | ERP, WMS |
| Finance | Collections achieved | Receivables falling due, debtor ageing | ERP finance module |
| Customers | Customer churn | Customers who have not ordered recently, order frequency | CRM, ERP |
Treat this as a starting list; each company's KPIs should be derived from its own goals. A contract manufacturer may put on-time delivery rate on the first line of the executive summary, while a retail chain will focus on sales per square metre by store. For municipalities, most KPIs are tied to location and are read alongside the municipal GIS map.
Three common mistakes
- Trusting the average. "Average delivery time: 3 days" looks fine, but if some orders take 10 days, that is where customers are lost. Show the distribution, or the number of records over the threshold, next to the average.
- Showing a rate without its base. When the scrap rate rises, volume or product mix may have changed too. Showing units or value next to the rate prevents false alarms.
- Feeding one KPI from two sources. If revenue comes from the ERP on one dashboard and from the CRM on another, the meeting-room argument simply moves onto the screen. Every KPI needs one official source.
The small details that make a dashboard usable
- A one-page executive summary, with a sub-dashboard behind each KPI.
- Context for comparison: target, same period last year and trend line on the same chart.
- Threshold alerts: rather than managers opening the dashboard daily, the owner is notified when a KPI turns red. For unusual deviations a fixed threshold cannot catch, AI anomaly detection takes over.
- Mobile access and a morning summary: a single page the managing director can open on a phone, or receive by email each morning.
- Data freshness: a "last updated" time next to each KPI. Something this simple stops decisions being made on stale data.
How we build it at Digital Bridge
On our BI dashboard and management reporting projects we design the indicators before the screens:
- KPI workshop. With management we capture goals and the questions that need answering every week, then write a definition card (formula, source, owner, threshold) for each KPI. Definitions are refined until departments answer the same question with the same number.
- Direct connections to source systems. We connect to Logo Tiger/GO, SAP, Mikro, Netsis and bespoke ERPs, CRMs, marketplace APIs and MySQL, MSSQL and PostgreSQL databases. For systems exposing a REST API we build the API integration; for production KPIs we bring in data from MES production management or SCADA.
- One source of truth. Where several systems and historical comparisons are involved, we consolidate the data in a data warehouse so reports do not slow down the live ERP. The architecture is covered in our data warehouse and ETL guide.
- Data quality first. If duplicate customer accounts, inconsistently coded products or empty fields distort a KPI, we fix it at source through data governance and quality work. Showing bad data in a nicer chart solves nothing; we cover the typical problems in our article on duplicate records and data quality.
- A dashboard that refreshes itself. Reports update at the agreed interval; managers drill from a KPI down to the invoice or work order, and the owner is alerted when a threshold is crossed.
- A phased start. Rather than moving the whole company onto dashboards at once, we begin with the executive summary and one departmental dashboard, then extend to other areas once the KPIs are bedded in. The team gets to test the definitions against real data early on.
For HR indicators such as absence, overtime and shift adherence, the source is usually entry and exit data. SmartPass combines card access control, time and attendance and canteen meal counting in one system; it calculates attendance against the shift plan and produces reports that can be passed to payroll. That data can feed the dashboard under the same definition rules.
We do not sell off-the-shelf packages; after a needs analysis we provide a written proposal covering scope, phases and cost. Since 2013 our Adana-based team has served clients in every province of Türkiye, remotely and on site.
Next step
Try a simple exercise before your next management meeting: write down the five questions management asks every month and note, for each, which system the answer comes from today, who prepares it and how many hours it takes. That list is enough to scope your first dashboard. Get in touch and we will define your KPIs with you and mock up an executive dashboard using your own data.