Business Intelligence System:
Turn data into confidence
On this page, you’ll learn how a Business Intelligence System
helps managers make data-driven, well-informed decisions.
Incomplete understanding of the business
Too much data, too little time to analyze it.
I don’t have a clear view of profit drivers or loss points.
Everything is digitized, yet we lack a holistic picture of how the business is doing.
The business runs — but I can’t tell what’s truly profitable and what’s just activity.
I lack a holistic view of the business — teams are misaligned and disconnected.
I can’t clearly explain who our ideal customer is — or why they would choose us.
We have tons of reports — but I’m not getting real insights from them.
Investors ask simple questions — and I can’t give clear answers.
Unjustified decisions
Strategic sessions turn into hypotheses and intuition instead of data driven decisions.
The team is building the product without A/B tests, hypotheses, or market feedback.
There is no visibility into pre sale processes, so we cannot optimize the customer journey.
I am making more decisions based on gut feeling than on numbers and it worries me.
It is unclear which features are working and which are not. The product team cannot prioritize.
We are losing customers, but I cannot tell where in the funnel it happens.
Решения принимаются на эмоциях или по срочности, а не по приоритетам.
Мы внедряем улучшения наугад, надеясь, что хоть что-то сработает.
Lack of accountability
KPI are vague or missing — it’s hard to measure the contribution of individual teams or functions.
I can’t evaluate the performance of a department, product, or channel — the right metrics just aren’t there.
I introduce KPIs, but the team doesn’t see their relevance or impact.
The team is working hard, but the product isn’t taking off — and no one can explain why.
Hands-on management
As long as decisions are manual, we can’t scale effectively or allocate resources with confidence.
I can’t explain to the team what’s working and what’s not because I don’t fully understand it myself.
We are growing, but it does not feel like we are in control. I am afraid everything could fall apart at any moment.
We waste time figuring out which version of the data is accurate.
To get an answer to a simple question, I have to wait several days for the analyst.
Unexplained drops in conversion
Customers do not use the full potential of the product or leave quickly, and we do not know why.
I do not understand why customers are leaving. There are many possible reasons, but no data.
Managing by reacting, not by anticipating
Operational issues catch us by surprise because we lack early warning signs.
A business intelligence system is a software platform that aggregates data from multiple sources and enables instant calculation of unit economics, profit margins, and return on investment. It allows the creation of end to end reports and provides actionable management insights. This empowers data based decision making and solves the problem of limited transparency and lack of an informational foundation for strategic leadership.
The system integrates three domains of expertise: business, analytical, and technical:
Examples of dashboards from different business intelligence systems:
(note) – sourced from non confidential, public materials
Step 1. Availability of primary data
To be usable, data must be in digital format and stored in a way that allows easy export.
Where and how can primary data be stored?
– Internal data storage systems (Data Warehouse or Data Lake)
– Third party cloud storage
– CRM application or customer records
– Inventory systems, price lists, and sales data
– Marketing reports
Step 2. Data collection
Data is often copied or exported from slower or less accessible sources into faster environments to improve performance. To optimize database size and speed, data is also filtered or transformed to ensure consistency, standardization, and uniform structure.
How is data collected and processed?
– ETL processing (storing only transformed data)
– ELT processing (storing both raw and transformed data)
– Connectors for databases, applications, and ERP systems
Step 3. Data analysis
The collected data must be analyzed to identify patterns, build models, and forecast customer behavior or business dynamics. Through analysis, data is transformed into actionable management insights.
What does “data analysis” mean?
– Pattern discovery (identifying trends in data and uncovering unexpected relationships between metrics and events)
– Intelligent analysis (using statistical and machine learning methods to extract meaningful patterns and correlations, such as identifying which customers are more likely to churn and why)
– Modeling (building analytical models such as forecasting, causal, and segmentation models; this may include unit economics, return on investment calculation, customer lifetime value, and more)
– Predictive analytics (forecasting future events such as customer churn, seasonal sales fluctuations, and resource demand)
– Prescriptive analytics (recommendations on what actions to take to achieve goals based on models, such as which features to prioritize or which customer segments are more profitable)
Step 4. Visualization
Once data has been collected, processed, and analyzed, the next step is proper visualization. The goal of this stage is to turn tables, charts, or raw information into clear visuals that support decision making.
What elements of the interface are configurable?
– Interactive dashboards (dynamic panels with key metrics that update in real time and give managers a company wide view of performance)
– End to end reports (combine data from multiple sources such as CRM, ERP, Excel, and more to present a complete picture in a single interface)
– Financial and product metrics (ROI, profit margin, LTV, retention, CAC, churn, activation — visualized in clear formats with drill down capability by department, segment, or funnel stage)
– Alerts and automations (events and triggers can be configured — for example, the system notifies you when a KPI drops below target or a spike in customer churn is detected)
– Access rights (you can configure different charts and dashboards for different employees based on their access level)
It is important to distinguish between a business intelligence tool and a business intelligence system:
Business Intelligence Tool (for example, Yandex DataLens or Microsoft Power BI) is a specific software solution that enables data collection, reporting, and visualization. It is only a technological component within a broader system.
Business Intelligence System is a comprehensive framework that includes tools, data sources, methods of extraction, selected business and product metrics, processes, governance rules, and defined roles. It transforms data into management insights and supports decision making at the business level.
Example:
Tool = Microsoft Power BI or Yandex DataLens
System = Microsoft Power BI + data sources + KPIs + calculation logic + roles + governance + business processes
It is important to distinguish between Business Intelligence and Business Analytics:
Business Intelligence (BI) is descriptive analytics (reporting).
Business Analytics (BA) is advanced analytics (diagnostic, predictive, prescriptive).
Example:
Business Intelligence: Revenue dropped by 10% in May.
Business Analytics:
– Root cause: Churn of a key customer segment;
– Forecast: Further 15% decline in June;
– Recommendation: Revise the value proposition.
