Solution

BI & Analytics

Analytics is not pretty charts on the wall. It's the answer to 'why?' before the problem becomes a loss.

Every company believes it has analytics. In practice, analytics is a person who once a month collects data from five systems into Excel and creates a 40-slide report.

The problem isn’t the tools. Power BI, Tableau, Metabase — they can all draw charts. The problem is that data is scattered across systems, references aren’t synchronized, and nobody has defined which metrics actually drive decisions.

A properly built BI system starts not with dashboards but with the question: what decisions does management make and what data is needed for that? The answer to that question determines everything else.

Target Model

How It Should Work

A single data warehouse from all systems. Dashboards update automatically. The executive sees key metrics in real time and can drill down to primary data. The analyst spends time on analysis, not data collection.

ETL and Data Warehouse

C-level management dashboards

Operational analytics for departments

Regulatory reporting (automation)

Customer analytics and segmentation

Financial analytics and forecasting

Real-time KPI monitoring

Ad-hoc reports and self-service BI

Проблемы

Где обычно все ломается

Reports assembled manually from multiple systems

Data in different systems doesn't match — each department has its own version of truth

Dashboards exist but nobody trusts the numbers

Analyst spends 80% of time collecting data and 20% analyzing

No historical data — can't track trends

Reporting for management and for regulators are separate processes

Excel is the main analytics tool

Consequences

What This Leads To

⚠ Decisions made on intuition, not data

⚠ Problems discovered months later, not days

⚠ Every management report is a week-long manual project

⚠ Can't measure the effect of changes — no baseline

⚠ Regulatory reporting is stressful every quarter

My Approach

How I Approach the Challenge

I start with questions: what decisions does management make and what data is needed for that. Then — where does that data currently come from, how many steps, how long. Usually there are 5–7 manual steps between data and decision, each introducing errors.

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Team

How We Work

My Role

Define key metrics and KPIs, design data model and ETL architecture, select visualization stack, monitor data quality at each stage.

Team Role

The team configures ETL processes, develops the data warehouse, creates dashboards, integrates data sources, automates regulatory reporting.

Important

Key Considerations for Implementation

🔎 Data without unified references is garbage. Start with master data

🔎 A dashboard nobody trusts is worse than no dashboard

🔎 Analytics should answer business questions, not display all available data

🔎 Automated regulatory reporting saves hundreds of hours per year

🔎 Start small: one report, one source, one consumer

Results

What Results to Expect

✓ Report preparation time reduced from days to minutes

✓ Single source of truth — all departments see the same numbers

✓ Management makes decisions on current data

✓ Regulatory reporting generated automatically

✓ Analyst focuses on analysis, not data collection

Практика

Cases for This Solution

telecom

Subscriber 360 for a Telecom Operator

Typical problem: 6–8% monthly churn, marketing firing blind, support doesn't know the customer. Cause — subscriber data scattered across 5–7 systems and nobody sees the full picture. Solution — an integration layer, not a new system.

Churn dropped from 8% to 3.5% within 4 months of launch

ARPU grew 15% through personalized offers

Churn prediction accuracy — 78%

banking

Lending Process Transformation at a Bank

Classic problem: loan applications take 2 weeks to process; clients leave for competitors. The cause — the bank digitized a paper process without redesigning it. The right approach: reengineering first, then automation.

Application-to-disbursement time reduced from 2 weeks to 2 days

Lending volume grew 45% in the first quarter

Process steps reduced from 23 to 9

manufacturing

MES Pilot at a Manufacturing Enterprise

The plant doesn't know its real cost of production — ERP calculates by standards, foremen enter data from memory. We explain how an MES pilot on one line reveals the real picture and why cost of production is often 20%+ above plan.

Production data transparency — 95% (was ~40%, the rest was manual entry)

Downtime reduced by 34% through real-time cause visibility

Real cost of production established: +22% vs plan — prices corrected on 3 products

FAQ

Frequently Asked Questions

Which BI platform should we choose?

Depends on scale and budget. For mid-market — Metabase or Apache Superset (open-source). For enterprise — Power BI or Tableau. But the platform is secondary; the data model is primary.

How long does implementation take?

First working dashboard — 2–4 weeks. Full data warehouse with ETL — 2–4 months. We start with a quick win and expand iteratively.

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