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Data Analytics Services

We build custom analytics dashboards, scoring engines, and decision-support tools that turn complex data into decisions your teams actually make. From concept to production.

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Trusted by:

Siemens
PwC
Toyota
Geberit
Rainbow
Chooose

Does your datawork this hard?

  • Complex metrics need expert interpretation before anyone acts on them.
  • Executives see charts. They need recommendations.
  • Your teams have dashboards. They still ask analysts for every answer.
  • Each report means stitching spreadsheets together by hand.
  • Nobody looks at the dashboard.
  • The auditor asks where this number came from.
  • Reports arrive weekly. Decisions are needed daily.
  • The data exists. The decision-support layer doesn't.
  • Every department has different numbers.
  • Budget burns before anyone notices.
  • Risk scoring is still done in Excel.
  • A new metric means a week of waiting for IT.

Data analytics services for digital teams

Custom Analytics Dashboard Development

Dashboards designed for the people who use them: executives, planners, marketers. Complex metrics translated into views that drive action, not interpretation meetings.

Scoring Engines & Decision Platforms

Risk scoring, credit decisioning, prioritization engines. We build the calculation layer and the interface that makes results usable, from a cyber risk scoring platform to an automated credit decisioning platform.

Data Visualization for Regulated Industries

Data visualization for fintech, cybersecurity, and enterprise SaaS: audit-ready, compliance-aware, built to present complex calculations to non-expert audiences.

Self-Serve Analytics for Non-Technical Teams

Marketing analytics for non-technical users, campaign reporting, budget tracking. Tools that remove the analyst bottleneck: your business teams explore data without writing a ticket.

Reporting Automation & Alerts

Automated anomaly alerts and scheduled reporting that flag problems before budgets burn, replacing manual monitoring with proactive signals.

Analytics Layer for Existing Platforms

We add decision-support capabilities to products already in production: conversational access to complex data, multi-persona reporting, executive briefings generated from live systems.

Data analytics platforms we built

Explore more case studies

From data audit to production in 5 steps

  1. 01

    Data & Decision Audit

    We map your data sources and, more importantly, the decisions they should support. Output: what to build first and why.

  2. 02

    Design for the Decision-Maker

    Dashboards and scoring interfaces prototyped with the actual users: executives, planners, non-technical teams. Tested before a line of production code.

  3. 03

    Build & Integrate

    Depending on what the audit shows, we build a standalone analytics platform from scratch or add a decision-support layer to your existing product. Either way, we integrate with your current systems (ERP, CRM, data warehouses, product databases).

  4. 04

    Validate on Your Numbers

    Accuracy checked against historical data and real user queries before go-live.

  5. 05

    Production & Iteration

    Deployment with monitoring, then iteration based on how your teams actually use it.

Why enterprises choose us for data analytics

We're a 50-person, cross-functional software development team based in Warsaw, Poland, building technology that delivers ROI, strong governance, and real adoption.

10 years

delivering digital products

est. 2016

100+

products shipped

web & mobile

50+

experts on board

Product & UX designers, Software engineers, AI specialists, PMs

75

client NPS

Praised for communication, pace and quality

5

continents served

North America, South America, Europe, Asia, Africa

Frequently Asked Questions

Data analytics services cover the design, development, and deployment of tools that turn raw business data into decisions: dashboards, scoring engines, reporting automation, and decision-support platforms. At SH, this means custom-built products integrated with your existing systems, not off-the-shelf BI licenses.

Data analytics focuses on making existing data usable for decisions: dashboards, visualization, scoring, and reporting. Data science focuses on building predictive capabilities from that data. If you need your teams to see, understand, and act on data, you need analytics. If you need systems that predict and learn, see our Data Science services.

Yes, that's most of what we do. For Reffine, we built marketing analytics that non-technical teams use daily without analyst support: campaign metrics translated into business language, with automated alerts replacing manual monitoring. The design process starts with the actual decision-makers, not the data schema.

Yes. We've delivered an automated credit decisioning platform for Siemens Financial Services (compliant with Polish financial regulations) and a NIST/CIS compliant cyber risk scoring platform for a US cybersecurity company. Compliance requirements shape the architecture from day one, including audit trails and dedicated infrastructure for sensitive data.

ERP systems, CRMs, data warehouses, product databases, and cloud platforms (GCP, AWS, Azure). We build the analytics layer on top of your existing stack; nothing needs replacing.

A focused dashboard or reporting tool typically ships in 6-12 weeks. Full decision platforms with scoring engines and multiple integrations take longer. After the data and decision audit, you get a concrete scope and timeline.

It depends on scope: number of data sources, complexity of the scoring or calculation layer, and user roles served. After a 30-minute call and a short audit, we propose team composition and a fixed estimate. No open-ended billing.

Ready to turn your data into decisions?

Let's talk about your data sources, your decision-makers, and what's blocking them today. We'll propose an approach within a week.

Book a 30-min call