Strategy made practical
We take a business-focused approach to running successful data science and AI projects. Starting from why data should be treated as a key company asset, we introduce our 8-step data science model — from defining the problem to communicating the results.
What We Do
Data Maturity Assessment
Evaluate your organization's data readiness, identify gaps, and create a roadmap to data-driven maturity.
AI Strategy & Roadmap
Define a practical, business-aligned AI strategy with prioritized use cases, timelines, and investment plans.
Team & Culture Building
Build cross-functional data teams, upskill your workforce, and foster a culture of data-driven decision making.
Many firms want "AI" but often just need solid analytics and BI.
Data is like "new oil": valuable but hidden, unstructured, and often poor quality.
Being data-driven means investing in data as an asset and understanding data models and structures.
Organizations evolve from data-averse to data-guided; culture change is slow.
To kickstart the journey: understand your data, create space for experimentation, set up a safe test environment, and start with small, practical use cases.
Everyone works with data to some extent; data skills are not just for formal data scientists.
Data scientists themselves need support, mentoring, and a broader competence framework that includes business and communication skills, not just R/Python.
From problem framing to storytelling with data — each phase is critical for delivering real business value.
step 01
The Art of Asking
Define the business problem clearly and align it with strategy. Achieve shared understanding, assess impact and complexity. Secure resources and buy-in from key stakeholders.
step 01
The Dataland
Map all relevant internal and external data sources. Assess quality, bias, linking keys, costs, and structure. Consider privacy, metadata, and real-time vs historical needs.
step 03
Data Knowledge
Deeply analyse selected sources: structure, fields, granularity, quality, and privacy. Covers data governance (DMBOK), GDPR legal bases, and anonymization techniques.
step 04
Mining & Combining
Prepare, clean, integrate, and explore data — the most time-consuming phase. Exploratory Data Analysis: univariate/multivariate analysis, correlation, PCA, clustering.
step 05
Deep into Domain
Domain experts validate and interpret patterns found in data. Data scientist–domain expert collaboration should be continuous and two-way.
step 06
Modelling
Build and validate models: from simple KPIs to predictive and prescriptive analytics. Seven AI branches: ML, NLP, expert systems, speech, vision, planning, robotics.
step 07
Feed the Eyes
Data visualization bridges complex data and human understanding. Principles: trustworthy, accessible (colour-blind friendly), and elegant.
step 08
Tell the Story
Synthesize insights and communicate via presentations and dashboards. Tailor content and technical detail to different audiences.
Understanding the common pitfalls is just as important as knowing the methodology.
Typical root causes: poor scoping, weak stakeholder support, lack of data literacy among leaders, neglected change management, missing cross-functional teamwork.
Data science is a “team sport” requiring many roles: business leaders, data architects, engineers, data scientists, visualization experts, translators, and more.
The “analytics translator” role bridges business and technical teams to ensure analytics actually solve real business problems and deliver value.
Engineering Intelligence from Data
AI Venture Studio & Enterprise Builder. Building AI companies since 2015 from Utrecht (NL) and Kaunas (LT)
Service we offer
What We’ve Built
- d-HealthIQ
- RiskFin-IQ
- Retail AI
- ERP Data Migration
