What exactly is a data platform: and do we really need one?

A data platform is the technical infrastructure that automatically consolidates data from different source systems, structures it, and makes it permanently available for analysis, reporting, and AI. You need one if more than one person regularly assembles data manually: or if your reporting depends on a single individual. This is not an optional project: without a data foundation, neither business intelligence nor AI systems work reliably.

How long does a data platform project take?

It depends heavily on scope. A first productive core: with the most important source systems integrated and automated pipelines: is typically operational in 8–14 weeks. The complete platform with all data sources, quality framework, and knowledge transfer takes 12–20 weeks depending on complexity. In an initial conversation we can estimate this for your specific situation quickly.

Which systems can be integrated?

Essentially any system that provides an API, database access, or export function: ERP systems (SAP, Microsoft Dynamics, Navision), CRM systems (Salesforce, HubSpot, Pipedrive), production systems, accounting software, marketing platforms, and more. Airbyte alone offers over 300 pre-built connectors. What is not covered by a standard connector is built as a custom pipeline.

On-premise or cloud: what do you recommend?

It depends on your requirements: particularly around data sovereignty, GDPR compliance, and existing infrastructure. Many of our clients prefer on-premise operation because data does not leave the company. We build platforms that support both: and explain transparently which architecture decision makes more sense for your situation.

What does a data platform project cost?

A first productive core: with the most important source systems integrated and documentation: is typically in the range of €15,000–40,000. The amount depends on the number of source systems, the complexity of the transformation logic, and the desired scope. In a short initial consultation we can quickly assess what is realistic for your situation.

Can we operate the platform ourselves afterwards?

That is the explicit goal of every project. At the end you receive complete documentation, an operations manual, and practical training. The platform is built and handed over so your team understands it, can operate it, and can extend it when new requirements arise: without opening a ticket to an external provider for every change.

Data Platform · Data Engineering

Onedatafoundation.Allsystems.Alwayscurrent.

ERP, CRM, production, finance: your data is scattered. We build the technical foundation that connects everything: integrated, documented, without vendor lock-in.

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Data silos detected

alpLytics · Live

Data silos

55 %

Data quality

14 D

Decision time

Manual reports

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Why this matters

Numbers that show what missing data integration costs

Three indicators that make the operational damage of fragmented data landscapes visible.

0–0 %

of analysis time lost to data preparation

In fragmented data landscapes, professionals spend 30–40 % of their analysis time on data preparation rather than analysis: a structural waste eliminated by an integrated data platform.

0 %

faster reports with an integrated platform

Companies with an integrated data platform reduce time-to-report by up to 70 %. The investment typically pays back within a year through saved manual labour.

0 %

of AI projects fail due to poor data foundations

The most common reason AI projects fail is not poor algorithms: it is insufficient data quality and missing integration. Without a clean, structured data foundation, AI remains a pilot project.

The reality: and what is possible

Status quo in most companies → With a modern data platform

Without data platform → current reality. With data platform → target state.

Status quo in most companies

ERP, CRM, production systems, and accounting do not talk to each other. Every cross-departmental analysis starts with manual data collection.

Reporting depends on one person who exports and merges data in Excel each month. Leave or sickness stops reporting entirely.

Integrating a new data source takes weeks: because no infrastructure exists to do it systematically.

With a modern data platform

All relevant source systems flow automatically into a structured, current data foundation. Reports come from a single reliable source.

Data pipelines run automatically: daily, hourly, or in real time. No manual intervention, no single point of failure.

New data sources are integrated following an established pattern. The platform grows with the company's requirements.

Measurable project results

What an integrated data platform changes in daily operations

Not more data, but available data, automatically current, unified, and without manual intermediate steps.

What changes operationally

Automated pipelines replace manual exports: business teams work with current data instead of Excel snapshots.

ERP, CRM, production data, and financials flow automatically into a shared, structured data foundation.

Dashboards and reports update automatically: no manual steps between data source and decision.

Results vary by starting point. All figures from completed, anonymised projects.

Manual data preparation per week

−94 %

12 hrs45 min

Automated pipelines replace manual exports: business teams work with current data instead of Excel snapshots.

Data sources integrated

−86 %

7 silos1 platform

ERP, CRM, production data, and financials flow automatically into a shared, structured data foundation.

Time to completed report

−97 %

3 days< 1 hr

Dashboards and reports update automatically: no manual steps between data source and decision.

Faster decisionsLess coordination overheadReliable KPI logic

Our approach: ADET methodology

From fragmented data landscape to integrated platform

4Phases8–12WeeksWeek 2first results

ASSESS1–2 weeksPhase 1 of 4

Understanding your data landscape

We analyse all relevant source systems, data flows, and usage needs in your organisation. Which systems produce which data? How is it used today: and by whom? Where do quality problems, manual processes, and information gaps arise? Only this complete picture enables an architecture decision that fits your company.

Scroll for DESIGN

Phase 1 of 4

Our approach: ADET methodology

From fragmented data landscape to integrated platform

4Phases8–12WeeksWeek 2first results

ASSESS1–2 weeks1 / 4

Understanding your data landscape

Swipe for DESIGN

DESIGN1–2 weeks2 / 4

Architecture for your reality

Based on the analysis, we develop a data architecture that fits your actual systems, your team, and your mid-term requirements. We recommend the technology stack with full justification: why these technologies, why these architecture decisions, which alternatives we evaluated.

Swipe for ENGINEER

ENGINEER4–12 weeks3 / 4

Pipelines that run. Code that is readable.

We build the data platform in working increments: data pipelines, transformation layers, data models, monitoring, and error handling. Each component is documented before the next begins. You see progress not just in meetings: but in running systems you can test yourself.

Swipe for TRANSFER

TRANSFER1–2 weeks4 / 4

Operations without external dependency

We hand over the platform so your team fully understands, operates, and can extend it. This includes technical documentation, an operations manual, training for relevant team members, and a structured handover process. The goal: after handover you no longer need us for day-to-day operations.

Framework complete

„I remember a project where the head of sales spent three hours every Monday manually copying data from five systems into an Excel file: so he could run a 30-minute meeting with management that afternoon. That is not an exception. It is everyday reality in many mid-size companies. And it is completely avoidable once the foundation is right."

Dominik Wörz

Founder, alpLytics · Vienna

What you concretely receive

Concrete deliverables

An integrated data platform with all relevant source systems: ERP, CRM, production data, financials, marketing tools: automatically merged into a structured, current data foundation. Every component is production-ready: robust error handling, monitoring setup, automatic alerts on anomalies.

Typical project results

up to – %

less manual data preparation

up to – %

faster report creation

from – wk.

to first productive data flow

Tools & Technologies

Proven technologies

Hover for details · We work tool-agnostically.

100 % Open Source

Keine Lizenzkosten, kein Vendor Lock-in

DSGVO-konform

Alle Tools laufen auf EU-Servern

Toolagnostisch

Das beste Tool für Ihr Problem — nicht mit Provision getrieben

bewährte Tools

in 5 Kategorien

Technologie-Rail

Airbyte

Ingestion

Data integration from 300+ source systems: open source, no vendor lock-in, community-supported

Typischer Einsatz

Data integration from 300+ source systems: open source, no vendor lock-in, community-supported

Warum gewählt

Ausgewählt für Stabilität, Wartbarkeit und Team-Fit.

Stack-Fit

Quellsysteme verlässlich anbinden

Why alpLytics

What sets us apart from typical consultancies

No claim to size: but to engineering quality, honesty, and knowledge transfer.

Engineering quality

Production-ready from the start

A data platform that is not built cleanly creates more problems over time than it solves. alpLytics builds pipelines and data models to the quality standard production systems demand: clean code, complete tests, thorough documentation. Not as an afterthought: as a project standard.

Open-source expertise

No vendor interest

alpLytics has no commercial interest in recommending a particular platform. We choose technologies solely based on technical fit, community stability, and long-term controllability by your team: and explain transparently what trade-offs each decision involves.

Transfer as project goal

Our goal: your independence

Many data platform projects end with a system that runs: but only the external consultant truly understands. alpLytics plans the handover from the start as a full project phase. The result is not dependency: it is self-sufficiency.

Who is this for?

Typical profiles

We prioritise the roles where missing data integration causes the greatest operational damage.

CTO

CTO / IT Director

Every department has its own data tool. Integrations are patchwork. New requirements take weeks because no shared foundation exists.

Typical profile

Mid-size company, 50–500 employees

Platform fit is clarified in a 30-minute initial consultation.

CEO

CEO / Managing Director

Decisions are based on data that is manually assembled. The process is error-prone, time-intensive, and depends on individual people.

Typical profile

Mid-size company with grown IT

Platform fit is clarified in a 30-minute initial consultation.

CFO

CFO / Head of Finance

Month-end closes take too long because financial data from ERP and operational data from other systems must be merged manually.

Typical profile

Mid-size company with ERP and CRM

Platform fit is clarified in a 30-minute initial consultation.

CTO / IT Director

Mid-size company, 50–500 employees

Every department has its own data tool. Integrations are patchwork. New requirements take weeks because no shared foundation exists.

CEO / Managing Director

Mid-size company with grown IT

Decisions are based on data that is manually assembled. The process is error-prone, time-intensive, and depends on individual people.

CFO / Head of Finance

Mid-size company with ERP and CRM

Month-end closes take too long because financial data from ERP and operational data from other systems must be merged manually.

Frequently asked questions

What clients usually ask

Next step

In 30 minutes you will know if a data platform is the right next step.

Free initial consultation: we analyse your current data landscape and show the most sensible first building block. No standard pitch.

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