Data Architecture

Foundation for Sustainable Data Management

A well-thought-out data architecture forms the backbone of successful data management. It defines how data is captured, stored, integrated, transformed, and used to efficiently meet business requirements and enable innovation.

Do These Data Challenges Sound Familiar?

Lack of Transparency

About existing data stocks and their quality

Data Silos

Redundant data storage leading to inconsistencies

Integration Gaps

Insufficient integration of new data sources like IoT

Compliance Issues

Difficulties in complying with data protection requirements

Slow Data Delivery

For analysis and reporting

Quick Check

Data Strategy Assessment Light

You want to know how your company's data strategy stands but have little time? Our Assessment Light is the perfect starting point to evaluate your data maturity.

Data Management Assessment

Your Quick Maturity Check

Get started in minutes

The 4-Phase Journey to the Right Data Architecture

We accompany you through every step of your data architecture transformation.

1
Phase 1

Analysis & Goal Definition

We capture your business requirements, analyze existing data landscapes, and identify weaknesses and potential for improvement.

2
Phase 2

Conceptualization & Modeling

We develop a customized data architecture blueprint – from conceptual to logical to physical data models. Standards, guidelines, and governance are clearly defined.

3
Phase 3

Implementation & Integration

We implement the architecture, integrate relevant data sources, establish ETL processes, and ensure consistent data management across your organization.

4
Phase 4

Quality Assurance & Optimization

We check data quality, establish monitoring, and continuously optimize the architecture to flexibly support future requirements.

Results and Artifacts

Documented as-is analysis of the data landscape
Target picture and blueprint of data architecture (including data models and data flows)
Detailed integration and migration plans
Guidelines and standards for data management and governance
Implemented data platforms (e.g., Data Warehouse, Data Lake)
Monitoring and quality reports on data availability and consistency

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FAQ on Data Strategy

What exactly is a data strategy and why is it important for my company?

A data strategy is a comprehensive plan that defines how your company captures, stores, manages, shares, and uses data as a strategic asset to achieve business goals. It's important because it helps break down data silos, make informed decisions, optimize processes, ensure compliance, and ultimately gain competitive advantages. Without a strategy, data often remains untapped potential or can even lead to inefficiencies and risks.

Is a data strategy also relevant for small and medium-sized enterprises (SMEs)?

Yes, absolutely. SMEs in particular can benefit enormously from a clear data strategy. It enables more efficient operations, better customer knowledge, and faster responses to market changes – often with fewer resources than large corporations. A data strategy tailored to SMEs ensures that data investments are targeted and deliver maximum benefit.

What is the process for developing a data strategy with Gotik?

Our approach is collaborative and customized. We typically start with an assessment to understand your current situation, challenges, and goals. Based on this, we develop the strategy together with you, define concrete measures, and create an implementation roadmap. We emphasize that the strategy is practical and fits your company.

Is it only about the strategy on paper, or does Gotik also support with implementation?

We accompany you beyond pure strategy development if desired. Our offering also includes support for implementing data management processes, introducing governance structures, integrating systems, and training your employees. Our goal is to ensure your data strategy is sustainably embedded in the company and delivers measurable results.

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