Data Architecture Consulting

Name: leverX Company
Brand: LeverX
Rating: 4.9 (114 reviews)

When data moves predictably, decisions follow confidently. Our data architecture consulting services structure your environment for accuracy, traceability, and control.

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As an SAP Gold Partner, we provide access to resources and seasoned professionals, ensuring your project's reliability. Our expertise helps us tailor SAP solutions for your unique needs, optimizing ROI and efficiency.

Partnering with LeverX as an SAP Global Strategic Supplier goes beyond buyer-seller dynamics. We provide critical solutions for global operations, collaborate with you on product development, and drive strategic initiatives.

LeverX is a certified partner of the SAP Center of Expertise. This accreditation ensures our unwavering commitment to upholding SAP's highest standards. Our mission is to deliver excellence, providing unparalleled support, and facilitating the seamless integration of SAP solutions into your business framework.

As an SAP AppHaus Network Member, we help businesses solve complex problems, deliver exceptional user experiences, and drive digital transformation using design thinking and innovation expertise. We leverage SAP's best practices and cutting-edge tools to create user-centric solutions.

LEVERX - YOUR RELIABLE DATA ARCHITECTURE PARTNER

LeverX is a global technology consultancy with over 20 years of experience designing, building, and optimizing enterprise data systems. Our data architecture consulting services help organizations establish structured, scalable environments for data collection, processing, and analysis. We align system architecture with business objectives, ensuring data integrity across cloud, on-premise, and hybrid platforms. The result is a resilient framework that supports analytics, integration, and digital transformation initiatives with measurable reliability.

1,500+

successful projects

offices, 10+ countries

2,200+

employees

700+

certified experts

When data architecture fails, it rarely collapses at once. It weakens quietly — through duplicated reports, unclear ownership, and systems that no longer scale with business growth. These gaps may slow analytics, erode trust in data, and increase the cost of every decision that depends on it.

Expert data architecture consulting helps organizations restore control and consistency. Here are the main challenges we solve:

Inconsistent reporting. When data definitions differ across systems, reports tell conflicting stories. As part of our consulting services, we align data models, ensure a single source of truth, and make reporting predictable.
Scalability issues. Legacy platforms often reach their limits as data volumes expand. A modern architecture separates compute from storage, automates scaling, and supports both structured and unstructured data. As a result, the system grows with the business instead of restraining it.
Weak governance. Without clear ownership, access rules, and audit controls, data becomes a security risk. Strong architecture embeds governance principles into system design. That ensures compliance, controlled access, and traceable data movement.
Fragmented infrastructure. Data stored across disconnected applications slows every analytical process. Architecture consulting creates a unified framework where data flows through well-defined pipelines. This improves system interoperability and reduces integration costs.

Our Data Architecture Consulting Services

Data architecture defines how a business sees, stores, and uses its information. Here is what we offer to help organizations create scalable, reliable, and analytics-ready environments.

Data assessment and strategy

Many companies don’t have a data problem — they have a data map problem. Information exists, but no one knows where it lives or how it is connected. Our team conducts a full assessment of current systems, data sources, and workflows. We define a strategy that outlines what to keep, what to modernize, and what to eliminate. As a result, you have a roadmap for turning data into a structured, governed, and future-ready asset.

Data architecture design

An architecture is more than storage diagrams. It is the logic behind how data moves, transforms, and supports business processes. We design architectures that define data models, integration layers, and processing patterns. Each element aligns with specific business and performance goals — whether for transactional speed, analytical depth, or scalability.

Data lake and big data architecture

Conventional warehouses begin to strain as your data volume increases. A well-designed data lake or big data architecture can prevent this bottleneck. Our team builds environments that handle structured, semi-structured, and unstructured data across multiple platforms. This creates a foundation for machine learning, advanced analytics, and long-term data retention without performance loss.

Data integration & ELT/ETL

Disconnected systems lead to inconsistent insights. In our data integration approach, we establish stable pipelines for continuous data movement between ERP, CRM, analytics, and external platforms. We design and implement ETL and ELT processes tailored to data complexity and performance requirements. This ensures timely, accurate, and reliable data flow across the enterprise.

Data migration and legacy system modernization

Migrating data is not just about transfer; it’s about translation. Old structures rarely fit modern platforms directly. We plan and execute different migrations 一 from legacy databases and on-premise systems to modern cloud or hybrid environments. Along this way, we resolve compatibility issues, cleanse outdated records, and ensure business continuity throughout the transition.

Data analytics and BI

Analytics depends on what happens behind the dashboard. We help build analytical environments that translate raw data into actionable intelligence. Our consulting covers model design, data preparation, and BI platform setup. Thanks to accurate, well-structured inputs, reporting becomes faster, clearer, and more meaningful.

Data warehousing

Data without structure remains unused. Our experts design and implement warehouses that centralize, standardize, and prepare information for analytical use. We optimize schema design, storage management, and query performance. The outcome is a dependable analytical core that delivers accurate reports and supports advanced visualization tools.

Data quality assessment and improvement

Poor data quality undermines every decision it touches. So, we assess data accuracy, completeness, and consistency across systems. Then, to detect and correct errors, we apply rules, validation frameworks, and automated checks. This process builds lasting trust in the information used for operations and analytics.

Data governance and compliance

Without governance, even accurate data creates risk. We help organizations establish policies, access controls, and audit mechanisms, which ensures data is handled responsibly. Our consultants align governance structures with compliance standards such as GDPR and HIPAA. As a result, you have a transparent system where every data action is traceable and secure.

Enhanced data quality and consistency

When every source speaks the same data language, confusion disappears. Clean, consistent inputs mean fewer errors and faster insights.

Trusted data for smarter decisions

Reliable architecture removes hesitation. Decisions come from verified facts, not conflicting reports or gut instinct.

Effective data governance

Governance becomes effortless when built into design. Access, compliance, and accountability happen by structure, not enforcement.

Proactive data quality monitoring

Quality shouldn’t be checked once — it should be watched constantly. Automated controls detect issues before they distort results.

Accelerated technology adoption

New tools deliver value only if the foundation supports them. A flexible architecture makes innovation a matter of integration, not reconstruction.

Improved data discoverability and collaboration

Data is only useful when people can find it. Clear structures and metadata turn scattered assets into shared knowledge.

Cloud-native first

We use cloud platforms like Azure, AWS, and others. They help scale seamlessly, reduce infrastructure overhead, and cut deployment time. Thanks to our partnerships with these providers, we always have access to advanced services, technologies, and enterprise-level support.

Lakehouse & unified data platforms

Data rarely fits neatly into tables. We unify structured, semi-structured, and unstructured data in such platforms as Databricks, Snowflake, or Delta Lake. This creates a single environment for analytics, AI, and advanced BI workloads.

API-first and microservices design

To improve system interoperability and adaptability, we implement API-driven, microservices architectures. Thanks to this, your systems connect without friction, components can be reused, and applications adapt as business needs change.

AI & machine learning integration

We prepare data pipelines to support predictive analytics, machine learning, and intelligent automation. Models require reliable, contextual data. Our pipelines deliver clean, structured inputs for consistent performance.

Data fabric and mesh principles

LeverX applies data fabric and mesh concepts to create decentralized but governed architectures. As a result, business units can self-serve data. While IT maintains quality, compliance, and oversight, it thus reduces bottlenecks and increases agility.

Automation by default

Manual pipelines introduce risk and delay. We embed automation across pipelines, metadata management, lineage tracking, and data quality monitoring. Thus, manual processes are minimized and errors are detected earlier, and efficiency and reliability increase across all systems.

Discovery and audit

Current environment analysis: Review existing databases, pipelines, applications, and technical dependencies.
Data quality assessment: Identify inconsistencies, gaps, or errors across data sources.
System performance evaluation: Highlight bottlenecks, scalability limits, or integration challenges.
Compliance and security review: Examine current practices against regulatory requirements and internal policies.

Step 1

Strategy and roadmap

Architecture strategy definition: Decide which systems to modernize, integrate, or rebuild.
Prioritization and planning: Identify high-impact areas, risks, dependencies, and quick wins.
Resource and tool selection: Determine team size, skills, and technologies required.
Roadmap creation: Develop a phased plan with timelines, milestones, and measurable success criteria.

Step 2

Design and implementation

Architecture design: Define data models, storage layers, integration patterns, and governance frameworks.
Pipeline and platform setup: Configure ETL/ELT processes, data lakes, warehouses, or lakehouse solutions.
Integration design: Plan reliable connections with ERP, CRM, analytics platforms, APIs, and external data sources.
Execution: Build, configure, or modernize systems according to the approved architecture plan.

Step 3

Validation and optimization

Testing and verification: Ensure data consistency, accuracy, security, and system performance.
Pilot validation: Run proofs of concept for critical components or workflows to validate architecture.
Performance tuning: Adjust pipelines, storage, and processing to meet performance and scalability requirements.
Refinements: Incorporate feedback and lessons learned into the final design and deployment.

Step 4

Continuous improvement

Monitoring and observability: Track system performance, data quality, and pipeline health using dashboards and alerts.
Incremental enhancements: Introduce new integrations, automation, or analytics capabilities as needs evolve.
Governance and compliance updates: Maintain policies, access control, and auditability.
Knowledge transfer: Provide documentation, best practices, and training to empower internal teams.

Step 5

Industries We Serve

Our experience spans multiple industries where we’ve learned what truly drives results and what pitfalls to avoid when managing data at scale.

Industrial Manufacturing

Telecommunications

Oil, Gas, and Energy

Transportation and Logistics

Pharmaceuticals & Life Science

Why LeverX?

Proven track record

For over 20 years, we have helped businesses worldwide succeed with SAP. We’ve already completed 1,500+ projects for over 900 clients, including top names on the Fortune 500 list.

Industry experts

The LeverX team comprises professionals with hands-on knowledge in 30+ industries, including manufacturing, logistics, and oil and gas.

Quality and security track record

LeverX operates in compliance with international standards such as ISO 9001, ISO 27001, ISO 22301, and ISO 55001, ensuring reliability and quality in every project.

Investment in innovation

We actively integrate advanced technologies, such as Data Science, IoT, AI, Big Data, Blockchain, and others, to help clients efficiently address their business challenges.

SAP, AWS, Microsoft, Snowflake, and Databricks partnerships

LeverX partners with SAP, AWS, Microsoft, Snowflake, and Databricks to deliver end-to-end implementations and unified data environments. This ensures future-ready data management across cloud and hybrid ecosystems.

Flexibility

Our team is available 24/7, which enables us to quickly deploy projects, maintain process transparency, and adapt each development phase to meet your specific requirements.

Frequently Asked Questions

Why is data architecture important for business strategy?

Because strategy runs on information, a well-built data architecture connects the essentials: systems, people, and insights. As a result, you know what is going on in your organization. What is more important, all your decisions are based on facts.

How much time does it usually take to design and implement a new data architecture?

Many factors can influence the project duration. The most important are your landscape and goals. Smaller environments take a few months. Enterprise transformations can take anywhere from a year to several years. What matters most here is doing it not fast, but right. This determination ensures scalability and long-term stability.

How do I know if my current data architecture needs improvement?

If reports are slow, integrations break, or teams spend more time fixing data than using it — that’s a signal. These are the most obvious signs. Also, modernization becomes urgent when your systems can no longer keep up with business speed.

How do you ensure a data architecture remains future-proof?

By designing for change. Thanks to our modular, cloud-native, and API-first approaches, we keep systems ready for new tools, data sources, or even business models.

1

An expert will reach out to you to discuss your specific needs and requirements.
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We'll sign an NDA to ensure any sensitive information is kept secure and confidential.
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We'll work with you to prepare a customized proposal based on the project's scope, timeline, and budget.