Modern data architecture services company

What is data architecture in a company?

Data architecture is the structural design that governs how data flows through your organization, from ingestion to storage, transformation, access, and analytics. It defines the standards, tools, and models that ensure data is accurate, secure, and usable across departments. Without a solid architecture, data becomes fragmented and unreliable, leading to poor decision-making and missed opportunities.

What are the most important things to consider when considering data architecture?

The most important part of data architecture is designing a system that can scale efficiently, maintain data integrity, and support business goals through reliable analytics. That includes choosing the right storage models (e.g. data lakes vs. warehouses), ensuring clean data pipelines, and setting up governance for security, lineage, and access control. When done right, it helps everyone in the company make faster, smarter decisions and sets the stage for things like analytics, AI, and compliance.

What is included in data architecture services?

Data architecture services typically include assessment of your current data environment, design of logical and physical data models, development of ETL/ELT pipelines, setup of data warehouses or lakehouses, implementation of governance and security policies, and integration with BI tools. The goal is to create a scalable, secure, and analytics-ready data ecosystem tailored to your business needs.

What are some examples of data architecture?

Some common examples of data architecture include a centralized data warehouse that consolidates data from multiple departments for unified reporting, an ELT pipeline that processes retail transactions in near real time, or a data lakehouse that merges raw IoT sensor data with curated datasets for advanced analytics. These architectures are designed to support specific business needs like forecasting, personalization, or regulatory compliance.

What is the difference between data architecture and big data architecture?

Data architecture is about organizing and managing data in traditional systems, making it easy to access and store. Big data architecture, on the other hand, is built to handle large amounts of fast-moving data. The main difference is that data architecture is for regular data management, while big data architecture is designed to scale and process massive datasets in real time.

Data architecture services

Our data architecture services

Data architecture setup  & development

Platform health checks

Data warehouse strategy

Data ecosystem implementation

AI infrastructure establishment and maintenance

Analytics process management

Case studies

Struggling with poor data quality?

How data architecture services benefit your business

Streamlined data workflow

Secure & scalable infrastructure

Real-time, reliable insights

Smarter data visualization

Automated data collection

Cost-efficient data storage

Industries we transform

Retail

Real estate

Finance

Insurance

Logistics

Advanced data architecture solutions

Our process

Take control of your data

Our data architecture tools

Why Innowise?

Choose your pricing model

Fixed price

Time & materials with a cap

Turn your data chaos into clarity

What our customers think

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