Data Architecture
Future-proof your data-centric architectures, drive frictionless data-driven transformation.
Picture a sprawling city, its buildings, roads, and utilities interconnected, each element playing a vital role in its overall function. A city without a well-defined plan would become chaotic. Like a city, your data needs a robust architecture for smooth operation and future adaptability.
As a data architecture consulting firm, Dataiso partners with you to deliver a data architecture tailored to your evolving needs. We simplify your data landscape, ensuring accessibility, reliability, and security.
Your challenges
Your challenges
A solid data architecture forms the backbone of effective data management strategies, essential for every successful data-driven organization. To build reliable and scalable data environments that unlock the full potential of data assets, it incorporates key technical areas such as data infrastructure, data governance, data modeling, data quality, data integration, and data security.
A strong data foundation enables valuable insights and informed decisions. However, the volume and variety of data create significant organizational challenges, requiring careful consideration of architectural complexities and user needs.
At Dataiso, we understand the unique challenges organizations face when integrating data architectures.
Omnipresence of data silos
Traditional departmental silos hinder data accessibility, compromising security. Many organizations lack well-designed data architectures, leading to weak data foundations for the modern era.
Increasingly complex data storage
Data volumes double every two years, constantly pushing technology boundaries. This rapid growth results in costly investments in advanced data management solutions.
Poor level of data quality
High-quality data is crucial for operational success. Several organizations struggle to ensure accuracy and consistency across diverse data sources, hindering effective data architectures.
More challenging data scaling
Scalable data architectures can handle increasing data loads efficiently. Unfortunately, many organizations inherited poorly designed data architectures, resulting in performance bottlenecks and slow response times.
Lax security and data protection
Data offers a treasure trove of insights for sharper decision-making to achieve success. However, data breaches are often underestimated, posing multiple risks for data architectures.
Our key factors of success
Our key factors of success
Scalability, performance, and future-proofing of your data infrastructure depend on a well-designed data architecture that mirrors your data strategy. Learn about the critical success factors that Dataiso prioritizes
Organizations that unify their data silos achieve a significant competitive advantage. A well-designed data-centric architecture provides a holistic business view, enabling users to access and analyze correlating valuable data signals across functions and enhance operational efficiency.
While a well-designed data architecture enables a shared ecosystem, a common data language is essential for its success. This unified vocabulary ensures consistent data analysis and understanding, maximizing the data architecture’s value.
Delivering the right data to the right users at the right time requires accurate, relevant, and timely insights. A contextualized data architecture is key, enabling easy consumption and clear visualization for informed decision-making.
Security is crucial from the start. A user-centric approach defines robust requirements upfront, meeting all users’ needs. This proactive method embeds security into the core data architecture, protecting raw data from the source.
A well-designed data architecture relies on a clear understanding of the data landscape. Data quality is key, as connecting systems reveal hidden issues. Therefore, ensuring quality data is crucial for achieving desired results.
Evolving organizations need adaptable data architectures. While traditional relational models can be inflexible, a well-designed architecture can accommodate new data types and user needs, effectively handling large data volumes.
A data-driven culture demands more than a single repository. Scalable data architectures combine modern data warehouses, data lakes, and databases to gain deeper insights and improve performance, exceeding traditional limitations.
Our approach
Our approach
As part of the design or improvement of a data architecture, we adopt an agile approach based on Test & Learn in order to cope with a growing evolution of the world of data, and the associated needs.
Through the Agile Test & Learn methodology, we proceed in two stages:
Test one or more hypotheses
...such as measuring the ROI (Return On Investment) of a specific data architecture component.
Analyze the results of the tests
...taking into account user feedback as well.
To ensure its effectiveness, we based this work approach on a People-Process-Technology (PPT) framework. We leverage this collaborative model to help you drive successful data architecture initiatives.
Our services
Our services
Dataiso provides cutting-edge data architecture services to help organizations achieve real-world results. We go beyond theoretical methods, delivering bespoke solutions that address your specific challenges and unlock new opportunities.
Data architecture strategy and roadmap
- Maximize return on investment (ROI) by aligning data architecture objectives with the overall strategy.
- Drive growth by identifying high-impact opportunities where data architectures can make a significant difference.
- Develop a comprehensive data architecture roadmap for successful implementation strategies.
- Define the appropriate data architecture technologies and tools to meet unique business needs and drive innovation goals.
- Strengthen data architecture scaling strategies by implementing data operations (DataOps) principles.
- Demonstrate data architecture value through compelling proofs of concept (PoCs) and proofs of value (PoVs).
Data architecture audit and diagnosis
- Assess all existing data architecture practices, policies, and technologies.
- Identify gaps between the organization’s current state and data architecture best practices, including both technical and functional discrepancies.
- Assess data architecture health and observability, including pipelines, quality, consistency, and accessibility.
- Evaluate data architecture systems’ strengths and weaknesses using methods like performance testing, and user feedback.
- Review data architecture ethics, sustainability, security, privacy, and compliance.
- Benchmark data architecture maturity against industry standards with proven maturity models.
- Maximize data architecture investments through efficient optimization plans.
Database architecture design
- Define tailored database architectures for on-premise, cloud, or hybrid environments using 1-tier, 2-tier, and N-tier models.
- Design the most suitable database management system (DBMS) for database storage, such as relational databases (RDBMS), object databases (ODBMS), NoSQL databases, vector databases (VDBMS), and other modern databases (e.g., NewSQL, HTAP).
- Establish high performance and scalability guidelines for OLTP (OnLine Transaction Processing) and HTAP (Hybrid Transactional/Analytical Processing) workloads.
- Implement database architecture optimization strategies with database developers and database administrators (DBAs), including partitioning, indexing, caching, querying, and resource allocation.
- Foster a strong, resilient database engineering culture for performance, integrity, security, and usability of database architectures.
- Ensure database architecture security and compliance with various regulatory requirements.
Business intelligence (BI) architecture design
- Define tailored BI architectures for on-premise, cloud, or hybrid environments using traditional, modern, self-service, and hybrid BI models.
- Accelerate time-to-insight with optimized data warehouse and data mart layers.
- Build scalable data ingestion and transformation layers with robust ETL/ELT pipeline frameworks.
- Enable actionable decision-making with analytical processing and reporting layers.
- Establish high performance and scalability guidelines for OLAP (OnLine Analytical Processing) and HTAP (Hybrid Transactional/Analytical Processing) workloads.
- Implement BI architecture optimization strategies with BI analysts, BI developers, and BI administrators, including partitioning, caching, analytical querying, and resource allocation.
- Embrace a strong, resilient BI culture for performance, integrity, security, and usability of BI architectures.
- Ensure BI architecture security and compliance with various regulatory requirements.
Big data and analytics architecture design
- Define tailored big data and analytics architectures for on-premise, cloud, or hybrid environments using Kappa, Lambda, and Data Lake models.
- Enable querying with scalable storage layers incorporating data lakes, data warehouses, and databases (e.g., NoSQL, NewSQL).
- Accelerate data transformations with robust ingestion and processing layers.
- Empower decision-making with augmented analytics and visualization layers.
- Establish high performance and scalability guidelines for OLTP (OnLine Transaction Processing), OLAP (OnLine Analytical Processing), and HTAP (Hybrid Transactional/Analytical Processing) workloads.
- Implement big data and analytics architecture optimization strategies with data analysts, data engineers, analytics engineers, data scientists, and big data administrators, including partitioning, caching, querying, and resource allocation.
- Facilitate a strong, resilient big data and analytics culture for performance, integrity, security, and usability of big data and analytics architectures.
- Ensure big data and analytics architecture security and compliance with various regulatory requirements.
Cloud data architecture design
- Define tailored cloud architectures for IaaS, PaaS, and SaaS using AWS, Azure, GCP, and more.
- Deliver high availability and scalability through fault-tolerant cloud data storage, processing, and analytics layers.
- Strengthen flexibility with hybrid and multi-cloud strategies.
- Establish high performance and scalability guidelines for OLTP (OnLine Transaction Processing), OLAP (OnLine Analytical Processing), and HTAP (Hybrid Transactional/Analytical Processing) workloads.
- Implement cloud data architecture optimization strategies with cloud architects, cloud engineers, and data professionals, including partitioning, caching, querying, and resource allocation.
- Establish a strong data-driven cloud culture for the performance, integrity, security, and usability of cloud data architectures.
- Ensure cloud data architecture security and compliance with regulatory requirements.
Modern data architecture design
- Unify data lake and data warehouse capabilities by designing robust data lakehouse architectures.
- Enable advanced analytics for actionable data insights, driving informed decision-making and innovation.
- Streamline data exchange across the organization by building scalable data hub architectures.
- Ensure data consistency and accessibility with centralized data governance and management, fostering data-driven decision-making.
- Enable domain-oriented, distributed data ownership by developing flexible data mesh architectures.
- Empower business and data teams with effective data collaboration and knowledge-sharing capabilities, fostering data-driven culture.
- Seamlessly connect disparate data across complex environments by creating efficient data fabric architectures.
- Ensure real-time data availability and accessibility with optimized data integration processes, enabling informed decision-making.
- Unify data-driven cloud platforms for flexible data management by implementing resilient data cloud architectures.
- Enhance reliability and scalability by leveraging cloud-native services and tools for cost-effective data management.
- Establish high performance and scalability guidelines for OLTP (OnLine Transaction Processing), OLAP (OnLine Analytical Processing), and HTAP (Hybrid Transactional/Analytical Processing) workloads.
- Implement modern data architecture optimization strategies with cloud and data professionals, including partitioning, caching, querying, and resource allocation.
- Promote a strong data-driven culture for performance, integrity, security, and usability of modern data architectures.
- Ensure modern data architecture security and compliance with regulatory requirements.
Data integration architecture design
- Define tailored data integration architectures for on-premise, cloud, or hybrid environments using batch (e.g., ETL, ELT), real-time (e.g., streaming, event-based), cloud-based, and hybrid models.
- Streamline data transformations and real-time workflows through scalable data integration pipelines.
- Empower intelligent integration with AI.
- Establish high performance and scalability guidelines for data integration workloads.
- Implement data engineering architecture optimization strategies with data integration analysts, ETL/ELT developers, and data integration engineers, including data pipeline and workflow optimizations, data caching and indexing, and data quality assurance (DQA).
- Nurture a strong data-driven culture for performance, integrity, security, and usability of data integration architectures.
- Ensure data integration architecture security and compliance with regulatory requirements.
Data security architecture design
- Define robust data security frameworks using threat modeling, risk assessments, and data classification.
- Fortify data architectures with threat management, including Zero Trust and cloud security.
- Align policies with industry standards like ISO 27001.
- Enable robust data access controls through robust identity and access management (IAM) methods, including role-based access controls (RBAC).
- Enforce granular access controls by applying the principle of least privilege for users.
- Safeguard sensitive data with advanced encryption techniques such as AES, RSA, and more.
- Strengthen data confidentiality and integrity through data encryption at rest and in transit.
- Ensure key management practices for effective encryption.
- Prevent data breaches through advanced data protection, including tokenization and data masking.
- Enforce stringent data privacy and compliance for data architectures with regulations like GDPR, CCPA, HIPAA, PCI DSS, and industry standards like ISO 27001, ISO 25012, and ISO 8000.
- Deliver regular privacy impact assessments (PIAs) for better risk management.
- Ensure business continuity through data loss prevention (DLP) and recovery plan (DRP) frameworks.
- Foster a security-conscious culture for data architectures through training and awareness programs.
- Stay ahead of emerging threats by exploring modern data security architectures like Data Security by Design and DataSecOps.
Data architecture migration
- Assess data architecture migration requirements for cloud or on-premise deployments through detailed assessments like gap analysis, risk assessment, and more.
- Evaluate compatibility, scalability, and performance of existing data architectures using efficient benchmarking and stress-testing methods.
- Build comprehensive data architecture migration plans addressing technical, operational, and business specifications.
- Implement comprehensive cutover and rollback plans, leveraging robust testing and validation methods.
- Seamlessly migrate data assets to the target platform, with minimal disruption and risks.
- Leverage enhanced features and patches, by ensuring security and reliability with upgraded platform versions.
- Validate data integrity and quality post-migration for better accuracy, completeness, and consistency of business-critical information.
Our benefits
Our benefits
- Data-driven decision making
- Scalable and future-ready data infrastructure.
- Reduced operational costs.
To navigate the complex landscape of data-driven business, a well-designed data architecture is essential for success. Unfortunately, organizations often struggle to select the right strategies and resources (e.g., technologies, talents) for building or optimizing data-centric architectures, leading to suboptimal outcomes.
At Dataiso, we collaborate with organizations to build robust, future-proof data architectures. Contact us to discuss your specific needs and transform your data landscape.