Modern data architecture

Modern data architecture is a way of organizing and managing data in a current and efficient way. It involves using the latest technologies and strategies to handle large amounts of diverse data. Key elements include seamless data integration, building centralized data warehouses for analysis, ensuring high availability with minimal downtime, and adapting to the evolving needs of businesses. This approach enables organizations to make better use of their data for decision-making and stay agile in a rapidly changing digital landscape.


Important Reads

Data democratization

Data democratization means making sure that everyone in the organization, regardless of their technical skills, can work with data comfortably. It involves empowering individuals across different roles and departments to access, analyze, and utilize data for decision-making purposes and as a result, build a customer experience powered by data. 

Key aspects of data democratization include: 

  • Data accessibility: Data democratization aims to enable users to easily access relevant data. This involves providing user-friendly interfaces, self-service tools, and secure data access mechanisms. Users should be able to retrieve data without relying on IT teams or specialized technical skills. 
  • Data literacy and training: To enable data democratization, organizations must train users on how to work with data effectively. Training programs can cover data analysis, visualization, interpretation, and basic data governance principles. 
  • Self-service analytics: Self-service analytics tools and platforms empower users to perform data analysis, create reports, and generate insights independently, without heavy reliance on IT or data experts. These tools provide features like intuitive interfaces, drag-and-drop functionality, and pre-built templates to simplify data exploration and analysis. 
  • Data governance and security: Data democratization should be implemented together with proper data governance and security measures. Organizations need to establish policies, guidelines, and controls to ensure data privacy, security, and compliance. This includes defining access controls, data classification, and monitoring mechanisms to safeguard sensitive information. 
  • Collaboration and sharing: Data democratization encourages collaboration and sharing of data insights among users. Collaboration platforms and data visualization tools enable users to share analysis results, reports, and dashboards with colleagues, promoting knowledge sharing and informed decision-making across the organization. 
  • Cultural shift: Data democratization requires a cultural shift towards a data-driven mindset. Organizations need to foster a culture that values data, promotes data literacy, and encourages employees to base their decisions on data rather than intuition or personal biases. 

The benefits of data democratization include faster decision-making, improved innovation, increased productivity, and better alignment between business objectives and data insights. However, it’s important to strike a balance between data accessibility and data security. 

Data mesh

Data mesh is a relatively new concept and architectural approach for managing and scaling data in large and complex organizations. It was introduced by Zhamak Dehghani, a data and software architect at ThoughtWorks, in a 2020 article titled ”How to Move Beyond a Monolithic Data Lake to a Distributed data mesh. 

The fundamental idea behind data mesh is to treat data as a product and to apply principles of domain-driven design and decentralization to data management. It aims to address some of the challenges and limitations associated with traditional monolithic data architectures, such as large, centralized data lakes or warehouses. Here are some key concepts of data mesh: 

  1. Domain-oriented ownership: In a data mesh, data is treated as a domain-specific product. Each domain or business unit takes ownership of its own data, including its quality, accessibility, and management. 
  2. Decentralized data ownership: Instead of having a single central data team responsible for all data, a data mesh advocates for decentralized data ownership. Cross-functional teams within different domains are responsible for their own data pipelines, data quality, and data sharing. 
  3. Data product teams: Each domain forms its own data product team, which includes data engineers, data scientists, domain experts, and other relevant roles. These teams are accountable for the end-to-end lifecycle of their data products. 
  4. Data as a service: Data products are treated as services, available for consumption by other parts of the organization. These services provide standardized interfaces and APIs for accessing and using the data. 
  5. Data mesh architecture: A data mesh architecture involves breaking down data processing into smaller, more manageable units called data domains. Each data domain is responsible for its own data processing, storage, and serving. The architecture supports a distributed, scalable, and modular approach to data management. 
  6. Data mesh principles: Data mesh principles include autonomy (each domain has control over its data), data as a product (data is treated with the same care as software products), and federation (data is shared and consumed across domains). 
  7. Data platform: A data mesh includes a data platform that provides tools, services, and infrastructure for managing data products, ensuring data quality and enabling data discovery and consumption. 


The goal of data mesh is to enable organizations to scale their data efforts more effectively by distributing data ownership, improving data quality, and facilitating collaboration among cross-functional teams. It’s important to note that implementing a data mesh approach requires significant organizational and cultural changes, as well as the adoption of appropriate technologies and practices to support domain-driven data management. 

Near-zero downtime

Near-zero downtime refers to the goal of achieving minimal or negligible interruption to business operations during system maintenance, upgrades, or other activities. It aims to minimize or eliminate the need for planned system downtime, allowing organizations to maintain continuous availability and uninterrupted service to their users. 

It is important to note that achieving near-zero downtime requires careful planning, thorough testing, and a robust infrastructure. Organizations must consider their specific requirements, system architecture, and business priorities when implementing strategies to minimize downtime in SAP systems. Regular system monitoring, performance tuning, and proactive maintenance play crucial roles in maintaining continuous availability and providing a seamless user experience. 

Here you can read a customer success story on migrating two exceptionally large ERP systems with only 14 hours of downtime.