What are the most common data architecture design issues and how do you resolve them?

1 Data silos

Data silos are isolated or disconnected data sources that hinder data integration, sharing, and analysis. Data silos can result from organizational, technical, or cultural factors, such as different departments using different systems, platforms, or formats, or lacking a common data governance framework. Data silos can lead to data duplication, inconsistency, incompleteness, and inaccuracy, as well as reduced efficiency, collaboration, and innovation. To resolve data silos, you need to establish a data strategy that aligns with your business goals and objectives, and implement a data integration solution that can connect and consolidate data from different sources, such as data warehouses, data lakes, or cloud services. You also need to adopt a data governance model that defines the roles, responsibilities, policies, and standards for data access, quality, security, and compliance across the organization.

2 Data complexity

Data complexity refers to the difficulty of understanding, managing, and using data due to its volume, variety, velocity, veracity, or value. Data complexity can pose challenges for data architecture design, as it requires scalable, flexible, and robust solutions that can handle different types and sources of data, as well as changing business needs and expectations. Data complexity can also affect data quality, performance, and usability, as it can introduce errors, delays, or inconsistencies in data processing and analysis. To resolve data complexity, you need to adopt a data modeling approach that can capture and represent the structure, relationships, and semantics of data, as well as a data quality framework that can monitor, measure, and improve data quality. You also need to leverage data engineering tools and techniques that can optimize data storage, processing, and delivery, such as data partitioning, compression, indexing, caching, or streaming.

3 Data security

Data security is the protection of data from unauthorized access, use, modification, or disclosure. Data security is a vital aspect of data architecture design, as it ensures the confidentiality, integrity, and availability of data, as well as the compliance with legal and ethical regulations. Data security can be challenging for data architecture design, as it involves balancing the trade-offs between data access and data protection, as well as addressing the risks and threats from internal or external sources, such as hackers, competitors, or employees. To resolve data security issues, you need to implement a data security strategy that defines the data classification, encryption, authentication, authorization, auditing, and backup policies and procedures for your data architecture. You also need to use data security tools and solutions that can enforce data security policies and prevent or detect data breaches, such as firewalls, antivirus software, or intrusion detection systems.

4 Data usability

Data usability is the degree to which data can be easily and effectively used for its intended purpose by its intended users. Data usability is a key outcome of data architecture design, as it enables data-driven decision making, insights, and actions. Data usability can be influenced by various factors, such as data quality, accessibility, relevance, timeliness, or format. Data usability can be a challenge for data architecture design, as it requires understanding the needs, preferences, and behaviors of data users, as well as providing them with the appropriate data tools and interfaces. To resolve data usability issues, you need to conduct a data user analysis that identifies the data user segments, personas, scenarios, and requirements for your data architecture. You also need to design and implement a data presentation layer that can deliver data to users in a clear, concise, and consistent manner, such as dashboards, reports, or visualizations.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?