How do you identify the roles of a data architect?

1 Data architect domains

Data architects typically work across three main domains: business, technical, and data. The business domain involves understanding the business context, objectives, and requirements of the data projects, as well as communicating and collaborating with stakeholders and users. The technical domain involves designing and developing the data solutions, such as databases, data warehouses, data pipelines, and data platforms, using various tools, technologies, and methodologies. The data domain involves managing and modeling the data itself, such as defining the data sources, formats, standards, and schemas, as well as ensuring the data quality, integrity, and security.

2 Data architect skills

Data architects need a combination of skills that span the three domains to perform their roles effectively. Essential skills include data modeling, which involves creating conceptual, logical, and physical models of data structures and documenting and communicating them using various techniques and notations. Data analysis involves understanding, interpreting, and manipulating data, as well as identifying and resolving data issues. Data engineering requires designing and implementing data solutions using various tools, technologies, and methodologies. Data governance involves defining and enforcing data policies, standards, and rules to ensure data quality, security, and compliance. Finally, data strategy involves aligning the data solutions with business goals and needs, as well as evaluating and recommending best practices for the data architecture.

3 Data architect challenges

Data architects face many challenges in their roles, such as data complexity, integration, quality, scalability, and innovation. The data landscape is becoming increasingly diverse, dynamic, and distributed, with different types and sources of data, such as structured, unstructured, streaming, and geospatial data. Furthermore, data needs to be exchanged securely across different systems and applications while supporting transformation and enrichment processes. Data quality must be ensured with accuracy, completeness, consistency, and timeliness. Additionally, the data architecture needs to scale with growing and changing data volumes while providing high-speed and high-availability data solutions. Finally, the data architecture should adapt to emerging and evolving trends and technologies such as AI, machine learning, data science, and data visualization to enable new capabilities.

4 Data architect opportunities

Data architects have many opportunities in their roles that can help the organization achieve strategic and operational goals, such as improving customer experience and enhancing decision making. Data architecture also promotes a data-driven culture, where data is recognized as a valuable asset and data literacy, awareness, and collaboration are supported. Furthermore, it provides data architects with diverse projects and the opportunity to expand their knowledge and skills. This can help them advance their career and professional development.

5 Data architect roles

Data architects can have different roles depending on the size, scope, and complexity of the data projects and the organization. For instance, an enterprise data architect is responsible for defining and overseeing the overall data architecture of the organization, in addition to aligning it with the business strategy and vision. They also coordinate and guide other data teams in the organization, while establishing and enforcing the data governance framework and standards. Solution data architects design and implement specific data solutions for individual projects or initiatives, like data warehouses, data lakes, or data platforms. They collaborate with business analysts, data engineers, and data analysts to understand requirements and deliverables. Furthermore, domain data architects manage and model the data within a domain or subject area - such as finance, marketing, or sales - while guaranteeing consistency and quality of the data within that domain. Additionally, they support integration and analysis processes between domains.

6 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?