What is a data architect?
A data architect visualizes and designs an organization’s data architecture and management framework.
In this sense, they function as a bridge between business operations and IT, designing the technical and governance architecture that will help the business to make use of their data.
A highly-effective data architect will possess a delicate balance of business understanding and technical expertise. They will be able to assess the business needs of an organization, assess how the current data governance and technology meets those needs and then draft new data architecture solutions to amend any shortcomings.
They must also be powerful communicators, able to articulate a common vocabulary across business and IT, expressing strategic requirements and championing data within the organization.
The key responsibilities of a data architect include:
- Translating business requirements into technical specifications
- Defining and governing how data is stored, consumed, integrated, and managed across the organization
- Developing data models
- Defining data standards and principles
- Defining reference architectures
- Defining and managing data flows
- Collaborating and coordinating with varied business domains, stakeholders and partners
- Communicating and championing data-first approaches within the business
This is the process of analyzing and defining the sets of data that your business produces and collects as well as the relationships between those sets of data.
High-quality data modeling underpins good analytics and business intelligence. By clearly defining all data and the relationships between them, figuring out exactly how you need to analyze the data becomes much easier.
Your data architect will need to be up-to-date with the latest modeling methods or be able to learn them quickly from industry case studies. It’s helpful to be familiar with some of the popular modeling tools, such as PowerDesigner, Enterprise Architect and Erwin.
They will need substantial skills in SQL development and database administration. They will also need to understand established data management technologies as well as other approaches such as NoSQL databases, data visualization and unstructured data.
Data governance sets the framework (context) within which your data (content) moves. It is the foundation on which data excellence is built, encompassing global data standards, processes, procedures, roles, responsibilities and so on.
Data architects are a key stakeholder in the development of effective data governance. Accordingly, your prospective data architect candidate should be familiar with the key pillars of data governance across people, process and technology, for example:
- People: team structure, skills, data ownership, accountability, communication
- Process: operating model, data standards, data protection, KPIs
- Technology: data tooling, infrastructure, cloud
Familiarity with modern approaches to data governance that emphasize federated approaches (as opposed to traditional, highly-centralized governance strategies) would be a plus if you have big ambitions for your data architecture!
Knowledge of relevant regulatory and compliance requirements is essential nowadays, as hefty penalties are being handed out for noncompliance.
Finally, they also need to understand how data governance ties into other organizational governance structures to ensure interoperability across the business.
Data analysis is one of the core pillars of any data framework, allowing data to be translated into valuable business insights.
A data architect should be familiar with popular analytics techniques as well as common tools, such as Microsoft Power BI and Tableau. Alongside, your data architect will need substantial technical data skills, including database languages (such as SQL), statistical programming languages (such as R or Python) and statistical expertise to use these tools effectively.
Ideally, they will be familiar with systems development, including system development life cycle, project management approaches and requirements, design and testing techniques.
Data lies at the heart of modern digital innovation. It’s the new source of competitive advantage for many organizations that are struggling with falling margins and growing costs.
Your prospective data architect should be aware of the criticality of data to modern innovation practices and be cognizant of how they can architect your data estate to lend itself to data-driven innovation.
Ideally, this would include knowledge of newer technical architectures that lend themselves to rapid innovation, such as the data mesh, as well as methodological approaches such as DataOps and Product Thinking.
Data standards are technical specifications that describe how data should be stored or exchanged so that they can be consistently used across different systems and users.
They are vital for ensuring the interoperability of data across your organization. A data architect must be able to develop an appropriate set of data standards for your organization, including common data standards components such as data type, identifiers, schemas, vocabulary, formats, and so on.
They should also be familiar with major data regulations that your standards will have to abide by, such as GDPR, HIPAA, PCI DSS etc.
Lastly, they will need to be keenly aware of the criticality of data standard excellence to the success of any data projects and be able to champion these within the business through workshops and awareness campaigns.
The data architect will help to establish common metadata principles that will apply to all data across the business to enable data discoverability, accessibility and interoperability. It is the glue that holds data architecture together.
They will need to be familiar with the end-to-end data lifecycle, so that they can understand how metadata is used and consumed at each point.
Likewise, they must be able to put together an overarching metadata strategy that enumerates the core standards, use metadata management tools (such as Alation, Oracle, SAP to capture and categorize metadata in line with global standards) and then, finally, be able to roll out this strategy across the organization.
Business mindset and focus on use cases
The most important principle for any data architect is that they are able to align business requirements with technical specifications.
Accordingly, they must have a firm understanding of the needs and requirements of the business. Otherwise all efforts to create new data architectures will be misaligned with actual business requirements and will be nothing more than interesting experiments.
There is a temptation to implement data technologies first and then decide how to use them, but this is a trap. A great data architect will be relentlessly focused, first and foremost, on business objectives with all technical expertise being leveraged exclusively as a means to that end.
Ideally, they will identify a specific business use case for any data architecture designs they draft in order to minimize waste and maximize measurable business outcomes.
Turning business requirements into data designs
Your candidate needs to be able to collaborate with key business and technology stakeholders to understand the business requirements and successfully translate these into the relevant data architecture: data streams, integrations, transformations, databases, and data warehouses.
Ideally, they will use Agile methodologies to optimize designs iteratively, continuously aligning the capabilities of the technology with the business goals and user needs.
Experience with Design and/or Product Thinking (treating data as a product that serves internal customers and is continuously improved in response to feedback) would be a big plus, as these approaches help to focus technology life cycles on business requirements.
Data architects function as a bridge between the business and IT. Doing so effectively makes being a clear and concise communicator an indispensable skill.
On the one hand, they need to be able to clearly articulate the capacities and limits of the organization’s technical capabilities to the business, so they understand what is possible.
On the other hand, they must be able to understand and communicate business requirements to technical teams in a way to make sure their efforts contribute to the business’ overall objectives.
They should be able to clearly articulate the value of their work to the business, have strong presentation skills, be able to make a powerful business case for the importance of data to key stakeholders and be able to ‘translate’ between technical and business stakeholders.
A first-class data architect is someone who’s able to maintain an exquisite balance between business savvy, communication and collaboration, and technical know-how.
If you’re looking for data excellence, check out our database of vetted, global technical talent that can seamlessly integrate with your existing team.