Let’s start off with the fact that data architects are not data engineers. These roles are often confused. A data architect has a very distinct set of responsibilities that do not overlap with those of data engineers, mainly because data architects are answerable to a wider set of stakeholders within the organization.

The main responsibility of a data architect is to stay close to the needs of the users of data within the company – like data analysts, data scientists, and users of data driven applications, including customer support staff, operations and marketing staff, and even end customers of the company.

The secondary responsibility of data architects is to ensure alignment of enterprise data architecture with the overall enterprise architecture and business goals. The role of a data architect is a very senior role, and involves lots of stakeholder management, conflict resolutions, strategy, organization and governance.

Lets try to simplify these responsibilities by using very specific examples of how data architects can cater to the needs of their users.

Data security

Data architects need to be sensitive towards the security concerns of organizational data, and should audit various applications within the enterprise to ensure that the security, privacy and confidentiality aspects of data are adequately handled.

Data Integrity

Data architects need to ensure that the data within the enterprise is integrated correctly to reflect the reality of the business. They should ensure that the data used within the organization is of good quality, and should put checks in place to ensure that the data is accurate, complete and appropriate for use by the business. Data architects are responsible to ensure that there is trust on data within the organization.

Data Access

Data architects should ensure that the data within the organization is easily accessible to the users. The users of data should be able to efficiently search data elements, so that they can use data for various applications, they should easily understand data within the organization. This requires systems that support business glossary and data traceability. The cost to use enterprise data should be low and data within the organization should be searchable, standardized and organized to ensure easy of use.