Building a data driven organization can be a long journey, and below are the competencies that every organization has to build to become truly data driven.
Data strategy focuses on value to the business by leveraging data. It also details out the high level approach of building this business value, ways to engagement with the business stakeholders and the strategy and roadmap to make it all happen.
Data Architecture is extremely useful on an enterprise level, to ensure consistency of ease and convenience to users and thus ensure long term patronage. It takes into account user experience while designing various systems, and also ensures cost optimization and technical excellence by providing design standards.
Data governance ensures ongoing usage of the systems to benefit its users, and defines various policies and processes to optimize and distribute the benefits across the enterprise. The main objective of this competency is to build confidence, trust, security and integrity of enterprise data within the minds of their stakeholders.
Data engineering systems have two main objectives – building the systems to acquire and store data and building systems to put data to use. Data engineering ensures that these objectives are achieved at scale across the enterprise, and confirm to the architectural guidelines and standards.
The focus of data engineering is to ensure that the processing of data can happen with a high throughput push side and low latency on the pull side.
Data science techniques are used to explore data to validate hypothesis, such that new use cases can be built to ensure that the business keeps up with the changing circumstances. Data science competency is the engine of innovation within the modern enterprise, and defines a process to continuously build new innovative data driven applications.