What Is Data-Driven Decision-Making? | IBM

In a data-driven organization, several key data science roles are essential for using data effectively and creating a data-driven culture. In addition to data analysts, data managers and data scientists, there are other important roles that guide an organization toward success in its data-driven decision-making initiatives.

Data engineers design, build and maintain the infrastructure and systems required for data collection, storage and processing. Data engineers safeguard data pipelines so they are efficient, scalable and reliable, enabling smooth data flow from various sources to analytical platforms.

Data architects are responsible for designing and implementing an organization’s overall data architecture. They create blueprints for data management systems, so data is organized, integrated and accessible.

Business intelligence (BI) developers create and manage BI solutions, such as dashboards and reporting systems. They transform raw data into meaningful insights through visualization tools, to help stakeholders make informed decisions.

Machine learning engineers are specialists who build, deploy and maintain machine learning models. They work closely with data scientists to implement algorithms that can predict outcomes and automate decision-making processes.

Chief data officer (CDO) is an executive role that oversees an organization’s data strategy and governance. They ensure that data initiatives align with business objectives, compliance standards and best practices.

Chief artificial intelligence officer (CAIO) is an executive role that guides the organization through the complexities of AI adoption. They provide strategic leadership and oversee the development, strategy and implementation of AI technologies.

Data analysts are professionals who analyze and interpret complex datasets to provide actionable insights. They use statistical methods and tools to identify trends, patterns and correlations.

Database administrators (DBAs) manage and maintain database systems. They protect data and confirm it is stored securely, backed up regularly and retrievable efficiently. DBAs also optimize database performance and resolve any data-related issues.

Data privacy officers are responsible for ensuring that data handling practices comply with privacy laws and regulations. They develop policies and practices to protect sensitive information and maintain customer trust.

AI/ML operations (MLOps) engineers manage the deployment, monitoring and maintenance of machine learning models in production environments. MLOps engineers guarantee that models operate efficiently and are updated as needed.