What skills do data warehousing professionals need for future data architecture?

1 Data Modeling

Data modeling is the process of creating and documenting the logical and physical structures of data, as well as the relationships and constraints among them. Data modeling helps data warehousing professionals to ensure that the data warehouse is consistent, accurate, and scalable. Data modeling skills include the ability to use different types of data models, such as conceptual, logical, and physical; to apply various modeling techniques, such as dimensional, relational, and hierarchical; and to use data modeling tools, such as ERwin, PowerDesigner, or SQL Developer.

2 Data Integration

Data integration is the process of combining data from different sources and formats into a unified and coherent data set. Data integration helps data warehousing professionals to provide a comprehensive and reliable view of the data for analysis and reporting. Data integration skills include the ability to use different methods and tools for data extraction, transformation, and loading (ETL), such as Informatica, Talend, or SSIS; to handle data quality issues, such as duplicates, errors, or inconsistencies; and to implement data governance policies and standards, such as metadata management, data lineage, or data security.

3 Data Analysis

Data analysis is the process of exploring, interpreting, and communicating the insights and patterns derived from data. Data analysis helps data warehousing professionals to support the decision-making and problem-solving processes of their organizations. Data analysis skills include the ability to use different techniques and tools for data manipulation, visualization, and reporting, such as SQL, Excel, Tableau, or Power BI; to apply different methods and frameworks for data mining, statistics, and machine learning, such as regression, clustering, or classification; and to communicate the results and recommendations effectively, such as through dashboards, reports, or presentations.

4 Cloud Computing

Cloud computing is the delivery of computing services, such as storage, processing, networking, or software, over the internet. Cloud computing helps data warehousing professionals to leverage the benefits of scalability, flexibility, and cost-efficiency of the cloud platforms and services. Cloud computing skills include the ability to use different cloud providers and models, such as AWS, Azure, or GCP; to migrate and manage data warehouses in the cloud, such as using Snowflake, Redshift, or BigQuery; and to integrate and orchestrate data workflows in the cloud, such as using Airflow, AWS Glue, or Azure Data Factory.

5 Data Ethics

Data ethics is the set of principles and practices that guide the ethical and responsible use of data. Data ethics helps data warehousing professionals to ensure that the data warehouse is compliant with the legal and regulatory requirements, as well as the ethical and social norms and values of their organizations and stakeholders. Data ethics skills include the ability to identify and assess the potential risks and impacts of data collection, storage, and analysis, such as privacy, security, or bias; to implement and monitor the appropriate measures and controls, such as encryption, anonymization, or auditing; and to promote and foster the data culture and literacy, such as through education, training, or advocacy.

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?