How do you identify essential data management skills when hiring or developing data staff?

1 Data Literacy

Data literacy is the ability to understand, interpret, and communicate data effectively. Data staff need to have a strong grasp of the data lifecycle, from data collection to data analysis to data visualization and reporting. They also need to be able to use data to answer business questions, solve problems, and generate insights. Data literacy skills include critical thinking, data ethics, data storytelling, and data quality assessment.

2 Data Modeling

Data modeling is the process of designing and implementing data structures and relationships that represent the business concepts and rules. Data staff need to have a solid understanding of data modeling principles, such as normalization, denormalization, dimensional modeling, and entity-relationship modeling. They also need to be able to use data modeling tools and languages, such as SQL, NoSQL, ERD, UML, and XML.

3 Data Integration

Data integration is the process of combining data from different sources and formats into a unified and consistent data set. Data staff need to have a proficient knowledge of data integration techniques, such as ETL (extract, transform, load), ELT (extract, load, transform), data pipelines, data warehousing, and data lake. They also need to be able to use data integration tools and platforms, such as Apache Airflow, AWS Glue, Azure Data Factory, and Google Cloud Dataflow.

4 Data Analysis

Data analysis is the process of applying statistical and analytical methods to data to discover patterns, trends, and correlations. Data staff need to have a keen ability to perform data analysis tasks, such as data cleaning, data exploration, data manipulation, data mining, and data testing. They also need to be able to use data analysis tools and frameworks, such as Python, R, Excel, Tableau, and Power BI.

5 Data Engineering

Data engineering is the process of building and maintaining the data infrastructure and architecture that support data processing and analysis. Data staff need to have a robust skill set in data engineering domains, such as data storage, data processing, data security, data governance, and data optimization. They also need to be able to use data engineering technologies and tools, such as Hadoop, Spark, Kafka, MongoDB, MySQL, and PostgreSQL.

6 Data Science

Data science is the process of applying advanced techniques and algorithms to data to generate predictions, recommendations, and insights. Data staff need to have a deep understanding of data science concepts and methods, such as machine learning, artificial intelligence, natural language processing, computer vision, and deep learning. They also need to be able to use data science tools and libraries, such as TensorFlow, PyTorch, Scikit-learn, NLTK, and OpenCV.

7 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?