What are the key skills and competencies required for a successful managed AI data and analytics team?

1 Data Engineering

Data engineering is the foundation of any AI data and analytics project. It involves collecting, processing, storing, and transforming data from various sources and formats into a unified and scalable data platform that can support AI and ML applications. A data engineering team needs to have strong skills in data modeling, data pipelines, data quality, data governance, cloud computing, and distributed systems. They also need to be proficient in using tools and frameworks such as Apache Spark, Apache Kafka, Apache Airflow, AWS, Azure, or Google Cloud Platform.

2 Data Science

Data science is the core of any AI data and analytics project. It involves applying statistical, mathematical, and computational methods to analyze data and generate insights, predictions, and recommendations. A data science team needs to have strong skills in data exploration, data visualization, data analysis, machine learning, deep learning, natural language processing, computer vision, and optimization. They also need to be proficient in using tools and frameworks such as Python, R, SQL, TensorFlow, PyTorch, Scikit-learn, Keras, or Spacy.

3 Data Analytics

Data analytics is the outcome of any AI data and analytics project. It involves communicating and presenting the results of data science to stakeholders and users in a clear, concise, and actionable way. A data analytics team needs to have strong skills in data storytelling, data reporting, data dashboards, data interpretation, data validation, and data-driven decision making. They also need to be proficient in using tools and frameworks such as Tableau, Power BI, Qlik, Excel, or Google Data Studio.

4 Data Strategy

Data strategy is the vision and direction of any AI data and analytics project. It involves defining the business goals, value propositions, use cases, and success metrics of the project, as well as aligning them with the data capabilities, resources, and governance. A data strategy team needs to have strong skills in business analysis, data literacy, data ethics, data governance, data security, and data innovation. They also need to be proficient in using tools and frameworks such as data catalogs, data lineage, data quality, data policies, or data audits.

5 Data Management

Data management is the operation and maintenance of any AI data and analytics project. It involves ensuring the availability, reliability, performance, and scalability of the data platform and the data applications, as well as monitoring and troubleshooting any issues or incidents. A data management team needs to have strong skills in data administration, data backup, data recovery, data migration, data integration, data testing, and data optimization. They also need to be proficient in using tools and frameworks such as SQL Server, Oracle, MongoDB, Cassandra, Hadoop, or Kubernetes.

6 Data Collaboration

Data collaboration is the culture and mindset of any AI data and analytics project. It involves fostering a cross-functional and agile team that can work together effectively and efficiently, as well as engaging with other stakeholders and users to gather feedback and deliver value. A data collaboration team needs to have strong skills in communication, teamwork, project management, problem-solving, critical thinking, and creativity. They also need to be proficient in using tools and frameworks such as GitHub, Jira, Slack, Zoom, or Google Docs.

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?