Explore KSACs by Pathway

In late 2025, the Partnership pulled together members of industry from across tech to share how the in-demand skillsets for entry-level roles in their fields have changed in the last two years. The Knowledge, Skills & Abilities and Credentials (KSACs) below reflect their feedback on entry-level expectations, especially in a tech workplace increasingly shaped by AI.

Previously two separate KSA lists, Data Management and Analytics have been combined as of 2026. This is in direct response to feedback from members of industry, who shared that with the onset of generative AI, most entry-level data roles will require literacy in skillsets across both of these two specializations.

1. Data Management, Governance, and Foundations

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Label KSAC Description Bloom's Taxonomy Level
a Define and appropriately use key data concepts including data engineering, data repositories, metadata, data governance, and data lifecycle knowledge 2
b Define and appropriately use AI-related data concepts including machine learning, model drift, training data, inference data, and data leakage, particularly as they relate to analytics and automated decision-making knowledge 2
c Explain the purpose and structure of data governance programs, including how governance supports data quality, consistency, compliance, and trust across analytics and AI-enabled use cases knowledge 3
d Explain the role of change management in data governance, including how policies, standards, and definitions are introduced, maintained, and adopted over time knowledge 2
e Describe roles within data governance frameworks (e.g., data owner, steward, producer, consumer), including how responsibilities differ across data creation, maintenance, and use knowledge 2
f Explain the importance of metadata, lineage, and provenance across analytics and AI workflows, including their role in reproducibility, auditability, and model reliability knowledge 2
g Identify governance risks introduced by AI-assisted data use, such as loss of transparency, inappropriate reuse of data, or misalignment with original data purpose knowledge 3
h Explain the concept of a system of record and golden source data, and why these distinctions matter for analytics, reporting, and AI training data knowledge 2
i Identify basic governance issues across the analytics lifecycle, including data access, versioning, reuse, and downstream dependency management knowledge 3

Credentials

Vendor Certifications - DBMS

  1. Oracle Associate
  2. Microsoft
  3. IBM

Vendor Certifications - Cloud

  1. AWS
  2. Oracle
  3. Microsoft

Vendor Certifications - Tools

  1. Tableau
  2. SAS

Others

  1. DAMA CDMP
  2. DataCamp Certificate