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Data Management
Aiman Zeid 1
No one route to analytics success

I led an analytical culture track at the SAS Global Forum Executive Conference last month in Washington, DC. I talked with leaders in fields as diverse as healthcare, chemical manufacturing and government. Although these organizations have very different operating models, their challenges, comments and questions were similar. They all recognized

Data Management
Sam Munoz 0
Series: BCBS 239 - Principle 4

Principle 4: Completeness – A bank should be able to capture and aggregate all material risk data across the banking group. Data should be available by business line, legal entity, asset type, industry, region and other groupings, as relevant for the risk in question, that permit identifying and reporting risk

Analytics
Leo Sadovy 2
Agility and the Analytic Sandbox

Analytics gives us not just the ability but the imperative to separate our planning activities into two distinct segments – detailed planning that leads to budgets in support of execution, and high-level, analytic-enabled business/scenario planning. My critique of Control Towers in this blog last time led me not only to

Data Management
Sam Munoz 0
Series: BCBS 239 – Principle 3

Principle 3: Accuracy and Integrity – A bank should be able to generate accurate and reliable risk data to meet normal and stress/crisis reporting accuracy requirements. Data should be aggregated on a largely automated basis so as to minimize the probability of errors. It seems logical that banks would want

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