This post is the second in a two-part series on CECL. Part 1 explains the rationale for CECL and its implications on financial allowance practices.
Implementing CECL: How changes to modeling and accounting processes bring change to financial services.
Understanding polling and election modeling: How the behind data science and models that were seemingly off-target in 2016 are different in 2020.
Highly regulated industries can better leverage machine learning tools (and create trust in their findings) by developing monitoring systems that are holistic, insightful and actionable.
Creating a culture of data quality not only has a concrete impact on the accuracy of your findings, but also inspires greater organizational buy-in when your framework adheres to certain standards.
From EUCs to structured, automated data, we walk you through the process, best practices and change management to ensure you’re leveraging analysts’ skillsets to the fullest.
Summary Solution: Organizations often shortcut internal tech products because they don’t provide direct value to the customer. This post explores where and how to invest your time and efforts. Read a book or article on the topic of building a great product, and the process seems simple enough: Identify a customer base with an underserved […]
In order to get accurate data into analysts’ hands, organizations should employ a combination of data lakes and data warehousing to effectively balance the need for freedom in a structured data environment.