article thumbnail

5 rules that transform outsourcing outcomes

CIO Business Intelligence

For organizations seeking a collaborative win-win approach to outsourcing, the Vested sourcing business model is worth consideration. It is the product of nearly 20 years of research at the University of Tennessee, beginning with a deep-dive funded by the United States Air Force on outcome-based outsourcing in 2003.

article thumbnail

Sport analytics leverage AI and ML to improve the game

CIO Business Intelligence

In the years since author Michael Lewis popularized sabermetrics in his 2003 book, Moneyball: The Art of Winning an Unfair Game , sports analytics has evolved considerably beyond baseball. Risk Mitigation Modeling can then be used to analyze training data and determine a player’s ideal training volume while minimizing injury risk.

Analytics 102
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Empowering data mesh: The tools to deliver BI excellence

erwin

In this blog, we’ll delve into the critical role of governance and data modeling tools in supporting a seamless data mesh implementation and explore how erwin tools can be used in that role. With erwin Data Modeler and erwin Data Intelligence working together, teams can experience powerful support for the needs of data mesh governance.

article thumbnail

CBAP certification: A high-profile credential for business analysts

CIO Business Intelligence

IIBA is a nonprofit professional association founded in 2003 to promote the field of business analysis. There must be a measurable learning objective or set of objectives that are directly applicable to changing the behavior or improving the skills of a business analyst.

article thumbnail

Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

article thumbnail

Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

Posteriors are useful to understand the system, measure accuracy, and make better decisions. But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. In our model, $theta$ doesn’t depend directly on $x$ — all the information in $x$ is captured in $t$.

KDD 40
article thumbnail

Multiplicity: Succeed Awesomely At Web Analytics 2.0!

Occam's Razor

My first eMetrics summit was June 2003 and as a young inexperienced person new in the field it was a great learning experience (eMetrics in Santa Barbara were the best!). For a person such as myself who came from the traditional Data Warehouse and Business Intelligence worlds that was a non-trivial mental model transformation.