In explaining a data and analytics operating model, it is helpful to understand the business context. As the consumer experience becomes increasingly digitized, companies have access to massive troves ...

Sparse data can impact the effectiveness of machine learning models. As students and experts alike experiment with diverse datasets, sparse data poses a challenge. The Leeds Master’s in Business ...

Buy-side firms are consistently faced with burgeoning volumes of data, necessitating adept management of expansive data extractions, a task fraught with intricacies and considerable costs. Arthur Orts ...

In the humanitarian aid community, research methods have traditionally skewed toward the qualitative: Participant interviews, focus groups, and field surveys have been the predominant tools ...

As artificial intelligence technology continues to improve, more companies have become interested in machine learning—technology that is able to “learn” and adapt to become increasingly adept at ...

However, knowing what specific data is valued is fundamental in effectively scaling up an analytics model, as the goal is to produce more of what you value. Let’s consider class attendance as a ...

Real estate is a massive (and complicated) part of agency operations. Leading-edge analytics help make the most of existing footprints. Rana Lahiri, left, and Mark DeRosa say improved analytics can ...

No matter the size or scope of an organization, there is little question that everyone wants to find ways to improve their performance. Baseball teams use sabermetrics; companies often use the stock ...

Lemonade is one of this year’s hottest IPOs and a key reason for this is the company’s heavy investments in AI (Artificial Intelligence). The company has used this technology to develop bots to handle ...

Data analytics can be a tough topic to teach. There’s no real road map for instructors to follow, and the technology that underlies it can change at a rapid clip. What’s more, students often have ...