There are three pillars of trusted AI: performance, operations, and ethics. Performance includes Data Quality, model accuracy, and speed. In this article, we will look at our second pillar of trust, operations.
Data Science on a Large Scale: Can it Be Done?
Click to learn more about author Mathias Golombek. The Challenge Michael Stonebraker, winner of the Turing Award 2014, has been quoted as saying: “The change will come when business analysts who work with SQL on large amounts of data give way to data scientists, which will involve more sophisticated analysis, predictive modeling, regressions and Bayesian […]
Prescriptive Analytics Use Cases
The term “prescriptive analytics” denotes the use of many different disciplines such as AI, mathematics, analytics, or simulations to advise the user whether to act, and what course of action to take. In that sense, prescriptive analytics offers an advisory function regarding the future, rather than simply “predicting” what is about to happen. This newer […]
Driving an Organizational Culture Shift to Realize the Potential of AI in Life Sciences
Click to learn more about author Updesh Dosanjh. Artificial intelligence (AI) and machine learning (ML) are present in nearly every industry today. However, not all industries have readily embraced these technologies with open arms. Due to the nature of the business, the life sciences industry has historically been careful about its adoption of new technologies. […]
Managing Data Governance Throughout the Data Lifecycle
Companies rely on Data Governance, the formal Data Management of people, technology, and practices, to balance data risks and opportunities. Managing Data Governance takes on even more importance across the data lifecycle, from data planning to data disposal, as shown in the diagram below Organizations tend to neglect more comprehensive Data Governance throughout the lifecycle. […]
Building a Time Series Analysis Application
Click to learn more about author Maarit Widmann. A complete time series analysis application covers the steps in a Data Science cycle from accessing to transforming, modeling, evaluating, and deploying time series data. However, for time series data the specific tasks in these steps differ in comparison to cross-sectional data. For example, cross sectional data are […]
The Importance of Data Science and Analytics in the Finance Industry
Click to learn more about author John Russell. It is estimated that by the end of 2020, there were approximately 40 zettabytes (40 trillion gigabytes) of data in the world. If it feels a bit difficult to wrap your head around that number, don’t worry – you are not alone! It is a truly staggering […]
Why Data Democratization Should Be Your Guiding Principle for 2021
Click to learn more about author Mathias Golombek. When Maximilien Robespierre first uttered the now famous phrase “liberté, equalité, fraternité,” during the French Revolution, the concept of data democratization didn’t yet exist. In Robespierre’s speech, the phrase was intended to unite and inspire French revolutionaries with the three ideals of freedom, equality, and brotherhood. However, […]
The Important Role of Cognitive Science in Marketing Data Creation
Click to learn more about author Dave Kelly. Human beings are imperfectly dynamic creatures shaped by experiences, biases, and cognitive processes, and this combination of behavior-shaping factors is unique from individual to individual and can even change from day to day. In the always-on digital world we live in, where competition for consumer attention is […]
Supporting Talent When Remote Work Goes Flexible: Data Is Key
Click to learn more about author Tom Ricks. Studies repeatedly show that remote work is welcomed by the majority of employees. McKinsey research tells us that 80% enjoy working from home, which is good news for organizations that were concerned about employee sentiment. Better still, there are signs that productivity has either remained largely the […]