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 […]
Citizen Data Scientists: Where Do They Belong?
The phenomenal growth of data technology in recent years has led to the rise of the citizen data scientists (CDS). Developments in augmented analytics and artificial intelligence (AI) automation have now made it possible for ordinary business employees to conduct advanced analytics or business intelligence (BI) which would have required expert knowledge even some years […]
The Role of Big Data in Business Development
Click to learn more about author Mehul Rajput. With the huge amount of online data available today, it comes as no surprise that “big data” is still a buzzword. As the name suggests, business owners around the world now have a high volume and variety of information at their fingertips. But big data is more […]
Tales of A Common Vocabulary, Data Quality, and Machine Learning, Oh My!
Order in the court! Order in the court! The case being heard is a serious one. The defendant—the Chief Data Architect of VeraVisionFake Inc.—is accused of having ignored the need to establish a valid and sound common data vocabulary in her organization. She is up against The Common Vocabulary Value regulatory agency, whose mission is […]
A Look Inside the Modern Analytics Stack
Click here to learn more about Amit Levi. In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […]
The Three Pillars of Trusted AI
Click to learn more about author Jett Oristaglio. As AI becomes ubiquitous across dozens of industries, the initial hype of new technology is beginning to be replaced by the challenge of building trustworthy AI systems. We’ve all heard the headlines: Amazon’s AI hiring scandal, IBM Watson’s $62 million failure in oncology, the now-infamous COMPAS recidivism […]
ETL vs. Data Preparation
Extract, Transform, and Load (ETL) technologies, managed exclusively by IT, have until recently been the primary tool used to combine data from multiple sources and thus provide the ability to drive important business decision making for organizations. But, with the advent of self-service data preparation, business users and subject matter experts (SMEs) can find those […]
Integrated Deployment: Continuous Deployment
Click to learn more about author Paolo Tamagnini. In this second article of our integrated deployment blog series – where we focus on solving the challenges around productionizing Data Science – we look at the model part of the process. In the previous article we covered a simple integrated deployment use case. We first looked at an existing […]
How to Ensure Your New Cloud Data Lake Is Secure
Click to learn more about author Brian Lachance. Enterprises migrating on-prem data environments to the cloud in pursuit of more robust, flexible, and integrated analytics and AI/ML capabilities are fueling a surge in cloud data lake implementations. The rationale is justified: Compared with legacy on-prem infrastructure, cloud data lakes – if implemented correctly – promise […]
Six Questions About Synthetic Data
Click to learn more about author Dr. Sigal Shaked. What is synthetic data, and why are more and more companies turning to it as an alternative to the real thing? Here’s what you should know about the benefits and uses of synthetic data. What is the buzz about synthetic data? Organizations often lack enough data […]