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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 […]

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 […]

Fundamentals of Self-Service Business Intelligence

It’s clear that there is considerable recent market movement towards self-service business intelligence (SSBI) in the numerous vendor offerings available. There is also a growing concern among the Data Science community that ordinary business users may misunderstand or misinterpret the available data, leading to incorrect results. Experienced data scientists have a tremendous ability to analyze, […]

Scaling Machine Learning Applications

When the number of users for a predictive model grows, it is expected (albeit often wrongly) that the machine learning powered systems will automatically scale to keep up with this growth. If the system fails to scale, processing requirements may outpace performance. Using an example from a LinkedIn article, a sample recommender system fails to […]

So You Want to be a Machine Learning Engineer?

Ideally, a machine learning engineer would have both the skills of a software engineer and the experience of a data scientist and data engineer. However, data scientists and software engineers usually come from very different backgrounds, and data scientists should not be expected to be great programmers, nor should software engineers be expected to provide […]

It’s Time to Build a Cohesive Data Strategy

George Yuhasz is helping businesses to innovate in another direction: building a rich Data Strategy by holistically and concurrently bringing together analytics, Data Governance, and Information Architecture. As U.S. Director, Business Intelligence and Data Services, he launched the Data Strategy at the global food services enterprise. It’s been a job of optimizing people and processes, […]

A Brief History of Data Literacy

Data Literacy is essentially the ability to read and understand data, much as one might read and understand a magazine article. The primary advantage of having the bulk of the staff made up of people who are data literate is that it reduces the need for data scientists. People on staff can handle many of […]

Fundamentals of Hyperautomation

Hyperautomation describes a mixture of advanced technologies – robotics, artificial intelligence, machine learning – currently being used to make automated processes drastically more efficient and to augment humans. It encompasses a range of tools which can be automated, especially the more sophisticated aspects of automation, including analysis, discovery, design, measuring, monitoring, and reassessing. To function […]

Artificial Neural Networks: An Overview

Neural networks and deep learning currently provide some of the most reliable image recognition, speech recognition, and natural language processing solutions available. However, it wasn’t always that way. One of the earliest and simplest teaching philosophies for artificial intelligence was marginally successful. It suggested that loading the maximum amount of information into a powerful computer […]

Challenges for Data Governance and Data Quality in a Machine Learning Ecosystem

The high availability of data, enhanced computing power and advanced Data Science technologies together make a lethal combination for data-driven outcomes. With the open data economy just around the corner, well-tuned Data Governance capabilities will be the goal of most businesses. Current Data Management practices are focused on risk free data sharing and regulatory compliance. […]

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