Investing in leadership skills as well as data skills allows organizations can ensure that they can have decisive marketplace and intra-organizational impacts. The Chief Data Officer (CDO) role is only a few years old, and many organizations still don’t have a CDO. Data Literacy is an essential component to all of this. In a recent […]
Building a Data Governance Program: Ten Steps to Success
Building a Data Governance program from the ground up can be a huge undertaking, much like a puzzle, but with no picture as a guide. The Chief Data Officer at American Fidelity Insurance, Ryan Doupe, spoke at DATAVERSITY® Enterprise Data World Conference and presented a practical ten-step plan for starting or improving a Data Governance […]
Working Towards Explainable AI
“The hardest thing to understand in the world is the income tax.” This quote comes from the man who came up with the theory of relativity – not exactly the easiest concept to understand. That said, had he lived a bit longer, Albert Einstein might have said “AI” instead of “income tax.” Einstein died in […]
Data Quality Dimensions
Data Quality dimensions are useful concepts for improving the quality of data assets. Although Data Quality dimensions have been promoted for many years, descriptions of how to actually use them have often been somewhat vague. Data that is considered to be of high quality is consistent and unambiguous. Poor Data Quality results in inconsistent and […]
Deep Reinforcement Learning: What, Why, How
Reinforcement learning (RL), a “niche” machine learning technique, has surfaced in recent years. In context-based decision-making, reinforcement learning helps the machine take action-provoking decision-making through a trial-and-error approach to achieve the optimal algorithmic model for a situation. Furthermore, the machine is trained through a reward/penalty-based feedback mechanism, the goal of which is to continuously improve […]
A Brief History of Deep Learning
Deep Learning, is a more evolved branch of machine learning, and uses layers of algorithms to process data, and imitate the thinking process, or to develop abstractions. It is often used to visually recognize objects and understand human speech. Information is passed through each layer, with the output of the previous layer providing input for […]
A Brief History of Data Architecture: Shifting Paradigms
Data Architecture is a set of rules, policies, and models that determine what kind of data gets collected, and how it gets used, processed, and stored within a database system. Data integration, for example, is dependent on Data Architecture for instructions on the integration process. Without the shift from a programming paradigm to a Data […]
Bringing Data Science into the Organization
In a nutshell, Data Science accelerates business growth. Sudeep Rao, offers solid evidence of this by stating that worldwide nearly $30 billion is invested annually for artificial intelligence (AI) and machine learning (ML) projects. A BI and analytics survey indicated that 94% of the survey participants reported “data and analytics” as important factors for the […]
Data Quality, Data Stewardship, Data Governance: Three Keys
Typically, Data Governance programs start with Data Quality, because that is where end users or stakeholders begin to interact with data, especially from the reporting and analytics perspective. “They get a report that doesn’t match another report and they can’t marry it to other data,” said Mary Anne Hopper, Data Management Consultant at SAS Institute. […]
A Brief History of Artificial Intelligence
In 1950, a man named Alan Turing wrote a paper suggesting how to test a “thinking” machine. He believed if a machine could carry on a conversation by way of a teleprinter, imitating a human with no noticeable differences, the machine could be described as thinking. His paper was followed in 1952 by the Hodgkin-Huxley […]