The primary purpose of implementing a Data Architecture is to standardize the methods and protocols, as well as the systems for acquiring, storing, managing, and sharing data across the enterprise for improved decision-making. In modern businesses, most decisions are made in real time, and to facilitate an efficient and real-time friendly Data Management infrastructure, data […]
A Brief History of Data Management
Data Management is the organization of data, the steps used to achieve efficiency, and gather business intelligence from that data. Data Management, as a concept, began in the 1960s, with ADAPSO (the Association of Data Processing Service Organizations) forwarding Data Management advice, with an emphasis on professional training and quality assurance metrics. Data management has […]
How to Become a Data Scientist
Becoming a data scientist does not necessarily require a master’s degree. There is a significant shortage of data scientists, and some employers are comfortable hiring people who lack a degree, but have the experience needed. The majority of employed data scientists have a master’s degree, but over 25% do not. If you have the experience, […]
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 […]
Better Data Modeling with Lean Methodology
The process used today in systems development started with principles developed for assembly lines in the 1950s, when manufacturers wanted a more disciplined approach to producing goods and services.Products would come off an assembly line, they’d be inspected, defects would be found, and would be sent back to rework or start from scratch. This process […]
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 […]
Data Governance Trends in 2022
Way back in the early 2000s, Data Governance wasn’t really much of a thing. There were a few really early pioneers that were doing Data Governance, and they were laying the groundwork, but governance was not a recognized capability. Companies who did see some value in Data Governance were primarily focused on the benefit to […]
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 […]