Reinforcement Learning (RL), a “niche” Machine Learning technique, has surfaced in the last five 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 […]
The Future of Data Engineering
A Data Engineering Guide reveals that while people often rely on the work of data engineers — depending on Siri for quick solutions or being enchanted by custom recommendations or promos — they often do not realize that these advanced tools can provide accurate results only because of the hard work put in by data […]
Data Virtualization Use Cases
Data virtualization, in a nutshell, utilizes data integration without replication. In this process, a single “virtual” data layer is created to provide data services to multiple users and applications at the same time. Why Data Virtualization Is a Necessity for Enterprises explains how data virtualization helps tackle data movement challenges by making a virtual dataset […]
Deep Learning and Analytics: What is the Intersection?
Emergent artificial intelligence (AI) technologies, especially the automated algorithms populating analytics platforms, are impacting and reshaping the world of business analytics. The underlying connections between traditional analytics processes and the disruptive technologies will make you cheer if you happen to be a data scientist or a business analyst — because your redefined role in the […]
Data-as-a-Service (DaaS): An Overview
As external data begins to gain importance in business analytics, data assumes a new role in global businesses. Now data is not only an organizational asset, but also a distinct revenue opportunity via data-related services offered under the umbrella term of “Data-as-a-Service” (DaaS). DaaS service providers are either replacing the traditional data analytics services or […]
Self-Service Analytics Use Cases
Self-service analytics offers dynamic reports to business users, who can analyze the data by sophisticated, in-built functions. This kind of user empowerment reduces reliance on IT staff. Moreover, self-service business intelligence (BI) capability may additionally equip users with external data access and built-in features to instantly generate finished reports. To use a complete range of […]
Blockchain Trends in 2020
2020 is likely the year when blockchain technologies will witness higher adoption rates and usage across industry niches. As announced in 2019, China may lead the way to substantial investment in blockchain research and applications. The largest cryptocurrency exchanges are in China, and this year may see some dramatic deployment shifts from the multi-cloud blockchains […]
Fundamentals of Cognitive Analytics
The journey of data analytics began with simple descriptive (review of past events) and diagnostic (analysis of past events) exercises and moved to the more sophisticated predictive and prescriptive genres,where advanced data models have enabled accurate future forecasts and actionable intelligence. And now cognitive computing has further strengthened the actionable predictive power of the machines […]
Hybrid Cloud vs. Multi-Cloud Architectures
The workloads (applications) running on an enterprise hybrid or multi-cloud network determine the architectural requirements. All cloud architectures share some common patterns, while the specifics of running workloads necessitate custom architectures. The architecture patterns are broadly of two types: “distributed-deployment” pattern and “redundant-deployment” pattern. In the former, an application runs in an environment best suited […]
Data Architecture and Data Science: What is the Intersection?
Data Science, in practice, should ultimately combine the best practices of information technology, analytics, and business. On the other hand, Data Architecture enables data scientists to analyze and share data throughout the enterprise for strategic decision-making. Thus, without a sound Data Architecture in place, data scientists will remain severely handicapped in their abilities to develop […]