Machine learning (ML), a branch of artificial intelligence (AI), was sometimes referred to as “cognitive computing” in the past, and certain academic circles still today. Machine learning applications have been used for decades to automate complex human tasks that require analytic thinking, but recently the technology has expanded to encompass more business functions. Advanced ML […]
Data Quality Challenges
The Data Quality and Data Management market is going through a paradigm shift where the focus has turned to the business user. Historically, business users have been at the mercy of over-burdened IT departments with limited resources, but IT is not to blame. Even with the simplest query, the answer used to be “It’ll be […]
Data Architect vs. Data Engineer
Data careers are becoming increasingly important and popular all across the globe, simply because “data” is the new currency of the data economy. The pandemic gave the needed push to accelerate the digital transformation of global businesses, and currently, the primary market differentiator is an enterprise’s data infrastructure readiness. This data infrastructure comprises systems, processes, […]
Data Warehouse vs. Data Lake Technology: Different Approaches to Managing Data
Solving business problems using big data depends upon the approach taken. For example, if an organization only knows data warehouses, then challenges will be framed to fit using a data warehouse. As Abraham Maslow, a prominent psychologist eloquently said “I suppose it is tempting, if the only tool you have is a hammer, to treat […]
A Brief History of Neural Networks
In the last few decades, neural networks have evolved from an academic curiosity into a vast “deep learning” industry. Deep learning uses neural networks, a data structure design loosely inspired by the layout of biological neurons. These neural networks are constructed in layers, and the inputs from one layer are connected to the outputs of […]
Artificial Intelligence, Machine Learning, and Data Protection
Artificial intelligence and machine learning techniques are altering the way organizations gather, process, and protect data. They are being used to gather massive amounts of information about internet users in the form of big data, and to secure and protect it. The challenge is how to maximize the use of big data, while simultaneously safeguarding […]
Is Streaming Analytics the Future of Business Analytics?
Analyzing data as it is created or changes? Is that possible? Now it is, with streaming analytics, which monitors and responds to continuously flowing data from connected devices and live data channels like the sensors, machine logs, relational databases, social media feeds, location data sources, and so on. The core differentiator of streaming analytics is […]
Seven Principles to Put DataOps into Practice
You’re going to hear a lot more about DataOps in the coming months and the next couple of years. That’s the word from DataKitchen co-founder Eric Estabrooks. You know something is gaining market traction when Gartner includes it in a hype cycle report. The research firm did just that for DataOps in its Hype […]
Case Study: Centrica Succeeds with Data Discovery at Scale
“When you’ve got a mass of data, how do you analyze that data and get to a point where you can get the gemstones, the diamonds out of it?” Mike Young, Chief Information Officer with Centrica, knows what it’s like to wade through a sea of petabytes and terabytes to find value. Centrica is an […]
The Data Warehouse, the Data Lake, and the Future of Analytics
Data lakes were created in response to the need for Big Data analytics that has been largely unmet by data warehousing. The pendulum swing toward data lake technology provides some remarkable new capabilities, but can be problematic if the swing goes too far in the other direction. Far from being at the end of this […]