One of the changes that we've seen happening in the analyst space recently is a huge shift in thinking. Gartner in particular is now talking about augmented consumers and multi-experience analytics. To me, this is really interesting because they’re talking about the business user and how they want to work and consume data. In the past it was all about the data analyst, but focusing on users opens up an entirely new level of thinking.
From the Wright Brothers and Ada Lovelace, to Elon Musk and Steve Jobs, when we consider who is behind the most celebrated innovations and industry transformations, we often think about individual bright thinkers and disruptors. However, over the years, studies have shown that the greatest potential lies in the “power of many," fostered by a shift in how new generations work.
When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. While model training on small data can typically take minutes, doing the same on large volumes of data can take hours or even weeks. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads.
Optoro’s platform finds returned items a new home. Here’s how its data engineering team keeps mission-critical data flowing.
Companies with B2C & B2B channels have unique challenges with intelligence and automation, best served by the Modern Data Stack.
Java developers are unlikely to ever suffer from a lack of libraries, utilities, and programs at their disposal. There's no shortage of tools that offer niche as well as fundamental features. However, some tools undoubtedly stand out due to their popularity, usefulness, data representation, and in-tool features. These 8 tools listed below are often listed as some of the best Java development tools available on the market.
You finish writing your code and launch your application. Then, you begin experiencing performance issues. How can you fix this? It doesn’t matter how talented your development team is, every code should always be analyzed, debugged, and reviewed to make it run faster. What you need is a performance profiling tool. In this article, you will learn about performance profiling and how to determine the best performance profiling tools for your software.
This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization.
Enterprise data warehouse platform owners face a number of common challenges. In this article, we look at seven challenges, explore the impacts to platform and business owners and highlight how a modern data warehouse can address them.
Many organizations are working to become more data-driven – increasing data use and leveraging data insights to improve decision-making, solve their most challenging problems and improve revenue and profitability. A February 2020 IDC survey showed a direct correlation between quality decision-making and strong data-to-insight capabilities; 57 percent of organizations with the best data analytics pipelines received the highest decision-making score.
