Byline: Brian ChidesterAgility. Speed. Flexibility. Efficiency. These are not words normally associated with government by most people. In defense of governments, though, they have huge challenges in providing services on the enormous scale at which they function. And the US federal government operates some of the largest organizations in the world. Now, the Federal Risk and Authorization Management Program (FedRAMP) is giving those government operations agility, speed, flexibility and efficiency and creating the framework for future successes.FedRAMP is a set of data security guidelines established in 2011 with a direct focus on cloud-based products and services. It was created by the Joint Authorization Board (JAB) with representatives from the Department of Homeland Security (DHS), the General Services Administration (GSA) and the Department of Defense (DoD).To read this article in full, please click here
Wharton’s Raghuram Iyengar explains three major challenges to omnichannel marketing and what firms can do to overcome them.
Everyone understands that data is your company’s fuel for insights and business innovation. In the “big data” days, it was defined by three V’s: volume, variety, and velocity. In today’s digital world, data is now defined by three D’s: diversity, distribution, and dynamics. But no matter how you define it, one thing is clear—everything about data has changed. The organizational roles consuming data have changed from business to technical, strategic to tactical, from the front office to the back office. There’s a new generation of data-native workers who rely on data to complete their daily tasks. They work with data and expect to be able to access it from any location. Moreover, the tools and technologies to engineer, govern, protect, and consume data have changed as organizations are looking to consolidate multiple data management tools to accelerate time to insight while simplifying data management. To read this article in full, please click here
The growth in the number of connected devices, new regulatory compliances, the promise of 5G, and evolving methods of using and analyzing data have ushered in a new age driven by insights gained from connecting […]
Work has become more dynamic, and the tools and platforms that support a modern, changing workforce need to adapt. Collaboration and workflows must be scalable and secure to keep businesses moving forward.At our ENGAGE: ALL IN virtual conference, we announced some exciting product developments, including Smartsheet Advance. This new offering builds on the core functionality of Smartsheet, helping organizations automate workflows across systems, align global teams, and build business-driven solutions. It is enterprise-ready and built to unlock the true power of business: its people.To read this article in full, please click here
For machine learning to be used more widely, it needs to be brought closer to the data that fuels ML modeling and insights. For database developers and data analysts that are still getting up to speed on ML modeling, the ideal scenario is to integrate ML algorithms and training models directly into the tools they already use, making it easier for them to extract meaningful insights from their data.In-house database teams are likely to be experts in SQL, but they may not know Python, which has emerged as a primary programming language for AI and machine learning. As a result, they’re reliant on data scientists to build the models for them to add intelligence to their applications.To read this article in full, please click here
Big waves of business process reengineering have historically been few and far between. One reason has been the length of time it takes humans to analyze trends, conceive new ways of operating, and then integrate the process changes into software code. Machine learning (ML) is knocking down that barrier.ML algorithms’ ability to adapt on their own, requiring no explicit programming to learn from changing conditions, lets them continually hone their automated problem-solving, predictions, and insights. The more good data they’re fed, the smarter they become. As such, ML is enabling organizations to rethink existing processes at unprecedented scale and speed, improving efficiencies and spurring innovation at a pace that once would have been unimaginable.To read this article in full, please click here
Plus: The origins of Blue Origin, Apple’s annoying error messages, and a Kardashian plot twist.
In the race to optimize manufacturing capabilities, more companies are turning to digital twins. These virtual clones of their physical operations can help them simulate scenarios that would be too time consuming or expensive to test with physical assets. On that score, Mars is working with Microsoft to craft a digital twin of its manufacturing supply chain in support of its confectionery, pet care, and food businesses.To read this article in full, please click here(Insider Story)
You can see the faint stubble coming in on his upper lip, the wrinkles on his forehead, the blemishes on his skin. He isn’t a real person, but he’s meant to mimic one—as are the hundreds of thousands of others made by Datagen, a company that sells fake, simulated humans. These humans are not gaming avatars or animated characters for movies. They are synthetic data designed to feed the growing appetite of deep-learning algorithms. Firms like Datagen offer a compelling alternative to the expensive and time-consuming process of gathering real-world data. They will make it for you: how you want it, when you want—and relatively cheaply. To generate its synthetic humans, Datagen first scans actual humans. It partners with vendors who pay people to step inside giant full-body scanners that capture every detail from their irises to their skin texture to the curvature of their fingers. The startup then takes the raw data and pumps it through a series of algorithms, which develop 3D representations of a person’s body, face, eyes, and hands. The company, which is based in Israel, says it’s already working with four major US tech giants, though it won’t disclose which ones on the record. Its closest competitor, Synthesis AI, also offers on-demand digital humans. Other companies generate data to be used in finance, insurance, and health care. There are about as many synthetic-data companies as there are types of data. Once viewed as less desirable than real data, synthetic data is now seen by