The #IoT and #Analytics @ThingsExpo #BigData #BI #AI #DX #MachineLearning

@machinelearnbot

The Internet of Things (IoT) promises to change everything by enabling "smart" environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.). I recently wrote about the importance of moving beyond "connected" to "smart" in a blog titled "Internet of Things: Connected Does Not Equal Smart". The article discusses the importance of moving beyond just collecting the data, to transitioning to leveraging this new wealth of IoT data to improve the decisions that these smart environments and products need to make: to help these environments and products to self-monitor, self-diagnose and eventually, self-direct. But one of the key concepts in enabling this transition from connected to smart is the ability to perform "analytics at the edge." Shawn Rogers, Chief Research Officer at Dell Statistica, had the following quote in an article in Information Management titled "Will the Citizen Data Scientist Inherit the World?": "Organizations are fast coming to the realization that IoT implementations are only going to become more vast and more pervasive, and that as that happens, the traditional analytic model of pulling all data in to a centralized source such as a data warehouse or analytic sandbox is going to make less and less sense.


The #IoT and #Analytics @ThingsExpo #BigData #BI #AI #DX #MachineLearning

@machinelearnbot

The Internet of Things (IoT) promises to change everything by enabling "smart" environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.). I recently wrote about the importance of moving beyond "connected" to "smart" in a blog titled "Internet of Things: Connected Does Not Equal Smart". The article discusses the importance of moving beyond just collecting the data, to transitioning to leveraging this new wealth of IoT data to improve the decisions that these smart environments and products need to make: to help these environments and products to self-monitor, self-diagnose and eventually, self-direct. But one of the key concepts in enabling this transition from connected to smart is the ability to perform "analytics at the edge." Shawn Rogers, Chief Research Officer at Dell Statistica, had the following quote in an article in Information Management titled "Will the Citizen Data Scientist Inherit the World?": "Organizations are fast coming to the realization that IoT implementations are only going to become more vast and more pervasive, and that as that happens, the traditional analytic model of pulling all data in to a centralized source such as a data warehouse or analytic sandbox is going to make less and less sense.


Water Sector Embracing Big Data

#artificialintelligence

The water sector has collected reams of data for decades, but it's only within the last few years that utilities, agencies, consultants and vendors have begun to use that data to improve everything from managing maintenance to predicting water flow to digitally mimicking an entire watershed. The move to leverage digital information in the sector over the last two to three years is "drastic," says Luis Casado, senior vice president of water for Gannett Fleming and one of several people who spoke passionately about the possibilities of water data at Water Environment Federation's annual WEFTEC conference Oct. 1-3 in New Orleans. Firms like Gannett Fleming, Arcadis, Brown and Caldwell, and Jacobs are taking previously underutilized information from supervisory control and data acquisition, or SCADA, systems, and pairing it with historic datasets and additional sensor data to create customized digital dashboards and applications for water agencies and related entities. "It's not a single piece of software, it's an approach of how you look at data and how you merge that information and use it effectively in day-to-day operation," said Kevin Stively, smart utility leader for Brown and Caldwell, in a presentation at the event. He said historical information can be layered on real-time information to help a younger workforce make the operational decisions that older workers relied on their "gut" to make.


Really Big Data At Walmart: Real-Time Insights From Their 40 Petabyte Data Cloud

Forbes - Tech

To make sense of all of this information, and put it to work solving problems, the company has created what it calls its Data Café – a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. Here, over 200 streams of internal and external data, including 40 petabytes of recent transactional data, can be modelled, manipulated and visualized. Teams from any part of the business are invited to bring their problems to the analytics experts and then see a solution appear before their eyes on the nerve centre's touch screen "smart boards". This tool has cut down the amount of time it takes to solve complex business questions, which are reliant on multiple external and internal variables, from weeks to minutes. Senior Statistical Analyst Naveen Peddamail – who won his job with the company through a competition on crowd-sourced data competition website Kaggle – spoke to me about the project.


How to Achieve #DigitalTransformation @CloudExpo @DellEMC #DX #AI #IoT

#artificialintelligence

Industry after industry is under siege as companies embrace digital transformation (DX) to disrupt existing business models and disintermediate their competitor's customer relationships. But what do we mean by "Digital Transformation"? Digital Transformation The coupling of granular, real-time data (e.g., smartphones, connected devices, smart appliances, wearables, mobile commerce, video surveillance) with modern technologies (e.g., cloud native apps, Big Data architectures, hyper-converged technologies, artificial intelligence, blockchain) to enhance products, processes, and business-decision making with customer, product and operational insights. The digital transformation starts by understanding the organization's business initiatives, and then prioritizing which initiatives are top candidates for enhancement through digital transformation. "Begin with an end in mind" to quote Stephen Covey.