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A growing population across the globe is creating a mounting challenge for food producers. By the year 2050, the Food and Agriculture Organization of the United Nations (FAO) predicts the global population will be 9.6 billion people – requiring a gigantic 70 percent increase in food production. Growers are also facing escalating pressure due to environmental constraints, including the limited availability of suitable land, the scarcity of fresh water, and unpredictable weather patterns. Can advances in technology help feed the world? For many, the forecast for food production looks troubling at best. There are, however, solutions developing to fill this gap. Advances in agricultural technology and smart data usage are making it possible for agronomists and local farmers to stay robust and productive against these challenges, improving their operations, enhancing crop yield, reducing water consumption, and increasing profits. In short: to feed the world, we need to rethink the supply chain. By making small realistic adjustments, growers and other agribusinesses can achieve substantial productive increases while deriving greater profitability from their outputs. For growers, this means rethinking their place in the agriculture supply chain and how best to drive their business going forward. Partnering with technology companies and agronomists who understand both their challenges and their opportunities will become more and more critical to meeting the changing demands of the future and achieving business success. Key problem areas for farms that may be transformed by smarter tech: Inability to quickly identify problem areas in existing crops. Inability to identify potential problems in new fields. Inaccurate annual processes that take many hours. Inconsistency in tracking information about crops and fields. Over-use of chemicals with a 'just in case' mentality. Use of the wrong chemicals or supplements due to a lack of information. Smarter farming One vital area that requires focus – while also offering lucrative opportunities – is that of efficiency. In modern times it is no longer viable for every aspect of farm management to be completed manually. Every day, farms generate a huge amount of data, but collecting that data is only the first step. To derive value from data, it's necessary to implement a solution that ultimately helps drive improvements and lifts farm performance. "Data in itself is not enough," said William Richardson, Director of Training and Development for Proagrica, an independent provider of connectivity and data-led insight across the agriculture and animal health sectors. Richardson said, "You need to analyze that data to unlock its value. However, this can be an immense learning curve. You are not just talking about doing soil sampling or crop protection recommendations, you are talking about discovering something new." "By utilizing the available data and combining precision planting and spraying techniques, growers are now able to operate more efficiently and more profitably, all while creating a lower environmental impact," said Richardson. Smarter tech We are currently undergoing nothing short of a technological revolution in agriculture. The drive towards precision farming and a greater focus on observing, measuring, and responding to inter and intra-field variability in crops has meant great strides for the industry. Smart farming techniques and the use of data analytics are the current technologies creating a real step-change for farming. This has been aided by the significant uptake of mobile devices and the resulting faster wireless data transmission now available (though naturally this is not yet universal.) "With what we have today, you can wirelessly send information back to the office and that field can be treated the same day," said Richardson. "This is as near to real-time as possible. As a result, we can do much more on farm, in a way that seamlessly aids production, making it possible to be more reactive and adjustable in a way that just wasn't possible only a few years ago." The most utilized smart farming tools of today include: Remote sensors Grid sampling Global Positioning Systems (GPS) and geographical information systems Variable rate technology Auto guidance equipment Proximate sensors These technologies are widespread among farms, but there has been a push in recent years for standardized data between these disparate systems. Currently, farmers are required to enter data over and over into multiple applications. This is often frustrating and time-consuming and prevents these different applications working together to provide a holistic overview of the farm that will be successful and productive. Standardized data offers the chance to effortlessly synchronize all data on farm, cutting down on man-hours, creating the opportunity for quick and easy training with new employees, fighting back better and faster against threats (such as weeds, insects, diseases, and crop damage) and, ultimately, driving higher yields. "Efficiency is important, but standardization is key," said Richardson. "Standardizing the data allows the users to track and trend information over time, creating the opportunity to plan ahead for purchases and planting patterns. Businesses that adapt and are flexible to change are well-placed to thrive. Many are already heavily investing in data solutions, priming their business for this new era of data-based partnerships and customer service." Standardized data may offer a boost to individual businesses' profitability and cumulatively aid the industry in working smarter to deliver more. Smarter use of data is a necessary step to meet the food demands of tomorrow. Smarter future More and more in the industry are beginning to adapt their businesses to incorporate the newest advances in technology. In the short-term, technology-driven improvements to daily farm operations will likely boost farmers' profits by cutting costs and increasing yields. In the longer run, these changes may help answer that increasingly urgent question: how can we feed the world's growing population without putting too much strain on the earth's natural resources? Download Proagrica's full report outlining the challenges and possible solutions facing agriculture, and how smarter tech can lead to smarter production. Article published by icrunchdata Image credit by Getty Images, Cultura, Janie Airey Want more? 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Data lakes refer to massive storage of any structured and unstructured data at a big data scale. Data from various streams flow into the data lake and are available to cross-functional data scientists to examine and interpret patterns for predictive analytics and machine learning. On the surface, the idea sounds fantastic and full of possibilities. Many enterprises jumped on the bandwagon and created Hadoop-based repositories and started filling those with all kinds of data. Whether or not organizations are finding a business value from their data lakes, however, is yet to be determined. In the hope of some future use, many companies are blindly putting all their data into their data lakes without any objectivity, governance or traceability. Data Lakes or Data Swamps? Without proper metadata and quality assurance of data, over time the data in lakes becomes unusable. Eventually, the data lakes become so-called data swamps that neither provide any operational value nor deliver any business insights. Even if enterprises use sophisticated tools to analyze and visualize big data, the lack of correlation back to accurate master profiles and operations means there are no guarantees that the answers are reliable. Companies need to put data management principles and processes in place to improve the data reliability. What if you could blend master data and big data across all your internal and external systems, third party data subscriptions and social media sources? What if you could quickly match, merge, clean and relate all these data entities to create a reliable data foundation? What if your business applications and big data analytics platforms had real-time access to this trustworthy information in a closed loop? If we could do all this, imagine the business challenges that could be addressed with this information. A modern data management foundation such as this is core to put your big data to sound business use. Data-Driven Applications Any business endeavor needs to fulfill a business purpose. The initial goal of collecting such large amounts of data is to help the business make data-driven decisions, uncover new opportunities and mitigate finance and compliance risks. Data-driven applications help achieve that goal by creating a comprehensive picture of business entities such as customers, products, places, channels and activities by combining cleansed data from all sources and revealing relationships across these entities. Understanding the complex relationships across all your data entities is important. By identifying and visually revealing relationships between people, products, places and activities your business cares about, data-driven applications focus on the most valuable products, biggest opportunities and most influential customers. With data-driven applications, business professionals work with industry-specific applications that bring together data and insights relevant to the task at hand to make better-informed decisions that have an immediate impact. Unlike analytics-only tools, such applications provide user-friendly visuals and also guidance in the form of intelligent recommendations for improvement and ability to act collaboratively, all within the operational use. For instance, you can create a full 360 degree view of a customer by bringing together their profile data, historical interactions, past transactions and service tickets. You can bring in insights like their business value and churn propensity. You can also bring recommended actions from predictive analytics and machine learning that prompt users for the next best action or offer for the customer. Now your big data is delivering real value. Another essential characteristic of data-driven application is closed-loop feedback for immediate actions, such as alerts for a compliance risk, proposed steps to improve data quality or business suggestions to improve customer experience. Putting Big Data to Use Deriving business value from your big data initiatives depends on two key elements: Is your data of good quality and reliable? Does the business facing application present that information in a form that helps in decision management? When you offer big data insights in the context of business operations and as personalized to the front line user, you are delivering demonstrable ROI. It helps users take the right actions based on accurate information. Now your big data, business applications and analytics are not disconnected. Your operational applications and analytics get access to reliable information, and closed-loop feedback makes sure that your data is always clean, current and complete. Article written by Ajay Khanna Image credit by Getty Images, Cultura, Monty Rakusen Want more? For Job Seekers | For Employers | For Influencers
The introduction of artificial intelligence (AI) into the manufacturing value chain is significantly changing the nature of that sector's workforce, according to new research from the MAPI Foundation. But rather than robots taking human jobs, new hybrid roles are emerging where humans enable machines and AI augments human capabilities. Robert Atkinson and Stephen Ezell of the Information Technology and Innovation Foundation (ITIF), and both the authors of " How AI Will Transform Manufacturing and the Workforce of the Future " say that within the next five years manufacturers will see significant growth in AI through machine vision, intelligent products, machine learning, and cobots, both within factories and throughout the supply chain. They project this will lead to a myriad of new types of AI-related jobs in manufacturing. The research was conducted in partnership with ITIF and included a survey of MAPI members to understand the current and future state of AI for manufacturing. According to this survey of U.S.-based manufacturers, currently almost three-fourths of them have not yet introduced new types of AI-related jobs into their companies. In addition, only 20% have comprehensively re-evaluated job roles, titles, levels, and pay scales in recognition of the need to attract employees with AI skills. However, Atkinson and Ezell note this is changing quickly. The study emphasizes that more than 40% of manufacturers have already created "data scientists/data quality analysts" in their workforces, and 35% more expect to do so within the next five years. A sizable proportion of manufacturers are also creating "machine learning engineers or specialists" (33% today, 70% within five years), "collaborative robotics specialists" (29% today, 27% within five years), and "data-quality analysts" and "AI solutions programmers/software designers" (26% today, 40% within five years). "Manufacturing is already facing a worker shortage, and advanced technologies create additional technical and workforce challenges to find and retain talent with the necessary digital skills," observed Stephen Gold, president of the MAPI Foundation. "Companies that acquire and cultivate new digital-related skills will have a distinct advantage as AI reshapes the industry, including identifying new roles for AI-focused jobs such as leading AI strategy and supervising implementations." Six Recommended Elements of the Manufacturing Evolution Atkinson and Ezell share recommendations for business leaders as they integrate new AI-related strategies and technologies: 1. Create teams to drive digital transformation in the enterprise. The digitalization of manufacturing, including the application of AI-based solutions, heralds the most significant transformation to manufacturing in a generation. Manufacturers will need dedicated teams to navigate this transformation, such as digital command centers and digital business teams tasked with leading the deployment of emerging digital technologies. 2. Define an "AI governing coalition" for AI transformation. Manufacturers should define their own AI transformation strategy, with a core mission of assessing the company’s current processes, procedures, production systems, and operations and evaluating how the application of AI-enabled systems could transform and improve them. Companies should establish an “AI governing coalition” of business, IT, HR, and analytics leaders who own responsibility for activities such as setting the direction of AI projects, analyzing problems to solve with AI, and managing internal change. 3. Evaluate AI and workforce transformation readiness. A workforce transformation strategy should consider what AI-specific jobs need to be created and how to provide relevant AI training to employees at every level of the organization. Manufacturers need an inventory of what AI skills the company will need, to ascertain to what extent internal resources can fill these needs, or skills that need to be acquired externally, and develop a plan to train and upskill workers. 4. Set measurable objectives for digital and AI transformation. Companies shouldn’t deploy AI technologies for technology’s sake – all implementations of digital technologies should address a clear business need and be supported by a reasonable return on investment rationale. Manufacturers should define annual objectives for how the application of AI can help meet key performance indicators such as overall operating efficiency and productivity growth. 5. Redefine digital and physical product innovation processes. The advent of digitally-based innovation creates a need to speed time to market, but this presents a challenge to the stage-gate models used to manage product development and innovation cycles. Companies will have to modify their product development processes to accommodate digital transformation while still meeting key safety, reliability, and product quality standards for their finished products. 6. Overinvest in communication for change management. Effective practices include developing a communications process to explain the implications of AI applications and solutions to employees, customers, and partners. Some companies have already set up worker councils to facilitate dialogue between the front office and the front line about how the advent of AI will change workers’ roles and responsibilities in the AI era. "Most manufacturing companies are only beginning to realize the opportunities possible with AI," said Ezell. "Businesses that want to remain on the cutting edge of manufacturing innovation need to implement policies that support and enable the use of the technology throughout their organizations." Learn more about "Building the Future of Work in the Age of AI: How New Jobs and Technologies Will Transform Manufacturing" at MAPI's annual executive summit ManufacturED held Sept. 16-18, 2019, in Chicago. Article published by icrunchdata Image credit by MAPI Foundation Want more? For Job Seekers | For Employers | For Influencers
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