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I have to admit, I am kind of a history buff. For example, I was just reading that a new North American Viking site was discovered from – of all places – space. Pretty amazing! From a historical perspective, as the Vikings moved southward, they were looking for better places to grow crops and create settlements. However, the further south they went, the more indigenous populations they encountered. The Vikings had many competitive advantages against the first North Americans, including ship building, metal weapons and even superior battle tactics. But as they faced larger adversaries, the Viking penetration of North America was blunted. It was only later when there was an overwhelming competitive advantage, including all forms of firearms, that Europeans gained a real foothold in the New World. If we look at enterprises today, overwhelming competitive advantage is about to displace existing marketplace competitors. Many call it the Uber effect. Most enterprises survived Web 1.0 by simply putting a Band-Aid on their existing systems and creating a Web server to the outside world. This response was much like the response of New World peoples. Today, however, we are seeing startup companies competing in ways where simply adopting new tactics quickly will not work. Companies with data at their core are going to win unless existing market players fix their data – in particular, their company data – and enable predictive models to guide, for example, how they interact with customers. Clearly, existing companies are playing with their back against startups with data chops. Most find themselves saddled with historical legacy investments that cannot keep up with the speed of today’s business. What to Fix In order to compete and eventually win, the top of the organization first has to make a commitment to data, analytics and putting value-added applications at the top of relevant data sources. For some, this means realizing for the first time (regardless of what their foundational business is) that they are a technology/software/data company. According to Alan Murray, editor of Fortune Magazine , Monsanto and John Deere have announced a deal to stream real-time data on soil and crop conditions. According to Alan, “If John Deere is a tech company, who isn’t?” The next thing to counter startups is for companies to put data at their core. For many, this starts by committing to fix key elements of their existing data. How can you compete on customer experience or provide predictive offers or dynamic adjust order rates unless you have a complete view of your customers and their relationship with you? Once you have fixed your company data, three more things need to be done simultaneously: Expenditure needs to be freed up from running the business IT. It costs real money to build predictive models to improve your supply chain or provide IoT-type solutions. This tends to involve upgrading and eliminating applications that are no longer relevant. Through all of the IT value chains, huge costs are contained in just keeping the lights on by maintaining applications, server capacity, storage capacity and network connectivity. Taking these costs out can allow existing market players to start to invest. IT needs to get closer to the business. If they don’t have a business technology vision, then IT needs to get closer to business change agents. IT needs to enable them and it needs to invest increasingly in level 2 investments—new data sources, big data lakes and self-service data evaluation capabilities. IT needs to be an enabler of predictive analytics models to provide offers to customers that matter and, where appropriate, value-added software and applications. Competitive Advantage As part of this, IT leadership needs to view themselves as venture capitalists. They need to look for the change-the-business investments with the highest business payoffs. Linear work needs to be done but should not be the driver of IT investment levels. IT, of course, can go further by asking, “Why do we have a datacenter? Can others do this function better? What do we do that leads to business competitive advantage or detracts from it?” This last question is the most important. IT needs to do the things that drive business advantage. By doing this, the business can gain the competitive advantages needed to go against a disruptor or a startup. Put simply, don’t let startups gain an overwhelming competitive advantage. This means data and analytics need to be made a priority before an Uber enters your business and attempts to rewrite the rules of existing business competition. The time is now to get your data and analytics act together. Further Reading Competitive strategy: Being “stuck in the middle” doesn’t have to be permanent Leaders championing digital transformation start by fixing the data it is built upon NASDAQ says winners aren’t satisfied with good data. How to move from good to great data? Article written by Myles Suer Image credit by Getty Images, DigitalVision Vectors, erhui1979 Want more? For Job Seekers | For Employers | For Influencers
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
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