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Data is all-powerful. Data is all-encompassing. Data is all… too intimidating to use? This seems to be the case at many organizations, including those deploying people analytics solutions to address recruitment, onboarding, training and other HR and talent management needs. More than three quarters of companies are investing or are planning to invest in big data initiatives, according to Gartner. However, 72 percent of organizations are gathering data that they never use , according to survey research from Pure Storage. Why? Because they feel that data processing is too time consuming (as cited by 48% of survey respondents); they lack the internal skills to do so (46%); and/or they don’t have the proper tools to sufficiently process data (30%). All of which creates the impression that you can’t derive value out of talent management or people analytics because the data is too big or too complex to tackle – or both. Thus, too much of it sits somewhere in your network, untapped of its great potential. So how do you reverse this scenario – to not only “tame” people analytics and Big Data but fully optimize it as a strategic resource? By pursuing what I call the following Three Stages of HR Data Empowerment: 1. Presentation Did you ever attend an industry seminar of sorts in which a really smart person was doing the presentation? And you realized the presenter had plenty of “good stuff” to offer – yet you learned nothing because he or she couldn’t convey the key thoughts in a clear, accessible way? Let’s face it: Given as little as you take away from these sessions, the presenters could just as well be speaking in a foreign language. Well, HR-based analytics solutions can be a lot like this. So you should invest in products designed with simple, utilitarian dashboard displays that any staffer – including the least “techie” of employees – can immediately grasp and start using. Streamlining the data goes a long way here. When you pick up a magazine off the rack, after all, you usually go right to the sections and articles that are most relevant to you and your interests or informational needs. In the same manner, users should be able to manipulate analytics tools so that only the information that is important to them and/or their role is depicted. 2. Collaboration and Sharing At this stage, your solution’s presentation has eliminated the “data intimidation” factor. Even relatively technophobic staffers feel comfortable taking a “deep dive” to obtain previously unavailable insights – and, every day, their enthusiasm for analytics grows. What’s more, the enthusiasm is contagious, and your entire organization is constantly making new discoveries via analytics. To take full advantage of the “building buzz,” your tools must make it easy for users to share and collaborate. When a person gains knowledge that can enhance talent management policies and practices, it’s a good thing. But when a person gains such knowledge and shares it enterprise-wide, it’s a better thing. And when employees throughout your organization – no matter where they work in the world – collaborate upon the knowledge and expand its value through comments and recommendations, the potential for tangible impact is limitless. 3. Storytelling By now, you’ve completed the first two stages of data empowerment. As part of their daily routine, your talent management team members are extracting new information which greatly supports them in their accomplishment of HR objectives, with maximum efficiency and cost-affordability. They’re sharing and collaborating upon the information to further extend its capacity to make a difference. This is when data ascends from being “a huge collection of numbers” to something more transcendent. Indeed, it has emerged as a “storyteller,” revealing to users new thematic threads about the most promising regions/colleges for recruitment; the likeliest department areas for retirements (i.e. those requiring proactive succession-planning); and the best programs for onboarding, training, etc. For the first time, users gain total visibility of “storylines” within the past and present to make insightful, actionable decisions about the future. For them, data once resembled an indecipherable code. But the code has been “broken,” and they now see pattern after pattern that significantly augments your organization’s ability to recruit and manage talent. Data can do so many things, but not if we continue to conclude that only a scientist can make sense of it. When your talent management solutions demystify effective people analytics for everybody – even staffers who consider themselves tech-challenged – through the three described stages, you’ll truly empower all of the information you own. And you’ll empower your HR team members as a result. Article written by Joe Abusamra Image credit by Getty Images, Cultura, Monty Rakusen Want more? For Job Seekers | For Employers | For Influencers
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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