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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
(This is Part 3 of a Sports Analytics series. Read Part 1 and Part 2 .) "Save a boyfriend for a rainy day  –   and another, in case it doesn’t rain." — Mae West A rain delay must last at least 30 minutes before the umpire cancels a Major League Baseball game. But it hasn’t been the weather that has been causing low turnout for Major League Baseball. Major League Baseball has been fighting dwindling attendance for years. Recently, of the league’s 30 teams, 18 are experiencing an attendance drop . And this is after a 2018 season in which attendance was down more than three million fans, an average of 1,237 per game. The reason? Some say it’s because the game has become boring. In a world of technology and social media, a game that lasts three hours with nine innings, three strikes and you’re out if the pitching is good, is no longer compelling or attention-grabbing. However, it’s not the only sport that’s fighting an image problem. National Football League is being perceived as becoming too political. Last year, 103.4 million people watched the big game, the smallest audience since 2009. Some owe this fact to the politics of both the players and team owners . National Basketball Association players believe the referees are ruining the game. Players are 29.3% in agreement that referee inconsistency in playing calls are disrupting the outcome of games. The Women’s National Basketball League is having its own issues as female players are battling for the same pay for the same play as their male counterparts. The brightest spot in sports is happening with the USA Women’s Football team after their winning streak during the World Cup in France. Fans of the USA Woman’s Football team sold out every stadium they played in France during the World Cup. Nike sold out of USA Women’s Soccer Team jerseys for all sizes and sexes but first selling out for men. In contrast, the USA Men’s Team barely had an attendance of 26,233 in the match between US-Curaçao and tickets for the double-header game were just $30. Is there one magical, unicorn solution that could help all the woes of all the major sports teams? Yes. And it indeed is magical. Just ask Ali Krieger and Ashlyn Harris of the U.S. women’s national soccer team: Love. By partnering with dating apps, sporting events may become the hottest place, pick-up spot, and go-to date night for 20, 30, 40, and 50-somethings. This new trend is a mix of analytics and psychology entitled social seating. The analytics for social seating is very different than most data sciences. It requires a new applied method called: trans-analytics pioneered by Qualex Consulting Services. How does trans-analytics work? It’s similar to the way first dates happen: One person offers information about themselves to coax out information from the other. This information intermixing together creates a bond or repels (like a magnet behaves) data into a new direction. Trans-analytics’ superpower is that it is centered on data transparency as opposed to traditional analytics being a black box of algorithms that no one is privy to seeing how the gears work. Trans-analytics uses a Huckleberry Finn way of perfecting a model: Trans-analytics performs basic analytics on patrons, data volunteered participants and progressively gets more and more complicated and complex by showing back to them the analytics you have done on them. Think of it as an algorithm mirror that they can correct in real-time. Or opt-out immediately if they are not happy with what it’s showing them. But the tweaking of the algorithm or the model pays dividends back to the person who is volunteering by allowing them to tighten focus on a particular outcome. So basically it’s what Huck Finn did when he was punished to paint a fence – he got his friends to pay him to do it because he said it was fun to do. In the end, Huck Finn brought together a group of similar type of people for a specific event. Social seating is not just locked to dating and hooking up. It’s been used on airlines to put mothers with babies in particular sections of the cabin. It’s been used for social networking events to have the same types of businesswomen and men with backgrounds in particular industries to be seated around one another. The difference with social seating in regard to using dating apps is the ability to link ticket sales, retail purchases, and food and beverage purchases on the premise of sports properties to specific demographics. However, it wouldn’t be in a creepy, stalker way. Singles would be volunteering information and tweaking analytical flowcharts to find: The One. Social seating is the algorithmic cousin of a flash mob. It brings like-minded people to find each other and experience a once and lifetime event. Imagine coming to a game knowing that your future date or future spouse will be in the designated social seating stadium seats in front of you or behind you. Or better yet beside you. Seat reservations based on specific factors that you want in a dating partner: drinks socially, loves hip hop but hates Star Trek. Suddenly a stadium becomes destiny. And a rain delay becomes a rainbow. Article written by Gary Jackson Image credit by Getty Images, Tetra, Steve Prezant Want more? For Job Seekers | For Employers | For Influencers
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