Data Science


5 requirements for building a strong data culture

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With virtually all enterprises investing more heavily in data analytics, big data, and AI projects these days, company executives overwhelmingly report that they are trying to shift their organizations to a data-driven culture. However, only about a third of executives say they've succeeded. Cultural shifts are complicated endeavors, and a shift to a truly data-driven culture is as much about people as it is about technology. Let's take a look at five areas where executives need to focus on laying the right groundwork for change. Executives can get started on the path to a strong data culture by empowering their teams to not only embrace change but to be catalysts for it.


Newcastle smart city moves forward with standardised data platform

ZDNet

The City of Newcastle has signed up to a single smart cities Internet of Things (IoT) enterprise platform from the National Narrowband Network Company (NNNCo), the company has announced. "The city standardised on the middleware platform as it prepares to roll out and scale multiple smart city applications," NNNCo said. "The deal between NNNCo and Newcastle City Council includes an agreement to run thousands of IoT devices through the platform for multiple city use cases." As part of the Newcastle City Intelligent Platform implementation, NNNCo will also provide its N-tick device certification program across all devices being deployed across the city. NNNCo CEO Rob Zagarella called the use of one platform and device certification program for an entire city a "breakthrough in the IoT market".


Scaling Agile, adopting AI: How Intel is making IT a strategic part of the business

ZDNet

Intel on Wednesday released its 2018-2019 IT performance report, giving an inside look at how the semiconductor business is transforming its IT operations to serve as a strategic part of the overall business. The report details the maturity of Intel's DevOps strategy and its ongoing efforts to scale Agile and DevOps practices. It also shows the progress Intel has made bringing machine learning into its operations. A large part of driving change within IT involves creating the right culture, Intel's chief data officer Aziz Safa told ZDNet. "Twenty years ago, we would not make a major change in the enterprise for years," he said.


Forrester: The Changing Role of Self-Service in Predictive and Advanced Analytics

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Martha serves CIOs and other tech leaders, helping them understand the impact of emerging technologies on their business. Martha provides in-depth coverage of blockchain technology and business intelligence (BI), as well as analytics and artificial intelligence at a strategic level. In her blockchain research, Martha focuses on demystifying this emerging technology, helping CIOs and strategy teams identify appropriate use cases and navigate the broad ecosystem of startups and established providers offering software and services. In her BI research, Martha analyzes the effect of emerging technologies and business pressures on the way BI capabilities are managed and delivered, and she helps CIOs and their business partners develop data and analytics strategies fit for the digital age.Martha earned an M.A. in English literature, American studies, and modern history from the University of Erlangen-Nürnberg in Germany. A qualified translator, Martha is fully bilingual in English and German.


Big data needs an ethical framework, says ICAEW

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The institute is calling for businesses, consumers and regulators to work together to protect vulnerable people from being exploited as a result of advances in technology. The growing use of big data (which includes data from social media, video watching habits, location data and photos to satellite images, traffic flows and data from smart devices) and artificial intelligence (AI) is increasingly changing financial products like credit cards and insurance policies – as well as the business models of the companies that provide them. It warns the changes will produce both winners and losers, and says banks, insurers and other financial services providers need to start managing these tensions now, before products and services spiral away from their true social purpose. Without an ethical approach to using big data, people could be unfairly or illegally excluded from vital financial services, leaving them without access to insurance or credit, owing to factors they cannot control like gender, ethnicity, where they live, or health conditions. This will also be influenced by factors like where they shop, whether they use cash instead of cards and other information.


Optimizing Data-to-Learning-to-Action - Programmer Books

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This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today's business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector. You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successfully addressing the bottlenecks, providing the credible support needed to make the right level of investments at the right place and at just the right time. In today's dynamic environment, with its never-ending stream of new, disruptive technologies that executives must consider (e.g., cloud computing, Internet of Things, AI/machine learning, business intelligence, enterprise social, etc., along with the associated big data generated), author Steven Flinn provides the comprehensive approach that is needed for making effective decisions about these technologies, underpinned by credibly quantified value.


Top 5 tech skills data scientists need, and how to learn them

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Data scientists remain in high demand, but those interested in pursuing a career in the field must have the right skillset to land a job with a top salary, according to a Thursday report from Indeed Prime. Demand for data science professionals continues to rise as more companies seek to collect and analyze data and draw business insights from that information. Data scientist job postings have increased by 256% since December 2013, and median base salaries have reached $130,000, according to Indeed data. As more companies adopt data-driven approaches, data scientists must keep their skills current based on what employers need, the report noted. Indeed Prime analyzed the most asked-for skills in Indeed job postings.


How a small company can make use of data

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Against this backdrop, the way in which small businesses use their data and, by extension, use Artificial Intelligence (AI) has become a hotly discussed business issue. But can a small business ever truly unlock the power of its data? Given obvious resourcing constraints is it a realistic aspiration for a small business or should they focus their attention on other things until they can use their data effectively and with better resources? According to a recent report by Microsoft UK, organisations investing in developing their ethical use of AI are already outperforming those that are not by 9%. Clearly, the effective use of AI and data science can be game-changing, but it is not only small businesses that are struggling to deploy data science.


Using Machine Learning To Improve Restaurant Management

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Machine learning takes artificial intelligence (AI) to the next level by allowing a system to learn without prior programming. Now, restaurants are starting to benefit from this technology. Simon Bocca, COO at Fourth, shared how his company is using machine learning. Fourth recently announced its end-to-end restaurant and hospitality platform and services. The company provides an all-in-one solution for purchase-to-pay, inventory and workforce management with advanced demand forecasting, predictive analytics and collaboration tools.


The Missing Link Between Machine Learning & Enterprise – GeekWire

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Machine learning models come in many forms and cater to many use cases, and the proliferation of machine learning over the past decade has happened in three stages. First, there were breakthroughs in deep learning and the complementary emergence of cloud computing and big data. The second stage has been the creation and standardization of tools and frameworks for machine learning development. And most recently the deployment and in-life support of machine learning has come to the fore. But is this enough, or is there something missing?