Kognitio spark modern data platform print

The document discusses the need for "Smart Data" over "Big Data". It argues that while Big Data has potential, the technology is constantly evolving and data scientists with the needed skills are scarce and expensive. It advocates for providing business users with self-service analytics capabilities to answer over 90% of their questions quickly without IT assistance. Examples are provided of how self-service analytics helped Coca-Cola and JD Group gain insights from their data more efficiently.

致詞歡迎：Big Data 無所不在，Data Technology 無 C 不歡

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This document contains the opening remarks from Lin Longfen, the general manager of Jingcheng Group. It discusses how Gartner dropped "Big Data" from its hype cycle of emerging technologies in 2015 because it is now considered a mainstream part of many industries. Big data is still essential for major trends like the Internet of Things, Industry 4.0, and smart everything. The document emphasizes that understanding customers ("C") is key to a company's ("B") competitive advantage in the digital economy, and that leveraging industry data is a common development strategy across Jingcheng Group's business units.

MySciencework - SSP Conference 2018

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The document discusses how data, text mining, and machine learning can be used for better decision making. It summarizes MyScienceWork, a company that provides data solutions for research. Key trends discussed are data management, peer review methods, and improving visibility and discoverability of data through open repositories and powerful semantic search tools using AI for suggestions and metrics. The presentation concludes that the value of data requires significant work and explores whether AI can replace humans.

How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns. Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler

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This document discusses big data and how organizations can better utilize the large amounts of data they collect. It argues that most organizations currently only access a small percentage of the data they collect and do not realize its full value. The document advocates for a approach involving deep learning, predictive analytics, discovery analytics, and data visualization to generate real insights from data. It also discusses how predictive analytics can be used to predict the future by analyzing vast amounts of real-time commentary with machine learning algorithms.

BDAS-2017 | Hanldling Target Bias in Predictive Modelling

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Todos cometemos errores cuando desarrollamos modelos predictivos y es así como nosotros aprendemos. Frank Vanden Berghen, CEO of Timi, y Daniel Soto Zeevaert (Director Lat Am of Timi) con más de 30 años de experiencia cometiendo errores en la construcción de modelos predictivos nos compartirán su experiencia y muchas formas creativas en que los analistas pueden equivocarse y construir malos modelos predictivos inclusive utilizando las mejores herramientas y algoritmos.

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Martin Szugat gave a guest lecture on developing a data strategy. He explained that a data strategy guides how a business uses data and analytics to make decisions and gain value. It involves understanding user and business needs, exploring available data sources, and developing analytics use cases and a roadmap to increase the maturity of data-driven processes, products, and business models over time. Szugat also discussed common reasons why many data projects and products fail, and emphasized the importance of designing data strategies using proven approaches like CRISP-DM to ensure business impact, user acceptance, and feasibility based on available data.

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Data Insights is a growing startup company based in Munich that provides analytics, big data, and data-driven product expertise. They focus on big data using the 3V model of volume, variety, and velocity. They are always looking to hire talented data engineers, data scientists, and information management specialists. The company offers competitive benefits and expects employees to be highly motivated, constantly progressing, and able to communicate in German and English.

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You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care? Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.

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The document discusses big data analytics and pitfalls to avoid. It covers the seven V's of big data, including volume, velocity, variety, veracity, value, virtual, and variation. It also discusses the KARMA framework for big data analytics, which stands for knowledge, strategy, action, recognition, and market/advance. The document provides examples of pitfalls such as lack of knowledge, too much data, silo culture, and established data warehouses. It also presents a case study on using big data analytics to identify distributed denial of service attacks. Throughout, it emphasizes finding "gold" in big data and leveraging open source tools to enable big data analytics.

Big data

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Big data refers to large and complex data sets that are difficult to manage and analyze using traditional data management tools. It is generated from various sources like social media, scientific instruments, mobile devices, and sensor technology. Big data provides opportunities for insights and smart solutions but also poses challenges in processing, analyzing, and gaining insights from such large volumes of data. For managers in India, big data is highly relevant as the Indian analytics industry is growing rapidly and is expected to reach $16 billion by 2025, with big data being a major driver of growth in industries. Digitalization is also expanding the big data market in India as internet and smartphone usage increases across more regions of the country.

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This document discusses the big data analytics market opportunity. It notes that the volume of data from various sources is growing exponentially. It then outlines the life cycle of big data, reference architectures, and characteristics of big data. It discusses drivers of big data, pain points for enterprises, and the market opportunity for big data analytics. It predicts strong growth in spending on big data analytics and outlines types of analytics initiatives and trends in big data technology.

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1) Anonymizing data is difficult and often destroys the utility of the data. High-dimensional data like images are nearly impossible to anonymize effectively. 2) Synthetic data generated by AI can provide a statistical representation of real private data while preserving over 99% of the value for analysis. It allows unrestricted sharing and use of data. 3) Case studies on public datasets like the US Census and mobility data show synthetic data matches the real data distribution and statistics closely while being fully private. Synthetic data enables new use cases for data sharing, analytics and innovation.

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Eneco's data science program has expanded its team of data scientists and established processes for innovation. It has set up data lakes to store customer and asset data while ensuring privacy. Several business units have seen significant returns, with predictive models in areas like boiler monitoring, solar energy optimization, and personalized thermostat assistance. Lessons learned include starting small but thinking big, letting business needs lead, and managing hype around new technologies.

How artificial intelligence (AI) can help maximize customer intelligence ROI

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This document discusses how artificial intelligence can help maximize customer intelligence ROI. It provides an overview of customer intelligence and discusses how AI is enabling more data, more predictive analytics, and more automation in customer intelligence. Specific use cases discussed include churn prediction and next best action recommendations for a large European bank, product recommendations for a B2B software vendor, and large-scale consumer insights for a global CPG company. The document concludes with best practices for AI projects, emphasizing defining processes, including business stakeholders, exploring and iterating on features, combining supervised and unsupervised machine learning, and robust deployment.

Kognitio Spark! Modern Data Platform

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