Big data's Influence to the World
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Big data has the potential to provide more comprehensive insights by analyzing larger data volumes and varieties, including unstructured data. This enables analyzing the entire population rather than just samples, allowing correlations to be discovered rather than just causal relationships. Big data is characterized by its volume, velocity and variety - the vast amounts of data being generated and the need to process both structured and unstructured real-time data from diverse sources. Current applications of big data include product recommendations, business consulting, data systems, and data analytics. Analyzing big data will increasingly underpin competition and drive innovation.
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- 87% said 'bad data' pollutes their data stores and 74% said 'bad data' is currently in their stores. Ensuring data quality was the top challenge cited.
- 72% build data flows through hand coding while 53% change pipelines several times per month.
- Only 12% rated their ability to detect issues like stopped pipelines or degraded performance as 'good' or 'excellent'.
- There are significant gaps between the real-time visibility needed and what current tools provide across metrics like error rates, divergent data, and privacy detection.
- 81% said upgrading big data components has significant operational impact.
AI-Led-Cognitive-Data-Quality.pdf
Data Quality Management (DQM) impacts a number of key business drivers, ranging from regulatory
compliances, to customer satisfaction, to building new business models. Quality is one of the key functions
under Data Governance, as unverified/unqualified data has little value to the organization. One of the leading
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02 a holistic approach to big data
The document outlines an agenda for a presentation on big data. It discusses key topics like the state of big data adoption, a holistic approach to big data, five high value use cases, technical components, and the future of big data and cloud. The presentation aims to provide an overview of big data and how organizations can take a comprehensive approach to leveraging their data assets.
Bigdata and Hadoop with applications
Big Data Analytics and Hadoop is presented. Key points include:
- Big data is large and complex data that is difficult to process using traditional methods. Domains that produce large datasets include meteorology, physics simulations, and internet search.
- The four V's of big data are volume, velocity, variety, and veracity. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. Its core components are HDFS for storage and MapReduce for processing.
- Apache Hadoop has gained popularity for big data analytics due to its ability to process large amounts of data in parallel using commodity hardware, its scalability, and automatic failover. A Hadoop ecosystem of
Necessity of Data Lakes in the Financial Services Sector
With the emergence of regulations such as the General Data Protection Regulation from the European Union (effective May 2018), with fines up to 20m Euro, Data Lakes are emerging as the data architecture of choice amongst financial institutions. Banks are embarking on a journey to enable data scientists to unlock the value of the data silo'ed in many disparate data systems. By enabling self service data access and merging multiple streams of data by using data clustering, entity extraction, identity resolution and other techniques - we will show how banks have used Analytics to uncover business value without falling into the abyss of data swamps. The build out of the data lake requires the ingestion of data from multiple operational systems . By leveraging an automated Data Cataloging service, organizations are able to search, profile, discover, tag, track lineage and capture tribal knowledge delivered on the FICO Analytics Cloud enabling the data scientists to build innovative models, make automated decisions, track fraudulent usage, make intelligent marketing campaigns and improve the top line and bottom line for the financial institution.
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Big Data Tools PowerPoint Presentation Slides
The document discusses big data analysis requirements and tools. It covers where big data comes from both internally and externally. It then discusses tools for analyzing big data such as BI tools, in-database analytics, Hadoop, decision management, and discovery tools. Techniques for analyzing big data like classification tree analysis, genetic algorithms, regression analysis, machine learning, and sentiment analysis are also covered. The key benefits and a successful implementation roadmap for big data in an organization are summarized.
Data science and its potential to change business as we know it. The Roadmap ...
The document summarizes a presentation on data science and its potential to change business. It discusses how organizations can increase their data science maturity and capabilities to gain more value from data. As data volumes continue growing exponentially, data science can help organizations move from simple reporting to predictive analytics in order to make real-time decisions. The presentation examines how data science is an emerging field that incorporates techniques from many areas and how organizations can assess their analytics maturity.
Crucial Considerations for AI-ready Data.pdf
This document discusses the importance of ensuring data is ready for AI applications. It notes that while most businesses invest in AI, only 4% of organizations say their data is truly AI-ready. It identifies several issues that can arise from using bad data for AI, including bias, poor performance, and inaccurate predictions. The document advocates for establishing strong data governance, quality practices, and integration capabilities to address issues like completeness, validity, and bias. It provides examples of how two companies leveraged these approaches to enhance their AI and machine learning models. The document emphasizes that achieving trusted AI requires a focus on data integrity throughout the data journey from generation to activation.
Big Data Management: Work Smarter Not Harder
The document discusses strategies for organizations to better manage big data when resources are limited. It recommends identifying unused data in the data warehouse in order to reduce costs by moving that data to cheaper platforms like Hadoop. Organizations can save millions by offloading data that is not frequently queried but must be retained for regulatory reasons. The document also suggests purging data that is not needed at all to further reduce storage and management costs. Proper classification and placement of data onto platforms suited to its usage level and type, such as Hadoop for less critical datasets, can help organizations get more value from their data with fewer resources.
Big Data
The document discusses the growth of big data, defining it as data that exceeds capabilities to capture, manage and process within a tolerable time. It notes global data grew from 800 terabytes in 2001 to over 2 zettabytes in 2012 and is projected to reach 35 zettabytes by 2020. Examples of big data sources include social media posts, web searches, medical records and sensor data. The value of big data lies in making information transparent and usable at high frequency to improve decision making, products/services and productivity. However, big data also faces challenges around skills shortage, privacy concerns and overreliance on past data.
California Data strategy
A Data Strategy discovery and discussion deck for various public entities, state and local governments
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Ponzi schemes, a notorious form of financial fraud, have plagued America’s investment landscape for decades. Named after Charles Ponzi, who orchestrated one of the most infamous schemes in the early 20th century, these fraudulent operations promise high returns with little or no risk, only to collapse and leave investors with significant losses. This article explores the nature of Ponzi schemes, notable cases in American history, their impact on victims, and measures to prevent falling prey to such scams.
Understanding Ponzi Schemes
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Historical Context: Charles Ponzi and His Legacy
Charles Ponzi is the namesake of this deceptive practice. In the 1920s, Ponzi promised investors in Boston a 50% return within 45 days or 100% return in 90 days through arbitrage of international reply coupons. Initially, he paid returns as promised, not from profits, but from the investments of new participants. When his scheme unraveled, it resulted in losses exceeding $20 million (equivalent to about $270 million today).
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1. Bernie Madoff: Perhaps the most notorious Ponzi scheme in recent history, Bernie Madoff’s fraud involved $65 billion. Madoff, a well-respected figure in the financial industry, promised steady, high returns through a secretive investment strategy. His scheme lasted for decades before collapsing in 2008, devastating thousands of investors, including individuals, charities, and institutional clients.
2. Allen Stanford: Through his company, Stanford Financial Group, Allen Stanford orchestrated a $7 billion Ponzi scheme, luring investors with fraudulent certificates of deposit issued by his offshore bank. Stanford promised high returns and lavish lifestyle benefits to his investors, which ultimately led to a 110-year prison sentence for the financier in 2012.
3. Tom Petters: In a scheme that lasted more than a decade, Tom Petters ran a $3.65 billion Ponzi scheme, using his company, Petters Group Worldwide. He claimed to buy and sell consumer electronics, but in reality, he used new investments to pay off old debts and fund his extravagant lifestyle. Petters was convicted in 2009 and sentenced to 50 years in prison.
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Confirmation of Payee was built to tackle the increasing numbers of APP Fraud and in the landscape of UK banking, the spectre of APP fraud looms large. In 2022, over £1.2 billion was stolen by fraudsters through authorised and unauthorised fraud, equivalent to more than £2,300 every minute. This statistic emphasises the urgent need for robust security measures like CoP. While over £1.2 billion was stolen through fraud in 2022, there was an eight per cent reduction compared to 2021 which highlights the positive outcomes obtained from the implementation of Confirmation of Payee. The number of fraud cases across the UK also decreased by four per cent to nearly three million cases during the same period; latest statistics from UK Finance.
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
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A toxic combination of 15 years of low growth, and four decades of high inequality, has left Britain poorer and falling behind its peers. Productivity growth is weak and public investment is low, while wages today are no higher than they were before the financial crisis. Britain needs a new economic strategy to lift itself out of stagnation.
Scotland is in many ways a microcosm of this challenge. It has become a hub for creative industries, is home to several world-class universities and a thriving community of businesses – strengths that need to be harness and leveraged. But it also has high levels of deprivation, with homelessness reaching a record high and nearly half a million people living in very deep poverty last year. Scotland won’t be truly thriving unless it finds ways to ensure that all its inhabitants benefit from growth and investment. This is the central challenge facing policy makers both in Holyrood and Westminster.
What should a new national economic strategy for Scotland include? What would the pursuit of stronger economic growth mean for local, national and UK-wide policy makers? How will economic change affect the jobs we do, the places we live and the businesses we work for? And what are the prospects for cities like Glasgow, and nations like Scotland, in rising to these challenges?
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Economic Risk Factor Update: June 2024 [SlideShare]
May’s reports showed signs of continued economic growth, said Sam Millette, director, fixed income, in his latest Economic Risk Factor Update.
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◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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校名:学校英文全称
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Monthly Market Risk Update: June 2024 [SlideShare]
Markets rallied in May, with all three major U.S. equity indices up for the month, said Sam Millette, director of fixed income, in his latest Market Risk Update.
For more market updates, subscribe to The Independent Market Observer at https://blog.commonwealth.com/independent-market-observer.
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Big data's Influence to the World
- 1. Big Data Big Data Change the Way we Think Past Now Sample Population Structured Unstructured Analysis based on samples can lead to errors because of missing or incorrect data and wrong sampling methods. Because of more accessible data, more information can be drawn from population. Only 5% of the data is structured and can be analyzed using traditional methods, but more unstructured data can be analyzed and give out more information right now. The data analysis moves from “cause-result” analysis to “correlation effects” analysis. Cause Correlation
- 2. Big Data’s Potential Value Volume, Velocity, Variety More Volume More Velocity More Variety The estimated dataflow will be up to 8 trillion GB in 2015 Real-time data inflow from high-speed Internet servers Unstructured data type including video, browsing history, etc. Less delay in collecting and processing data Access data that is previously inaccessible due to its unstructured nature Real-time updates in databases for decision making More comprehensive picture about individual behaviors and interests Clearer view about potential resources and target customers Larger sample base to conduct statistical and consumer behavior analysis
- 3. Big Data Usage Current Usages of Big Data Product Sales Business Consulting • Amazon • Google • IBM • Oracle Data Systems • Cloudera • Factual Data Analytic • Palantir
- 4. Conclusion Remarks The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. Thank you for watching