O uso de grandes bancos de dados torna-se, cada vez mais, crucial para a sobrevivência das empresas.– Que tipo de profissional o mercado mais procura?
Será que o estatístico está preparado para atender à demanda desse tipo de trabalho?
– O que o mundo está dizendo sobre isso?

Compilamos aqui referências e links das mais diversas discussões em torno desse assunto e que, certamente, poderão ser aproveitados pelos estatísticos que querem entrar para esse mercado.


  • Um fenômeno chamado big data – A possibilidade de analisar um volume inédito de dados digitais — chamado de big data — é, para as empresas, uma revolução comparável à popularização da internet. By Luiza Dalmazo, de EXAME / TI | 06/10/2012 (o artigo não menciona o estatístico, mas certamente traz muitas ideias que servirão de ponto de partida/objetivo para nossos profissionais)
  • Como a era do Big Data impacta a carreira dos estatísticos  – Resumo das palestras da VII Semana de Estatística, promovida Instituto de Ciências Matemáticas e de Computação (ICMC) da USP e da UFSCar, em 2017, traça um panorama dos desafios da área aos interessados
  • Big data: The next frontier for innovation, competition, and productivity – The amount of data in our world has been exploding. Companies capture trillions of bytes of information about their customers, suppliers, and operations, and millions of networked sensors are being embedded in the physical world in devices such as mobile phones and automobiles, sensing, creating, and communicating data. Multimedia and individuals with smartphones and on social network sites will continue to fuel exponential growth. Big data—large pools of data that can be captured, communicated, aggregated, stored, and analyzed—is now part of every sector and function of the global economy. Like other essential factors of production such as hard assets and human capital, it is increasingly the case that much of modern economic activity, innovation, and growth simply couldn’t take place without data. By James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers – McKinsey Global Institute, May 2011
  • Big Data and the Role of Statistics – The White House Office of Science and Technology Policy is hosting a “Big Data event” tomorrow, March 29, at AAAS and will feature representatives from NSF, NIH, NIST, DOD, DARPA and DOE. Other than the speakers, there is little available about what will be announced. We’ll keep you informed as information becomes available. In the meantime, we want your input. There is no question Big Data has hit the business, government and scientific sectors. Unfortunately, the role of statistics seems too often to be undervalued. Instead, computer science, applied math or other fields are frequently mentioned as the pertinent scientific discipline while statistics is often left out. This piece is co-authored with Ron Wasserstein, Executive Director for the American Statistical Association (ASA). Blog Viewer – March 28, 2012.
  • Statistics Ready for a Revolution – Next Generation of Statisticians Must Build Tools for Massive Data Sets. The statistics profession has reached a tipping point. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready for a revolution, one driven by clear, objective benchmarks by which tools can be evaluated. The new generation of statisticians must be ready to take on this challenge. They have to be dynamic and thoroughly trained in statistical concepts. They have to work effectively on an interdisciplinary team and understand the immense importance of objective benchmarks to evaluate statistical tools. They have to produce energetic leaders who stick to a roadmap, but who also break with current practice when necessary. By Mark van der Laan, Jiann-Ping Hsu/Karl E. Peace Professor in Biostatistics and Statistics at UC Berkeley, and Sherri Rose, PhD candidate at UC Berkeley, 1 September 2010 – AMSTAT NEWS.
  • The Age of Big Data – GOOD with numbers? Fascinated by data? The sound you hear is opportunity knocking. Mo Zhou was snapped up by I.B.M. last summer, as a freshly minted Yale M.B.A., to join the technology company’s fast-growing ranks of data consultants. They help businesses make sense of an explosion of data — Web traffic and social network comments, as well as software and sensors that monitor shipments, suppliers and customers — to guide decisions, trim costs and lift sales. “I’ve always had a love of numbers,” says Ms. Zhou, whose job as a data analyst suits her skills. By Steve Lohr, NYTIMES – NEWS ANALYSIS, February 11, 2012.
  • An ambitious experiment in Data Science takes off: a biased, Open Source view from BerkeleyToday, during a White House OSTP event combining government, academia and industry, the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation announced a $37.8M funding commitment to build new data science environments. This caps a year's worth of hard work for us at Berkeley, and even more for the Moore and Sloan teams, led by Vicki Chandler, Chris Mentzel and Josh Greenberg: they ran a thorough selection process to choose three universities to participate in this effort. The Berkeley team was led by Saul Perlmutter, and we are now thrilled to join forces with teams at the University of Washington and NYU, respectively led by Ed Lazowska and Yann LeCun. We have worked very hard on this in private, so it's great to finally be able to publicly discuss what this ambitious effort is all about. BLOG Fernando Perez – Thoughts and notes on open scientific computing, with a focus on Python-based tools (IPython, numpy, scipy, matplotlib and friends), Tuesday, November 12, 2013.
  • The Big Data Brain Drain: Why Science is in TroubleRegardless of what you might think of the ubiquity of the "Big Data" meme, it's clear that the growing size of datasets is changing the way we approach the world around us. This is true in fields from industry to government to media to academia and virtually everywhere in between. Our increasing abilities to gather, process, visualize, and learn from large datasets is helping to push the boundaries of our knowledge. Pythonic Perambulations – Musings and ramblings through the world of Python and beyond, OCT 26, 2013.
  • The Rise of the Data Scientist – A New breed of database expert bridges business object5ives and bita data analytics. If current forecasts prove correct, some 50 billion devices—from cars to household appliances to phones—will be generating data and silently communicating with each other by the end of this decade. Being able to analyze this data tsunami will be critical for organizations that rely on information for insights and decision making. McKinsey Global Institute, the research branch of global management consulting firm McKinsey & Company, has identified big data analytics as an activity that’s becoming vital for business competitiveness and growth in its report “Big data: The next frontier for innovation, competition, and productivity.” By John Edwards, Teradata Magazine Online, Q3 2012.
  • 'Big Data' Means Business Needs Mathematicians – The era when all the data a business gathered on itself could be accommodated by a single spreadsheet is coming to a close. The proliferation of ways to measure things — point of service terminals, web analytics, geographic and temporal records, even semantic information — means businesses are drowning in data. This has led to a new class of engineer, the "data scientist," whose job it is to perform the sophisticated mathematical gymnastics required to extract actionable information from this mass of numbers. According to mathematician Cathy O'Neill, the skills of a data scientist include not only crunching numbers, but also visualizing the results. By Christopher Mims, September 27, 2011, MIT Technology Review.
  • A Data Deluge in April, April was a busy month for those involved in the mathematical aspects of data science. As regular readers of SIAM News will know, April is Mathematics Awareness Month and the 2012 topic was Mathematics, Statistics, and the Data Deluge (see www.mathaware.org). An opportunity to address some of the more challenging questions in this field, while raising others, came at the 12th SIAM International Conference on Data Mining, held April 26–28, in Anaheim, California. The popularity and timeliness of the topic were reflected in the best attendance at the conference so far: nearly 300 participants, with papers presented by a set of authors who, true to the conference name, had come from Australia, Belgium, China, Germany, Italy, Japan, Netherlands, Singapore, Switzerland, Turkey, the UK, and the US. By Chandrika Kamath, SIAM NEWS, July 17, 2012
  • The limits of Big Data – Two new books argue that algorithms can't fully replace human judgment. A review of Samuel Arbesman's The Half-Life of Facts and Nate Silver's The Signal and The Noise. FORTUNE — Stop me if you've heard this one: Three statisticians go rabbit hunting. They spot a rabbit. The first statistician shoots. He misses the rabbit's head by a foot. The second statistician fires; misses the rabbit's tail by a foot. The third statistician cries out, "We got him!" By Michael Schrage, contributor, CNN MONEY October 10, 2012
  • Big Data Reaches The Hill: A Guide To Making It More Actionable – Big data, which has been the hot topic for conferences this year, has also received a good deal of attention on Capitol Hill in recent weeks, most notably with two recent events: The ACT-IAC discussion with Congressional staff members on Big Data at the Hill – Defining and Understanding Policy Implications; The TechAmerica Foundation's release of its Big Data Commission report, "Demystifying Big Data: A Practical Guide to Transforming the Business of Government," "roadmap to using big data to better serve the American people." By Brand Niemann, AOL GOVERNMENT / TECHNOLOGY, October 10, 2012
  • A Statistician’s View of Big Data, By Max Kuhn, Ph.D (Pfizer Global R&D, Groton, CT) and Kjell Johnson, Ph.D (Arbor Analytics, Ann Arbor MI) – www.cs.yale.edu
  • Education, statistics and the big data future. The RSS and its members have a vital role to play in ensuring that ‘computing education’ in schools provides a sound foundation that enables responsible data analysis, says Tom King. RSSeNEWS – April 5, 2012 – Royal Statistics Society
  • Q&A: Statistician Nate Silver talks big data, sports analysisIDG News Service – New York-based political statistician and author Nate Silver was a special guest speaker at IBM's Information On Demand conference. Big data, algorithms and sports analysis were among the topics of discussion. Conference MC Jason Silva conducted a question and answer session with Silva during the keynote. By Hamish Barwick, COMPUTERWORLD, October 26, 2012
  • Data Scientist – Interview – This week we sat down with +Rachel Schutt, a Statistician in Google’s New York research group, also currently teaching a course in Data Science at Columbia University. We were interested to hear her views on the emerging field of Data Science, the complexities of gaining insight from massive data sets, and what motivates her to educate the next generation of problem solvers. Research at Google – Oct 25, 2012.

    • Next-Gen Data Scientists – The following is a prologue to a discussion of what makes for a good data scientist. Data is information and is extremely powerful. Models and algorithms that use data can literally change the world. Quantitatively-minded people have always been able to solve important problems, so this is nothing new, and there’s always been data, so this is nothing new. But what is new is the massive amounts of data we have on all aspects of our lives, from the micro to the macro. The data we have from government, finance, education, the environment, social welfare, health, entertainment, the internet will be used to make policy-decisions and to build products back into the fabric of our culture. I want you, my students, to be the ones doing it. I look around the classroom and see a group of thoughtful, intelligent people who want to do good, and are absolutely capable of doing it. By Rachel Schutt, October 4, 2012, In Ethics and Humanity, Models – Blog to document and reflect on Columbia Data Science Class. Introduction to Data Science, Columbia University.
    • 10 Important Data Science Ideas – Here’s a list of 10 important ideas we’ve explored this semester so far. By Rachel Schutt, October 15, 2012, In Defining Data Science – Blog to document and reflect on Columbia Data Science Class. Introduction to Data Science, Columbia University.
    • Columbia data science course, week 1: what is data science? – I’m attending Rachel Schutt’s Columbia University Data Science course on Wednesdays this semester and I’m planning to blog the class. Here’s what happened yesterday at the first meeting. By Cathy O'Neil, mathbabe.org – I’m attending Rachel Schutt’s Columbia University Data Science course on Wednesdays this semester and I’m planning to blog the class. Here’s what happened yesterday at the first meeting. September 6th, 2012.
    • Columbia Data Science course, week 12: Predictive modeling, data leakage, model evaluation. This week’s guest lecturer in Rachel Schutt’s Columbia Data Science class was Claudia Perlich. Claudia has been the Chief Scientist at m6d for 3 years. Before that she was a data analytics group at the IBM center that developed Watson, the computer that won Jeopardy!, although she didn’t work on that project. Claudia got her Ph.D. in information systems at NYU and now teaches a class to business students in data science, although mostly she addresses how to assess data science work and how to manage data scientists. Claudia also holds a masters in Computer Science. By Cathy O'Neil, mathbabe.org. November 20, 2012.
    • Big Data in my Blood – Check out this story in this week’s NYT Big Data in Your Blood. I want to use it to explore a couple things and ideas I was struggling with before the class started this semester, and that I wasn’t sure how to communicate with you about on our first day together. By Rachel Schutt, September 9, 2012, in Data Journalism, Data Science Domains, Defining Data Science – Blog to document and reflect on Columbia Data Science Class. Introduction to Data Science, Columbia University.
  • Big Data in Your Blood. Very soon, we will see inside ourselves like never before, with wearable, even internal , sensors that monitor even our most intimate biological processes. It is likely to happen even before we figure out the etiquette and laws around sharing this knowledge. By Quentin Hardy, THE NYTIMES – BITS – September 7, 2012
  • Data Science – The Problem Isn’t Statisticians, It’s Too Many Poseurs. I recently was hugely flattered by my friend Cosma Shalizi’s articulate argument against my position that data science distinguishes itself from statistics in various ways. By Cathy O’Neil, a data scientist who lives in New York City and writes at mathbabe.org
  • Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field. Introduction – As the cost of computing power, data storage, and high-bandwidth Internet access and have plunged exponentially over the past two decades, companies around the globe recognized the power of harnessing data as a source of competitive advantage. But it was only recently, as social web applications and massive, parallel processing have become more widely available that the nescient field of data science revealed what many are becoming to understand: that data is the new oil, the source for corporate energy and differentiation in the 21st century. Companies like Facebook, LinkedIn, Yahoo, and Google are generating data not only as their primary product, but are analyzing it to continuously improve their products. Pharmaceutical and biomedical companies are using big data to find new cures and analyze genetic information, while marketers leverage the same technology to generate new customer insights. In order to tap this newfound wealth, organizations of all sizes are turning to practitioners in the new field of data science who are capable of translating massive data into predictive insights that lead to results. By Usama Fayyad, Former Chief Data Officer at Yahoo, and currently CEO or Chairman at 3 mid-stage start-up companies. e EMC Data Science Community Survey. 2011
  • Big Data Statistics: $16.9 Billion Market By 2015 – We have written about cloud computing stats – we spotted this today: “Market research firm IDC has released a new forecast that shows the big data market is expected to grow from $3.2 billion in 2010 to $16.9 billion in 2015. Stat Spotting – March 11, 2012.
  • Why do statisticians "hate" us?By David Hand, Heikki Mannila, Padhraic Smyth. "Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner." CS.CSI.CUNY
  • What's in a Name? – The rate at which many businesses are amassing huge quantities of so-called “big data” has created an urgent need for experts capable of managing, extracting, analysing and interpreting these enormous datasets. This, in turn, has led to the dramatic growth of new roles such as “data scientist” and “informatician” that were all but unheard of just a few years ago. However, what distinguishes the role of a data scientist from that of a statistician, informatician or analyst? Although there have been a number of interesting blogs and discussion posts on the subject, it is difficult to find any clear agreement. Data scientists, statisticians, informaticians and analysts alike occupy the world of information or decision science and have complementary skill sets that together unlock the value in data. They all work in an environment that is interdisciplinary in nature and they need to blend a combination of technical knowledge and programming skills with contextual understanding, teamwork and communication. By Select Statistical Services
  • Statistics/statisticians need better marketing. Statisticians have not always been great self-promoters. I think in part this comes from our tendency to be arbiters rather than being involved in the scientific process. In some ways, I think this is a good thing. Self-promotion can quickly become really annoying. On the other hand, I think our advertising shortcomings are hurting our field in a number of different ways. Simply Statistics – August 14, 2012
  • Big Data's big issue: Where are all the data scientists coming from? – This personnel gap isn't just a job-title change. Analysis Plug “data scientist” into Google and it is clear the job title has finally come of age and, suddenly there is a huge skills shortage. By Mark Whitehorn • Get more from this author, 2nd November 2012.
  • Data scientist: The new kid on the IT block! – Statisticians or mathematicians ideally make today's data scientists and McKinsey predicts India will need nearly 1,00,000 data scientists in the next few years. By Pankaj Maru, CIOL TOPIC CENTER – DATA STORAGE – September 03, 2012
  • Don’t Shun the ‘S’ Word – As children, we learned never to use the “S” word. But the “S” word I am talking about this month is not the one our parents and teachers did not want to hear. My “S” word is “Statistics,” or maybe “Statistician.” – By Nancy Geller, ASA President, AMSTAT NEWS – 1 August 2011
  • Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics. An action plan to enlarge the technical areas of statistics focuses on the data analyst. The plan sets out six technical areas of work for a university department and advocates a specific allocation of resources devoted to research in each area and to courses in each area. The value of technical work is judged by the extent to which it benefits the data analyst, either directly or indirectly. The plan is also applicable to government research labs and corporate research organizations. By William S. Cleveland, Statistics Research, Bell Labs. 2001 — Comentários de Nathan Yau sobre este artigo aqui.
  • NYC and Columbia to Create Institute for Data Sciences & Engineering – Mayor Michael R. Bloomberg and Columbia University President Lee C. Bollinger today announced an agreement between the City of New York and Columbia University that will lead to the creation of a new Institute for Data Sciences and Engineering, to be located at Columbia’s Morningside Heights and Washington Heights campuses in New York City, and the hiring of dozens of new faculty within the university’s Fu Foundation School of Engineering and Applied Sciences. The announcement is the next milestone in the City’s groundbreaking Applied Sciences NYC initiative, which seeks to dramatically increase New York City’s capacity for applied sciences and engineering while strengthening and transforming the City’s economy for generations. July 30, 2012 – New York City Government – Mike Bloomberg latest News.
  • The Data Science Interview: Mingsheng Hong, Hadapt – Data scientists are data junkies—when they see a new data set they are just naturally excited and can’t wait to explore. Mingsheng Hong is Chief Data Scientist at Hadapt, a Boston-based startup that offers an analytical platform that integrates structured and unstructured data in one cloud-optimized system. By Gill Press, Contributor – FORBES – TECH – August 28 2012
  • Data Scientists: The Definition of Sexy – I put “sexy” in the title because I’m told that the words in the title make all the difference in getting noticed on the Web. That has certainly proven true for the Harvard Business Review after it included the word “sexiest” in the title of a recent article. By Gill Press, Contributor – FORBES – TECH – September 27 2012
  • Could data scientist be your next job? – The definition of a data scientist varies, but no matter how it's defined, the skills are in demand. Network World – Want to extract real value from your data? Better hire a data scientist or two. By Sandra Gittlen, Network World June 04, 2012
  • Should tech pros get an MBA? – IT pros who earn MBA degrees are valued for their hybrid technical-business skills. Network World – Michael Morris, 36, had a decade of networking and communications experience (including four years as a paratrooper in the U.S. Army) under his belt when he decided to go back to school to earn a Master's of Business Administration degree, or MBA. An IT manager at a $5 billion tech company, Morris leads a team of engineers responsible for data networks, storage area networks, IP telephony and security. His certifications include Cisco Certified Internetwork Expert (CCIE) and Cisco Certified Design Expert (CCDE). By Ann Bednarz, Network World – October 13, 2011
  • 5 Hidden Skills for Big Data Scientists. By Matthew Hurst – SmartData Collective – May 28 2012
  • Why becoming a data scientist might be easier than you think – Several novice programmers who signed up for a free machine-learning class on Coursera have gone on recently to win predictive-modeling competitions. Maybe it’s not that hard to mint new data scientists after all. By Derrick Harris – GIGAOM – October 14 2012
  • Why The Search For The Mystical Data Scientist Should Not Be A Feat Of Magic – The data scientist is a mystical spirit. A wizard, whose skills are fired in the deep unknown of a developer’s lair. Their secrets are worth the gold of a million empires.They possess the keys to eternity.They have pet dragons. Not! It’s time to take away the staff and stop thinking of data scientists as lord wizards of middle earth lore. By Alex Williams – August 12 2012 – TECHCRUNCH Hot Topics – Enterprise.
  • Scientists See Promise in Deep-Learning Programs. Using an artificial intelligence technique inspired by theories about how the brain recognizes patterns, technology companies are reporting startling gains in fields as diverse as computer vision, speech recognition and the identification of promising new molecules for designing drugs. By JOHN MARKOFF, November 23, 2012 – NEW YORK TIMES – SCIENCE

    • Comments on this NYTimes article – ​Computer scientists discover statistics and find it useful. This article in the New York Times today describes some of the advances that computer scientists have made in recent years. By Roger D. Peng, Associate Professor, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health. BLOG Simply Statistics, November 24 2012.
  • Probabilistic Graphical Models – In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques. By Daphne Koller, Professor – COURSERA – STANFORD UNIVERSITY





Dicas de websites contendo materiais diversos sobre esta área


  • BUSINESS INTELLIGENCE (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. BI encompasses a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers. SAIBA+
  • BUSINESS INTELLIGENCE: TECNOLOGIAS DA INFORMAÇÃO NA GESTÃO DE CONHECIMENTO (PDF) – Autores: Santos, Maribel Yasmina e Ramos, Isabel. Palavras-chave: Business intelligence, Gestão de conhecimento, Data mining, Sistemas de processamento analítico, Data warehouses. Data: 2006. Editora: FCA – Editora de Informática. Resumo: Neste livro é abordado o conceito de Business Intelligence e as TI a ele associadas. Os sistemas de Business Intelligence utilizam os dados disponíveis nas organizações para disponibilizar informação relevante para a tomada de decisão. Combinam um conjunto de ferramentas de interrogação e exploração dos dados com ferramentas que permitem a geração de relatórios, para produzir informação que será posteriormente utilizada pela gestão de topo das organizações, no suporte à tomada de decisão. SAIBA+
  • UMA PROPOSTA DE APLICAÇÃO DE BUSINESS INTELLIGENCE NO CHÃO-DE-FÁBRICA – Marcos Roberto Fortulan, Tecumseh do Brasil Ltda / São Carlos/SP; Eduardo Vila Gonçalves Filho, Escola de Engenharia de São Carlos, EESC, Departamento de Engenharia Mecânica, Universidade de São Paulo, São Carlos / SP. Resumo: A evolução do chão-de-fábrica tem sido significativa nas últimas décadas, quando grandes investimentos têm sido realizados em infra-estrutura, automação, treinamento e sistemas de informação, transformando-o numa área estratégica para as empresas. O chão-de-fábrica gera hoje grande quantidade de dados que, por estarem dispersos ou desorganizados, não são utilizados em todo o seu potencial como fonte de informação. Com vistas nessa deficiência, este trabalho propõe a implantação de um sistema de Business Intelligence por meio do uso de ferramentas de Data Warehouse e OLAP (On-Line Analytical Processing), aplicadas especificamente ao chão-de-fábrica. O objetivo é desenvolver um sistema que utilize os dados resultantes do processo produtivo e os transforme em informações que auxiliem o gerente na tomada de decisões, de forma a garantir a competitividade da empresa. Um protótipo foi construído com dados simulados para testar a proposta. SAIBA+
  • SOLUÇÕES BUSINESS INTELLIGENCE OPEN SOURCE NO SUPORTE À ESTRATÉGIA ORGANIZACIONAL – Ana Virgínia A. G. Bertolini, Márcia Almeida Chiappin, Vianei Roberto Mayolo, Fernanda Pauletto D’Arrigo, Paulo Fernando Pinto Barcellos, Deise Taiana de Ávila Dias. Resumo: Uma das grandes condutoras das transformações no cenário competitivo econômico é a contínua evolução da tecnologia da informação e comunicação (TIC). Na criação e manutenção de vantagens competitivas das organizações, softwares surgem como elementos-chave favoráveis aos processos estratégicos organizacionais. O objetivo do artigo é comparar os recursos disponíveis em quatro softwares de Business Intelligence (BI) open source identificados na literatura e assim, informar aos gestores das organizações que identificam a necessidade de utilizar ferramentas da TIC, dos recursos disponibilizados por estes softwares nas suas versões open source. A abordagem da pesquisa é qualitativa exploratória. Os dados foram coletados através de sites oficiais dos desenvolvedores desistemas de BI open source, fóruns de usuários, sites especialistas em softwares desta natureza e principalmente artigos científicos.  Os resultados sugerem que sistemas de BI open source podem ser uma opção viável para organizações inviabilizadas financeiramente de utilizar versões pagas destes softwares. Ainda, indicam que houve um crescimento do número de opções de soluções de BI, porém com limitações maiores nas suas versões open source. SAIBA+
  • APLICANDO TÉCNICAS DE BUSINESS INTELLIGENCE SOBRE DADOS DE DESEMPENHO ACADÊMICO: UM ESTUDO DE CASO –  Ana Magela Rodriguez Almeida, Sandro da Silva Camargo. Curso Engenharia de Computação– Universidade Federal do Pampa (UNIPAMPA)- Bagé – RS – Brasil. Resumo: Atualmente, os volumes de dados gerados e armazenados pelas organizações estão crescendo de forma desmesurada. Esta situação, vem acompanhada de grandes desafios, entre eles, a integração e transformação destes dados em informações relevantes para aprimoramento do processo decisório Desta forma, faz-se necessária a execução de um complexo processo para organizar e integrar os dados disponíveis de forma facilmente entendida e que permita o acesso rápido, possibilitando a exploração e análise de dados a fim de atender as expectativas estratégicas das organizações. Neste cenário, este trabalho apresenta uma abordagem para implementação de Business Intelligence dentro de uma instituição federal de ensino superior, a fim de otimizar a aplicação de seus recursos. SAIBA+
  • EXPERIMENTAÇÃO NA INDÚSTRIA PARA AUMENTO DA EFETIVIDADE DA CONSTRUÇÃO DE PROCEDIMENTOS ETL EM UM AMBIENTE DE BUSINESS INTELLIGENCE – Juli Kelle Góis Costa, Igor Peterson Oliveira Santos, André Vinícius R. P. Nascimento, Methanias Colaço Júnior. Resumo: Aplicações de Business Intelligence (BI) efetivas dependem de um Data Warehouse (DW), um repositório histórico de dados projetado para dar suporte a processos de tomada de decisão. Sem um DW eficiente, as organizações não podem extrair, em um tempo aceitável, os dados que viabilizam ações estratégicas, táticas e operacionais mais eficazes. Ambientes de BI possuem um processo de Engenharia de Software particular, baseado em dados, para desenvolver programas de Extração, Transformação e Carga (ETL) de informações para o DW. Este artigo apresenta um método e uma ferramenta de Desenvolvimento Rápido de Aplicações (RAD) para aumentar a eficiência do desenvolvimento de programas ETL. A avaliação experimental da abordagem foi realizada em um experimento controlado feito na indústria para analisar a efetividade da ferramenta neste tipo de ambiente. Os resultados indicaram que a nossa abordagem pode ser usada como método para acelerar e melhorar o desenvolvimento de processos ETL. SAIBA+
  • DE BUSINESS INTELLIGENCE A DATA SCIENCE: UM ESTUDO COMPARATIVO ENTRE ÁREAS DE CONHECIMENTO RELACIONADAS. Alexandre de Oliveira Paixão, Verônica Aguiar da Silva, Asterio Tanaka. Programa de Pós-Graduação em Informática – Centro de Ciências Exatas e Tecnologia – Universidade Federal do Estado do Rio de Janeiro (UNIRIO). Resumo: Ao longo dos anos, as empresas e organizações em geral têm se deparado com a contínua necessidade de tomar decisões em espaços de tempo cada vez menores. Ao mesmo tempo, cresce também a quantidade de dados disponíveis, fato que traz grandes desafios na coleta, armazenamento e processamento de tais dados, assim como em obter dos mesmos a informação e o conhecimento necessários para gerar a Inteligência de Negócio (do Inglês, Business Intelligence), permitindo, assim, o devido suporte na tomada de decisão. Recentemente, uma nova denominação tem sido usada para o tratamento de dados em diversas áreas do conhecimento, conhecida como Ciência de Dados (do Inglês, Data Science). O presente artigo tem como objetivo fazer um estudo comparativo dos diversos conceitos e técnicas usados em Inteligência de Negócio e Ciência de Dados, bem como de suas aplicações, traçando um paralelo entre esses dois termos contemporâneos.  SAIBA+
  • BUSINESS INTELLIGENCE E ANÁLISE DE SENTIMENTOS NO CONTEXTO DE REDES SOCIAIS ONLINE. Leonardo José de Andrade Costa Santos. Trabalho de Graduação. Resumo: A utilização de redes sociais tem crescido de forma assustadora nos últimos anos, o que contribuiu para um aumento exponencial na produção de informações na Web. Este crescimento tem atraído o interesse de diversas organizações por consistir numa oportunidade de investigar o que seus consumidores falam sobre suas marcas e obter informações sobre estes clientes, visto que uma análise eficaz deste tipo de informação pode ajudar a guiar decisões corporativas. Este trabalho tem como objetivo a realização de um estudo de técnicas e ferramentas de extração de largas quantidades de dados não estruturados oriundos de redes sociais online. Será discutido como este tipo de informação pode ser recuperado e como ela pode ser útil em diferentes contextos. Aliado ao processo de extração de dados, o trabalho seguirá explorando algoritmos de análise e tratamento de dados. Em particular, técnicas de análise de sentimentos, que possuem o objetivo de avaliar sentimentos expressos em fragmentos de texto. Por fim, será mostrado um estudo de caso desenvolvido que ilustra como soluções de Business Intelligence e análise de sentimentos podem ser combinadas dentro do contexto de redes sociais para diversos tipos de análise que podem ser extremamente úteis para corporações. SAIBA+
  • FERRAMENTAS GRATUITAS PARA DESENVOLVIMENTO DE SOLUÇÕES DE BUSINESS INTELLIGENCE. Tiago Alexandre Marques da Silva. Resumo: O mundo dos negócios é orientado pela tomada de decisão. A diferença entre a decisão certa e a errada  é a informação. No mundo corporativo, ganha vantagem quem tem acesso mais rápido às informações  que oferecem suporte à gestão empresarial. A Business Intelligence (BI) é o processo que recolhe,  organiza, analisa, compartilha e monitoriza as informações necessárias para o crescimento de uma  organização.  Os dados são armazenadas e transformados em informação qualitativa que ajuda a definir as melhores  soluções. Isto acontece, pois ao conhecer melhor o negócio e ao ter uma visão sólida, bem  fundamentada e completa dos dados corporativos, é possível analisar todos os pontos e arquitectar um  planeamento estratégico. No contexto prático existem várias ferramentas que auxiliam os  programadores, analistas e responsáveis de negócio a desenvolver aplicações de BI sobre dados  existentes em sistemas relacionais (ou outros). No entanto, a maioria está associada a licenciamento. O  objectivo do projecto é desenvolver uma plataforma online de BI utilizando apenas ferramentas  gratuitas, sendo que, para além de um possível produto final, será estudada a viabilidade da(s)  ferramenta(s) a utilizar, em projectos futuros.  Neste trabalho são apresentadas várias ferramentas gratuitas que permitem desenvolver aplicações de  BI. Existem, actualmente, diversas opções no mercado, desde ferramentas que fazem reporting,  passando pelo dashboarding, data mining, entre outras e acabando nas consultas do tipo Online  Analytical Processing (OLAP). Todas as ferramentas apresentadas foram estudadas e experimentadas.  Estas foram alvo de comparação e análise crítica, no fim uma delas será escolhida para um problema  de aplicabilidade prática. A ferramenta escolhida foi então utilizada para criar uma prova de conceitos  prática, tendo como objetivo mostrar a qualidade da solução produzida por este tipo de ferramenta.  Esta destina-se a ser utilizada pela organização Pessoas e Processos, para solucionar um problema real  que consiste na possibilidade de obter dashboards e outros elementos BI com ferramentas gratuitas,  podendo substituir, em determinadas situações o uso de uma ferramenta paga, com uma qualidade  semelhante e assegurando compatibilidade com a infraestrutura informacional pré-existente.  Depois de estudadas várias ferramentas, foi desenvolvida uma aplicação de BI com a prova de  conceitos prática. Com este trabalho prova que existem muitas alternativas viáveis para fazer BI de  forma gratuita. Dependendo dos objectivos a atingir, as organizações, têm ao dispôr várias ferramentas  gratuitas, que podem utilizar para desenvolver soluções de BI. A qualidade das soluções produzidas  por estas ferramentas é assinalável, e deve haver um esforço por parte das organizações, em optar por  este tipo de software. Estas soluções conseguem reduzir os custos das empresas em software  comercial, que oferecem praticamente os mesmos resultados que as ferramentas gratuitas. SAIBA+
  • FERRAMENTAS GRATUITAS PARA DESENVOLVIMENTO DE SOLUÇÕES DE BUSINESS INTELLIGENCE. Deanne Larson, Victor Chang. ABSTRACT: Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.  SAIBA+
  • UNLOCKING THE FULL POTENTIAL OF BIG DATA – A CHANGE MANAGEMENT APPROACH. Alice Ricci, Foteini Kalyva. Abstract: Big Data has experienced recent developments in the field of Business Intelligence and has captured the increasing interest of enterprises who are trying to seize its potential. More and more companies, operating in industries ranging from insurance to the entertainment industry, are moving forward with the adoption of Big Data, in an attempt to gain significant benefits, such as customer insight, increased value, faster decision-making, and the ability to maintain competitive advantage. Although a few companies, such as Amazon, Google, IBM and Netflix, have managed to reap the benefits from Big Data, most firms are in the very early stages of addressing the challenges presented by this new phenomenon and they still struggle to adapt to the changes that are required by Big Data. This research aims to apply the theories of change management on the concept of Big Data, in order to develop a strategic model, which can later be used by companies to implement changes that may help in unlocking the full potential of Big Data. For that purpose, a mixed methods approach was chosen and the data was collected through the use of a questionnaire and interviews conducted worldwide with managers, consultants and experts in the field. The final findings indicate that changes in both corporate resources and culture are necessary. In particular, corporate culture should favor close collaboration and knowledge exchange between data specialists and decision-makers. That is, leaders should encourage this change, and soft skills such us communication, teamwork and problem-solving should be pursued. At the same time, decision-makers should change their mindset, shifting from a decision-making process based on their “gut feeling” to a data-driven approach. The resulting resistance to change can originate from decision-makers mainly due to lack of information about the reasons of the change and from other employees, due to fear of not having the skills required. Moreover, companies where Big Data brings episodic changes are more likely to encounter resistance than others. In order to maximize the benefits of the change, companies should work to prevent and overcome specific instances of resistance by means of education and communication, initiatives for decision-makers and facilitation and support for all employees. SAIBA+
  • BUSINESS INTELLIGENCE IN BANKING: A LITERATURE ANALYSIS FROM 2002 TO 2013 USING TEXT MINING AND LATENT DIRICHLET ALLOCATION. Sérgio Moro, Paulo Cortez, Paulo Rita. Abstract: This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research. SAIBA+








Reunimos aqui uma série de links, websites e dicas de listas contendo dados abertos e de livre acesso para que os estatísticos possam aprender a manusear grandes massas de informação e melhorar suas habilidades para compreender, analisar e concluir analiticamente. Alguns websites têm informações repetidas, mas achamos melhor deixar como estão para que o estatístico possa escolher a partir de seus links preferidos. SAIBA+