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cezarovidiu

Visual Business Intelligence - Naked Statistics - 0 views

  • You can’t learn data visualization by memorizing a set of rules. You must understand why things work the way they do.
  • you must be able to think statistically
  • This doesn’t mean that you must learn advanced mathematics, nor can you do this work merely by learning how to use software to calculate correlation coefficients and p-values.
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  • I am happy to announce that I’ve just found the book that does this better than any other that I’ve seen: Naked Statistics: Stripping the Dread from the Data, by Charles Wheelan (W. W. Norton & Company, 2013).
  • Wheelan teaches public policy and economics at Dartmouth College and is best known for a similar book written several years ago titled Naked Economics.
  • In Naked Statistics, he selects the most important and relevant statistical concepts that everyone should understand, especially those who work with data, and explains them in clear, entertaining, and practical terms.
  • He wrote this book specifically to help people think statistically. He shows how statistics can be used to improve our understanding of the world. He demonstrates that statistical concepts are easy to understand when they’re explained well.
  • If you read this book, you’ll come to understand statistical concepts and methods such as regression analysis and probability as never before.
  • Statistics is more important than ever before because we have more meaningful opportunities to make use of data. Yet the formulas will not tell us which uses of data are appropriate and which are not. Math cannot supplant judgment.
  • “Go forth and use data wisely and well!”
cezarovidiu

What is business intelligence (BI)? - Definition from WhatIs.com - 0 views

  • Business intelligence is a data analysis process aimed at boosting business performance by helping corporate executives and other end users make more informed decisions.
  • 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.
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  • The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; driving new revenues; and gaining competitive advantages over business rivals. BI systems can also help companies identify market trends and spot business problems that need to be addressed.
  • BI data can include historical information, as well as new data gathered from source systems as it is generated, enabling BI analysis to support both strategic and tactical decision-making processes.
  • BI programs can also incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis and big data analytics.
  • In many cases though, advanced analytics projects are conducted and managed by separate teams of data scientists, statisticians, predictive modelers and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.
  • Business intelligence data typically is stored in a data warehouse or smaller data marts that hold subsets of a company's information. In addition, Hadoop systems are increasingly being used within BI architectures as repositories or landing pads for BI and analytics data, especially for unstructured data, log files, sensor data and other types of big data. Before it's used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.
  • In addition to BI managers, business intelligence teams generally include a mix of BI architects, BI developers, business analysts and data management professionals; business users often are also included to represent the business side and make sure its needs are met in the BI development process.
  • To help with that, a growing number of organizations are replacing traditional waterfall development with Agile BI and data warehousing approaches that use Agile software development techniques to break up BI projects into small chunks and deliver new functionality to end users on an incremental and iterative basis.
  • consultant Howard Dresner is credited with first proposing it in 1989 as an umbrella category for applying data analysis techniques to support business decision-making processes.
  • Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.
cezarovidiu

Raportare sau BI C - 0 views

Raportare sau BI Ce sau de ce? http://www.marketwatch.ro/pdfs/MW%20131.pdf Controlul asupra companiei, obiectiv pe care îl doreºte orice manager, poate fi  exercitat numai p...

started by cezarovidiu on 17 Jan 13 no follow-up yet
cezarovidiu

Cora Romania implementeaza solutia IBM SPSS - 0 views

  • Cora România a implementat cu succes soluţia completă de analiză predictivă - IBM SPSS Statistics, prin intermediul IBM Innovation Centre şi cu suportul IBM Business Partner.
  • Cora România urmărește creşterea nivelului de satisfacere a clienţilor, fidelizarea acestora şi crearea de oferte personalizate.
  • Soluţia implementată de retailer satisface nevoia de îmbunătăţire a ofertelor de produse pentru clienţii săi.
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  • Soluţia de analiză predictivă va ajuta Cora România să înţeleagă mai bine nevoile clienţilor și schimbările survenite în comportamentul de cumpărare al acestora, astfel încât va putea să dezvolte noi produse şi servicii care să corespundă dorințelor acestora. Totodată, retailer-ul va putea preveni plecarea cumpărătorilor majori, va putea vinde servicii adiţionale clienţilor actuali, va dezvolta produse mai eficiente şi va putea identifica şi minimiza frauda şi riscurile.
  • Soluţia IBM implementată ne va permite să evaluăm corect gradul de informare asupra brandului Cora. În viitor, vom fi capabili să identificăm direcţiile noi de business ce trebuie urmate şi să economisim timp preţios prin automatizarea proceselor de colectare a datelor, concomitent cu creşterea satisfacţiei clienţilor”, a declarat Simona Sahin, responsabil cercetare de marketing Cora România.
  • •Optimizarea tuturor tipurilor de decizii utilizând informațiile cheie obţinute cu ajutorul soluţiilor analitice;•Furnizarea de informaţii care ţin cont de perspectivele şi orizonturile de timp, de la raportarea istorică până la analiza în timp real şi la modelarea predictivă;•Îmbunătăţirea rezultatelor de business şi a managementului riscurilor;Creşterea nivelului de retenţie (menţinere) a clienţilor.
cezarovidiu

Rittman Mead Consulting - The Changing World of Business Intelligence - 0 views

  • Schema on write This is the traditional approach for Business Intelligence. A model, often dimensional, is built as part of the design process. This model is an abstraction of the complexity of the underlying systems, put in business terms. The purpose of the model is to allow the business users to interrogate the data in a way they understand.
  • The model is instantiated through physical database tables and the date is loaded through an ETL (extract, transform and load) process that takes data from one or more source systems and transforms it to fit the model, then loads it into the model.
  • The key thing is that the model is determined before the data is finally written and the users are very much guided or driven by the model in how they query the data and what results they can get from the system. The designer must anticipate the queries and requests in advance of the user asking the questions.
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  • Schema on read Schema on read works on a different principle and is more common in the Big Data world. The data is not transformed in any way when it is stored, the data store acts as a big bucket. The modelling of the data only occurs when the data is read. Map/Reduce is the clearest example, the mapping is the understanding of the data structure. Hadoop is a large distributed file system, which is very good at storing large volumes of data, this is potential. It is only the mapping of this data that provides value, this is done when the data is read, not written.
  • New World Order So whereas Business Intelligence used to always be driven by the model, the ETL process to populate the model and the reporting tool to query the model, there is now an approach where the data is collected its raw form, and advanced statistical or analytical tools are used to interrogate the data. An example of one such tool is R.
  • The driver for which approach to use is often driven by what the user wants to find out. If the question is clearly formed and the sources of data that are required to answer it well understood, for example how many units of a product have we sold, then the traditional schema on write approach is best.
cezarovidiu

Tableau Software's Pat Hanrahan on "What Is a Data Scientist?" - Forbes - 0 views

  • In the contemporary enterprise, almost everyone will need to have data-science skills of some kind.
  • “When most people think of a data scientist, they think of a statistician, a guy with ‘analyst’ in his title,’” Hanrahan says. “Or, someone who works in IT and manages the data warehouses. To do these jobs, you certainly needed programming skills; you probably needed advanced statistics skills, or some combination of those skills.”
  • “At the most basic level, you are a data scientist if you have the analytical skills and the tools to ‘get’ data, manipulate it and make decisions with it,” he says.
  •  
    "What is a Data Scientist?"
cezarovidiu

Magic Quadrant for Advanced Analytics Platforms - 1 views

  • Gartner defines advanced analytics as, "the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) — such as query and reporting — are unlikely to discover."
  • packaged analytics applications that target specific business domains
  • Revolution Analytics
cezarovidiu

Why BI projects fail -- and how to succeed instead | InfoWorld - 0 views

  • A successful initiative starts with a good strategy, and a good strategy starts with identifying the business need.
  • The balanced scorecard is one popular methodology for linking strategy, technology, and performance management. Other methodologies, such as applied information economics, combine statistical analysis, portfolio theory, and decision science in order to help firms calculate the economic value of better information. Whether you use a published methodology or develop your own approach in-house, the important point is to make sure your BI activities are keyed to generating real business value, not merely creating pretty, but useless, dashboards and reports.
  • Next, ask: What data do we wish we had and how would that lead to different decisions? The answers to these questions form top-level requirements for any BI project.
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  • Instead a team of data experts, data analysts, and business experts must come together with the right technical expertise. This usually means bringing in outside help, though that help needs to be able to talk to management and talk tech.
  • Nothing makes an IT department more nervous than asking for a feed to a key operational system. Moreover, a lot of BI tools are resource hungry. Your requirements should dictate what, how much, and how often (that is, how “real time” you need it to be) data must be fed into your data warehousing technology.
  • In other words, you need one big feed to serve all instead of hundreds of operational, system-killing little feeds that can’t be controlled easily.
  • You'll probably need more than one tool to suit all of your use cases.
  • You did your homework, identified the use cases, picked a good team, started a data integration project, and chose the right tools.
  • Now comes the hard part: changing your business and your decisions based on the data and the reports. Managers, like other human beings, resist change.
  • oreover, BI projects shouldn't have a fixed beginning and end -- this isn't a sprint to become “data driven.”
  • A process is needed
  • and find new opportunities in the data.
  • Here's the bottom line, in a handy do's-and-don'ts format: Don’t simply run a tool-choice project Do cherry-pick the right team Do integrate the data so that it can be queried performance-wise without bringing down the house Don’t merely pick a tool -- pick the right tools for all your requirements and use cases Do let the data change your decision making and the structure of your organization itself if necessary Do have a process to weed out useless analytics and find new ones
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