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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!”

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.

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.

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.