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Intelligencia.co

Analytics

Today, the analytics space is more crowded than ever. Standard ETL-solution providers are adding analytics (at least descriptive analytics) to their multitude of offerings. Many of these new players in the Master Data Management (MDM) field have BI platforms that combine integration, preparation, analytics and visualization with governance and security features. Such standard analytics processes as column dependencies, clustering, decision trees, and a recommendation engine are all included in many of these software offerings. Instead of forcing clients to purchase modules on top of modules, new software companies are creating packages that contain many built-in analytical functions. Open source products like R, Python, and the WEKA collection can easily be added to many of these software solutions, thereby reducing the need for expensive analytics layers. 

Unsupervised learning, or Pattern Discovery can be described as trying to find hidden structures in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning.

Predictive analytics (or Supervised Learning) is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. Said another way, it gives mere mortals the ability to predict the future like Nostradamus. In recent years, data-mining has become one of the most valuable tools for extracting and manipulating data and for establishing patterns in order to produce useful information for decision-making. Whether you love it or hate it, predictive analytics has already helped elect presidents, discover new energy sources, score consumer credit, assess health risks, detect fraud and target prospective buyers. It is here to stay, and technology advances ranging from faster hardware to software that analyzes increasingly vast quantities of data are making the use of predictive analytics more creative and efficient than ever before. Forecasting can be looked upon as an extension of supervised learning where there is a time dependency involved in the outcome of interest

It could be argued that the most valuable use of predictive analytics is in the marketing and sales department. Imagine the ability to accurately predict not only who your best leads and prospects will be, but when and how will be the most effective ways to reach them and then to engage. This ability alone will empower marketers and salespeople in the coming seasons to be radically more productive and profitable than they are today. Used properly, it can transform the science of sales forecasting from a dart-throwing exercise to a precision instrument.

The concept of sales and marketing automation has already produced some of the highest-flying success in high-tech. Companies like Salesforce.com have been wildly successful in automating the sales process for salespeople and managers. Organizations like Marketo have enriched the marketing discipline with automation and tools for lead generation, lead nurturing and lead scoring.

For a sports book, analytics could be used to spot problem gamblers, risk management, increase share of wallet of other gamblers, incentivize people to spread out their gambling spend as well as a whole host of other things.

ANALYTICAL MODELS INCLUDE THE FOLLOWING:

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