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Flexibility and a variety of options also characterize analytics in the cloud. There are many new
possibilities with analytical analysis, but the most exciting possibility is predictive analytics; that is, the ability to predict future performance from historical data. The possibilities are almost limitless, but consider the use of Big Data for predictive analysis in the following:

 Churn analysis
 Social-network analysis
 Recommendation engines
 Location-based tracking and services
 IT infrastructure and web application optimization
 Weather forecasting for business planning
 Legal discovery and document archiving
 Equipment monitoring
 Advertising analysis
 Pricing analysis
 Fraud detection
 Personalized everything

The tools available in the cloud to help you with analytics include HDInsights, Machine Learning, Stream Analytics, Search, and business intelligence (BI) and reporting. With these tools you can explore how to turn today’s noise into tomorrow’s insights.

Azure HDInsight is a Hadoop-based service that brings an Apache Hadoop solution to the cloud. You can use it for managing data of any type and any size. HDInsight can process unstructured or semi-structured data from web clickstreams, social media, server logs, devices and sensors, and more. This makes it possible for you to analyze new sets of data that uncovers new business possibilities to drive your organization forward.

Azure Machine Learning offers a streamlined experience for all data-science skill levels, from setting up with only a web browser to using drag-and-drop gestures and simple data flow graphs to set up
experiments. Machine Learning Studio features a library of time-saving sample experiments, R and Python packages, and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Azure
Machine Learning also supports R and Python custom code, which you can drop directly into your
workspace and share across your organization.

In this case the experiment is to ingest data about cars, including engine size, mileage, size of the car,
and so on, and then to train a model to predict a car’s price based on those parameters. Data scientists can pick from any one of many prebuilt algorithms to train the model, or they can supply a custom R module. At the end of the training, a Web Service can then be created (see the bottom panel of the screen) that can subsequently be used in an application in which an end user supplies data and Azure Machine Learning provides a predicted price.

Azure Stream Analytics gives you the ability to rapidly develop and deploy a low-cost, real-time analytics solution to uncover real-time insights from devices, sensors, infrastructure, and applications. It opens the door to various opportunities, including IoT scenarios such as real-time remote management and monitoring or gaining insights from devices like mobile phones or connected cars. Stream Analytics provides out-of-the-box integration with Azure Event Hubs to ingest millions of events per second. Stream Analytics will process ingested events in real time, comparing multiple realtime streams or comparing real-time streams together with historical values and models. You can use this to detect anomalies, transformation of incoming data, to trigger an alert when a specific error or condition appears in the stream, and to power real-time dashboards.

Azure Search is a fully managed cloud service with which developers can build rich search applications by using.NET SDK or REST APIs. It includes full-text search scoped over your content, plus advanced search behaviors similar to those found in commercial web search engines, such as typeahead query suggestions based on a partial term input, hit-highlighting, and faceted navigation.
Natural language support is built in, using the linguistic rules that are appropriate to the specified
language.

Search is an API-based service for developers and system integrators who know how to work with
web services and HTTP. Search takes the complexity out of managing a cloud search service and
simplifies the creation of search-based web and mobile applications.

Azure BI and Reporting; The Microsoft Azure Virtual Machine gallery includes images that contain SQL Server installations that you can use to easily set up SQL Server Reporting Services on the cloud. You can create an Azure Virtual Machine that runs Microsoft SQL Server Business Intelligence (BI) features and Microsoft SharePoint 2013. Microsoft PowerBI (at http://www.powerbi.com) is a SaaS application with which you can quickly build visually appealing, interactive dashboards. An ever-increasing number of connectors gives you the ability to bring data from cloud data sources and other Microsoft and third-party SaaS applications into PowerBI. Whereas data is dramatically increasing in volume, velocity, and diversity, actionable analytics is
challenging. You need interoperable tools and systems to maximize your existing investments in analytics, and provide the flexibility to evolve on your own terms.

Source of Information : Microsoft Enterprise Cloud Strategy

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