Cloud

CLOUD

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet to offer faster innovation, flexible resources, and economies of scale. Cloud services have revolutionized computing, not least through IaaS, PaaS, and especially SaaS, which have allowed businesses to develop virtualized IT infrastructure and deliver software through the cloud, independent of a user's operating system.

Today, businesses can mix and match cloud services from different providers through cloud brokers in order to ensure these services work to maximum efficiency and cost effectiveness, but also to reduce the chances of vendor lock-in while also improving redundancy. This may require additional cloud management software, but for larger businesses the economic effects can be significant. Because everything is run through software platforms and virtualized networks, it means that it's easy to access and analyse data for the purposes of analytics as well as for business intelligence purposes. It also makes it easier to simplify all aspects of monitoring through cloud orchestration and the easy processing of log files through cloud logging services. The result is IT infrastructure that allows for better maintenance and patching, while providing for insights that would have previously been much more difficult to access.

One of the biggest advantages of cloud computing services is scalability, which means resources can be accessed only when needed. Services will be charged only when needed, which can drastically reduce the need to buy extra hardware. This especially applies to when storing data, as online cloud storage can be treated as effectively limitless. Even though you might be using cloud databases for your structured data, you can also archive everything else into massive data lakes for additional processing using AI and machine learning for greater insights. Altogether, cloud services offer unparalleled potential for improving business performance and increasing profits, and here we'll look at the leading cloud computing service providers, along with some additional recommendations to consider.

Descriptive

Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis. Data aggregation and data mining methods organize the data and make it possible to identify patterns and relationships in it that would not otherwise be visible. Querying, reporting and data visualization may be applied to yield more insight. Descriptive analytics is sometimes said to provide information about what happened. Seeing an increase in Twitter followers after a particular tweet, for example.

Diagnostic

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It attempts to understand causation and behaviors by utilizing such techniques as drill-down, data discovery, data mining and correlations. Building a decision tree atop a web user’s clickstream behavior pattern could be considered a form of diagnostic analytics as these patterns might reveal why a person clicked his or her way through a website. Diagnostic analytics takes a deeper look at data to attempt to understand the causes of events and behaviors. 
 

Predictive

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavioral patterns. Predictive analytics is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavioral patterns. Often the unknown event of interest is in the future, but predictive analytics can look into past behavior as well.

Prescriptive

Prescriptive analytics tries to optimize a key metric, such as profit, by not only anticipating what will happen, but also when it will happen and why it happens. Prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually ingest a mixture of structured, unstructured, and semi-structured data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options every minute of every day.
 

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