{"id":15971,"date":"2016-12-01T16:54:20","date_gmt":"2016-12-02T00:54:20","guid":{"rendered":"https:\/\/devwww.3cloudsolutions.com\/post\/using-real-time-analytics-for-quick-decisions-2\/"},"modified":"2024-04-17T14:03:14","modified_gmt":"2024-04-17T21:03:14","slug":"using-real-time-analytics-for-quick-decisions","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/using-real-time-analytics-for-quick-decisions\/","title":{"rendered":"Using Real-Time Analytics for Quick Decisions"},"content":{"rendered":"

Every company is increasing their use of data.\u00a0 In addition to increasing the amount of data they use, companies are looking to reduce the latency from the source system to analysis.\u00a0 This is where real-time analytics comes into play. \u00a0Real-time analytics<\/strong>, as the name suggests, is time sensitive and enables a decision with as little delay as possible<\/strong>.\u00a0 The incoming data requires immediate analysis and is composed of analyzing data in motion and at rest.\u00a0 This low latency concept is not new, but it is gaining popularity as the scalability of hardware becomes more economical to handle high volumes of data.<\/p>\n

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\"iStock-496701616edited.png\"<\/p>\n

So, what can you do with real-time analytics?\u00a0Instead of trying to list off every example possible (an endless list), let’s jump into one example BlueGranite has recently come across.\u00a0 This example demonstrates how to transform the manual process of reviewing requests into an automated analysis, delivering decisions in real-time<\/strong>.<\/p>\n

Problem<\/strong><\/span><\/p>\n

The organization has a goal of reducing processing time of the large volume of incoming requests.\u00a0 One of the firm\u2019s bottlenecks is a manual process of directing requests to their respective specialized departments to handle.\u00a0 Implementing a simple yet effective real-time analytical process provides a strategic solution.<\/p>\n

Solution<\/strong><\/span><\/p>\n

Under the old process, a request would arrive, wait until someone was available, and then that individual would take the time to read through the request statements.\u00a0 After processing the information, the individual would decide on what department should handle the request.<\/p>\n

\"solutionRT.png\"<\/p>\n

Under the new process, a request will arrive and immediately a text analysis function consumes the request statements, then classifies which department handles the request.<\/p>\n

\"solutionRT2.png.jpg\"<\/p>\n

Benefits<\/strong><\/span><\/p>\n

Two of the major benefits from implementing such a strategy addresses the organization’s goal of reducing processing time.\u00a0 One benefit is reducing the time spent deciding<\/strong> where to send the request.\u00a0 In more tangible terms, the process to review an incoming request was expected to take around 15 minutes or less.\u00a0 The text analysis function only takes a second to run, providing reliable time savings. In addition to the reduced review time, more resources can be spent handling the request\u00a0rather than deciding which department needs to review the request.<\/p>\n

Another major benefit is reducing the overall latency time<\/strong> from the request to the department.\u00a0 Any surge of requests could flood the system under the old process requiring manual direction.\u00a0 In return, this would cause delays before the departments could even start processing the requests.\u00a0 By using text analytics in real-time, the hardware can be scaled appropriately to handle any flood of requests and reduce the latency time by automatically executing at the arrival of a request.<\/p>\n

Getting Started<\/strong><\/span><\/p>\n

The components in this system design can be kept at a minimum to maintain a simple and effective solution.\u00a0 One way is to take advantage of SQL Server R Services<\/a> by using SQL 2016<\/a>.\u00a0 This allows for a set of R commands to be encapsulated in a stored procedure to be executed immediately, as needs arise.\u00a0 In this example, R commands use a trained SVM model which consumes the request statements to determine the most suitable department alongside the probability or confidence in the classification.<\/p>\n

To learn more about how BlueGranite can help move your analytics into real-time, contact us<\/a> today!<\/p>\n","protected":false},"excerpt":{"rendered":"

Companies are increasing their use of data. Because of this, they are looking to reduce latency from source system to analysis.<\/p>\n","protected":false},"author":21,"featured_media":14749,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[395],"tags":[319],"class_list":["post-15971","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science-ai","tag-machine-learning-ai","topics-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/15971","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/comments?post=15971"}],"version-history":[{"count":0,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/15971\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media\/14749"}],"wp:attachment":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media?parent=15971"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/categories?post=15971"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/tags?post=15971"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}