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Qlik Sense, HPE SERVICE MANAGER (SM), HPE Universal Configuration Management Database (UCMDB)
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Hewlett Packard Enterprise
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Now there are no limits.\r\nQlik Sense Cloud\r\nSecure hosting\r\nInteract with Qlik Sense apps whenever the need arises. Invite others to do the same in a secure environment.\r\nIntuitive\r\nUse simple drag-and-drop interfaces to create flexible, interactive data visualizations. Explore data with smart visualizations that automatically adapts to the parameters you set — no need for developers, data scientists or designers.\r\nSmart Search\r\nUse natural search to navigate complex information to accelerate discovery\r\nSelf-service creation\r\nDrag-and-drop to create your own visualizations — without scripting, complex SQL queries, or joins\r\nCentralized management\r\nFind and share relevant analyses, including apps and stories, through a centralized hub\r\nData integration\r\nUnify data sources for a complete view of information, without gaps, and make it easier to discover hidden insights\r\nBeautiful\r\nEasily engage, understand, communicate, collaborate and share data to any device with eye-catching, ready-to-use visualizations\r\nSmart, interactive visualizations\r\nBeautiful out-of-the-box visualizations instantly respond to interactions and changes in context\r\nData storytelling\r\nAllows you to share multiple points of view — with the ability to dive back into source analysis at any point\r\nResponsive design\r\nAccess on any device, anytime, anywhere\r\nCustomize and extend\r\nOpen APIs allow developers to rapidly customize, extend and embed Qlik Sense in new applications to address unique business needs\r\nEasy to start, easy to grow\r\nTurn your team into creators. 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OLAP business intelligence queries often aid in trends analysis, financial reporting, sales forecasting, budgeting and other planning purposes.\r\nTo facilitate this kind of analysis, data is collected from multiple data sources and stored in data warehouses then cleansed and organized into <span style=\"font-weight: bold; \">data cubes.</span> Each OLAP cube contains data categorized by dimensions (such as customers, geographic sales region and time period) derived by dimensional tables in the data warehouses. Dimensions are then populated by members (such as customer names, countries and months) that are organized hierarchically.\r\nAnalysts can then perform five types of online analytical processing system operations against these multidimensional databases: \r\n<ol><li><span style=\"font-weight: bold; \">Roll-up.</span> Also known as consolidation, or drill-up, this operation summarizes the data along the dimension.</li><li><span style=\"font-weight: bold; \">Drill-down.</span> This allows analysts to navigate deeper among the dimensions of data, for example drilling down from "time period" to "years" and "months" to chart sales growth for a product.</li><li><span style=\"font-weight: bold; \">Slice. </span>This enables an analyst to take one level of information for display</li><li><span style=\"font-weight: bold; \">Dice. </span>This allows an analyst to select data from multiple dimensions to analyze</li><li><span style=\"font-weight: bold; \">Pivot.</span> Analysts can gain a new view of data by rotating the data axes of the cube.</li></ol>\r\nOLAP software then locates the intersection of dimensions, such as all products sold in the Eastern region above a certain price during a certain time period, and displays them. The result is the "measure"; each OLAP cube has at least one to perhaps hundreds of measures, which are derived from information stored in fact tables in the data warehouse.\r\n<p class=\"align-center\"><span style=\"font-weight: bold; \">Types of OLAP: </span></p>\r\n<ul><li><span style=\"font-weight: bold;\">Relational online analytical processing (ROLAP):</span> ROLAP is an extended RDBMS along with multidimensional data mapping to perform the standard relational operation.</li><li><span style=\"font-weight: bold;\">Multidimensional OLAP (MOLAP):</span> MOLAP Implementes operation in multidimensional data.</li><li><span style=\"font-weight: bold;\">Hybrid OnlineAnalytical Processing (HOLAP):</span> In HOLAP approach the aggregated totals are stored in a multidimensional database while the detailed data is stored in the relational database. This offers both data efficiency of the ROLAP model and the performance of the MOLAP model.</li><li><span style=\"font-weight: bold;\">Desktop OLAP (DOLAP):</span> In Desktop OLAP system, a user downloads a part of the data from the database locally, or on their desktop and analyze it. DOLAP is relatively cheaper to deploy as it offers very few functionalities compares to other OLAP tools.</li><li><span style=\"font-weight: bold;\">Web based OLAP (WOLAP):</span> Web OLAP which is OLAP system accessible via the web browser. WOLAP is a three-tiered architecture. It consists of three components: client, middleware, and a database server.</li><li><span style=\"font-weight: bold;\">Mobile OLAP:</span> Mobile OLAP process helps users to access and analyze OLAP data using their mobile devices</li><li><span style=\"font-weight: bold;\">Spatial OLAP:</span> SOLAP is created to facilitate management of both spatial and non-spatial data in a Geographic Information system (GIS) </li></ul>","materialsDescription":"<h1 class=\"align-center\">Implementing an OLAP Solution</h1>\r\nImplementation of OLAP depends not only on the type of software, but also on underlying data sources and the intended business objective(s). Each industry or business area is specific and requires some degree of customized modeling to create multidimensional “cubes” for data loading and reporting building, at minimum. An OLAP program might be intended for dynamic reporting for finance professionals, with source data originating in an ERP system. Or a solution might address a medical institution’s activities as concerns patient analysis. All of which is to say that customers need to have clear objectives in mind for an intended solution, and start to consider product selection on that basis. Another factor to consider in an OLAP implementation is the delivery to end users: does the initial user base want to adopt a new front end, or is there a preference for utilizing a dashboard? Or perhaps users are better served by a dynamic spreadsheet “delivery” system to achieve, for example, a collaborative budgeting and forecasting solution.\r\n<h1 class=\"align-center\">Advantages and Disadvantages of OLAP</h1>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Advantages</span></p>\r\n<ul><li>OLAP is a platform for all type of business includes planning, budgeting, reporting, and analysis.</li><li>Information and calculations are consistent in an OLAP cube. This is a crucial benefit.</li><li>Quickly create and analyze "What if" scenarios</li><li>Easily search OLAP database for broad or specific terms.</li><li>OLAP provides the building blocks for business modeling tools, Data mining tools, performance reporting tools.</li><li>Allows users to do slice and dice cube data all by various dimensions, measures, and filters.</li><li>It is good for analyzing time series.</li><li>Finding some clusters and outliers is easy with OLAP.</li><li> It is a powerful visualization online analytical process system which provides faster response times</li></ul>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Disadvantages</span></p>\r\n<ul><li>OLAP requires organizing data into a star or snowflake schema. These schemas are complicated to implement and administer.</li><li>You cannot have large number of dimensions in a single OLAP cube.</li><li>Transactional data cannot be accessed with OLAP system.</li><li>Any modification in an OLAP cube needs a full update of the cube. 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When companies analyze Big Data, they are using Business Analytics to get the insights required for making better business decisions and strategic moves.\r\nCompanies use Business Analytics (BA) to make data-driven decisions. The insight gained by BA enables these companies to automate and optimize their business processes. In fact, data-driven companies that utilize Business Analytics achieve a competitive advantage because they are able to use the insights to:\r\n<ul><li>Conduct data mining (explore data to find new patterns and relationships)</li><li>Complete statistical analysis and quantitative analysis to explain why certain results occur</li><li>Test previous decisions using A/B testing and multivariate testing</li><li>Make use of predictive modeling and predictive analytics to forecast future results</li></ul>\r\nBusiness Analytics also provides support for companies in the process of making proactive tactical decisions, and BA makes it possible for those companies to automate decision making in order to support real-time responses.","materialsDescription":"<span style=\"font-weight: bold; \">What does Business Analytics (BA) mean?</span>\r\nBusiness analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. 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Improve IT service support and delivery processes across the complete lifecycle leveraging ITIL® industry best practices.\r\nBig Data Intelligence\r\nAutomated and Social Self-service\r\nBest Practices\r\nAgile and Automated Change Management\r\nMobile Phone and Tablet Support\r\nReduce ITSM Costs with a Smarter Service Desk\r\nHelp your service desk agents and IT managers proactively manage service quality. 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OLAP business intelligence queries often aid in trends analysis, financial reporting, sales forecasting, budgeting and other planning purposes.\r\nTo facilitate this kind of analysis, data is collected from multiple data sources and stored in data warehouses then cleansed and organized into <span style=\"font-weight: bold; \">data cubes.</span> Each OLAP cube contains data categorized by dimensions (such as customers, geographic sales region and time period) derived by dimensional tables in the data warehouses. Dimensions are then populated by members (such as customer names, countries and months) that are organized hierarchically.\r\nAnalysts can then perform five types of online analytical processing system operations against these multidimensional databases: \r\n<ol><li><span style=\"font-weight: bold; \">Roll-up.</span> Also known as consolidation, or drill-up, this operation summarizes the data along the dimension.</li><li><span style=\"font-weight: bold; \">Drill-down.</span> This allows analysts to navigate deeper among the dimensions of data, for example drilling down from "time period" to "years" and "months" to chart sales growth for a product.</li><li><span style=\"font-weight: bold; \">Slice. </span>This enables an analyst to take one level of information for display</li><li><span style=\"font-weight: bold; \">Dice. </span>This allows an analyst to select data from multiple dimensions to analyze</li><li><span style=\"font-weight: bold; \">Pivot.</span> Analysts can gain a new view of data by rotating the data axes of the cube.</li></ol>\r\nOLAP software then locates the intersection of dimensions, such as all products sold in the Eastern region above a certain price during a certain time period, and displays them. The result is the "measure"; each OLAP cube has at least one to perhaps hundreds of measures, which are derived from information stored in fact tables in the data warehouse.\r\n<p class=\"align-center\"><span style=\"font-weight: bold; \">Types of OLAP: </span></p>\r\n<ul><li><span style=\"font-weight: bold;\">Relational online analytical processing (ROLAP):</span> ROLAP is an extended RDBMS along with multidimensional data mapping to perform the standard relational operation.</li><li><span style=\"font-weight: bold;\">Multidimensional OLAP (MOLAP):</span> MOLAP Implementes operation in multidimensional data.</li><li><span style=\"font-weight: bold;\">Hybrid OnlineAnalytical Processing (HOLAP):</span> In HOLAP approach the aggregated totals are stored in a multidimensional database while the detailed data is stored in the relational database. This offers both data efficiency of the ROLAP model and the performance of the MOLAP model.</li><li><span style=\"font-weight: bold;\">Desktop OLAP (DOLAP):</span> In Desktop OLAP system, a user downloads a part of the data from the database locally, or on their desktop and analyze it. DOLAP is relatively cheaper to deploy as it offers very few functionalities compares to other OLAP tools.</li><li><span style=\"font-weight: bold;\">Web based OLAP (WOLAP):</span> Web OLAP which is OLAP system accessible via the web browser. WOLAP is a three-tiered architecture. It consists of three components: client, middleware, and a database server.</li><li><span style=\"font-weight: bold;\">Mobile OLAP:</span> Mobile OLAP process helps users to access and analyze OLAP data using their mobile devices</li><li><span style=\"font-weight: bold;\">Spatial OLAP:</span> SOLAP is created to facilitate management of both spatial and non-spatial data in a Geographic Information system (GIS) </li></ul>","materialsDescription":"<h1 class=\"align-center\">Implementing an OLAP Solution</h1>\r\nImplementation of OLAP depends not only on the type of software, but also on underlying data sources and the intended business objective(s). Each industry or business area is specific and requires some degree of customized modeling to create multidimensional “cubes” for data loading and reporting building, at minimum. An OLAP program might be intended for dynamic reporting for finance professionals, with source data originating in an ERP system. Or a solution might address a medical institution’s activities as concerns patient analysis. All of which is to say that customers need to have clear objectives in mind for an intended solution, and start to consider product selection on that basis. Another factor to consider in an OLAP implementation is the delivery to end users: does the initial user base want to adopt a new front end, or is there a preference for utilizing a dashboard? Or perhaps users are better served by a dynamic spreadsheet “delivery” system to achieve, for example, a collaborative budgeting and forecasting solution.\r\n<h1 class=\"align-center\">Advantages and Disadvantages of OLAP</h1>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Advantages</span></p>\r\n<ul><li>OLAP is a platform for all type of business includes planning, budgeting, reporting, and analysis.</li><li>Information and calculations are consistent in an OLAP cube. This is a crucial benefit.</li><li>Quickly create and analyze "What if" scenarios</li><li>Easily search OLAP database for broad or specific terms.</li><li>OLAP provides the building blocks for business modeling tools, Data mining tools, performance reporting tools.</li><li>Allows users to do slice and dice cube data all by various dimensions, measures, and filters.</li><li>It is good for analyzing time series.</li><li>Finding some clusters and outliers is easy with OLAP.</li><li> It is a powerful visualization online analytical process system which provides faster response times</li></ul>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Disadvantages</span></p>\r\n<ul><li>OLAP requires organizing data into a star or snowflake schema. These schemas are complicated to implement and administer.</li><li>You cannot have large number of dimensions in a single OLAP cube.</li><li>Transactional data cannot be accessed with OLAP system.</li><li>Any modification in an OLAP cube needs a full update of the cube. This is a time-consuming process.</li></ul>","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/OLAP_-__online_analytical_processing.png"},{"id":69,"title":"Business Analytics","alias":"business-analytics","description":"Business Analytics is “the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of these results to customers, business partners, and college executives.” Business Analytics requires quantitative methods and evidence-based data for business modeling and decision making; as such, Business Analytics requires the use of Big Data.\r\nSAS describes Big Data as “a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.” What’s important to keep in mind about Big Data is that the amount of data is not as important to an organization as the analytics that accompany it. When companies analyze Big Data, they are using Business Analytics to get the insights required for making better business decisions and strategic moves.\r\nCompanies use Business Analytics (BA) to make data-driven decisions. The insight gained by BA enables these companies to automate and optimize their business processes. In fact, data-driven companies that utilize Business Analytics achieve a competitive advantage because they are able to use the insights to:\r\n<ul><li>Conduct data mining (explore data to find new patterns and relationships)</li><li>Complete statistical analysis and quantitative analysis to explain why certain results occur</li><li>Test previous decisions using A/B testing and multivariate testing</li><li>Make use of predictive modeling and predictive analytics to forecast future results</li></ul>\r\nBusiness Analytics also provides support for companies in the process of making proactive tactical decisions, and BA makes it possible for those companies to automate decision making in order to support real-time responses.","materialsDescription":"<span style=\"font-weight: bold; \">What does Business Analytics (BA) mean?</span>\r\nBusiness analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company. Business analytics are performed in order to identify weaknesses in existing processes and highlight meaningful data that will help an organization prepare for future growth and challenges.\r\nThe need for good business analytics has spurred the creation of business analytics software and enterprise platforms that mine an organization’s data in order to automate some of these measures and pick out meaningful insights.\r\nAlthough the term has become a bit of a buzzword, business analytics are a vital part of any business. Business analytics make up a large portion of decision support systems, continuous improvement programs and many of the other techniques used to keep a business competitive. Consequently, accurate business analytics like efficiency measures and capacity utilization rates are the first step to properly implementing these techniques.","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/Business_Analytics.png"}],"additionalInfo":{"budgetNotExceeded":"","functionallyTaskAssignment":"","projectWasPut":"","price":0,"source":{"url":"http://www.globalcio.ru/projectoftheyear/2016/projects/#best_decision/34/project/674","title":"Media"}},"comments":[],"referencesCount":0}},"aliases":{"1":["qlik-sense-hpe-service-manager-sm-hpe-universal-configuration-management-database-ucmdb"]},"links":{"first":"http://apis.roi4cio.com/api/implementations?page=1","last":"http://apis.roi4cio.com/api/implementations?page=1","prev":null,"next":null},"meta":{"current_page":1,"from":1,"last_page":1,"path":"http://apis.roi4cio.com/api/implementations","per_page":20,"to":1,"total":1},"loading":false,"error":null},"agreements":{"agreementById":{},"ids":{},"links":{},"meta":{},"loading":false,"error":null},"comparison":{"loading":false,"error":false,"templatesById":{},"comparisonByTemplateId":{},"products":[],"selectedTemplateId":null},"presentation":{"type":null,"company":{},"products":[],"partners":[],"formData":{},"dataLoading":false,"dataError":false,"loading":false,"error":false},"catalogsGlobal":{"subMenuItemTitle":""}}
