OLAP Analysis & Reporting Guide

Modern OLAP/Tabular analysis & reporting features for SSAS environment. A quick guide to new concepts of OLAP data analysis and reporting. New ways to analyze OLAP cubes and Tabular models, discover trends and patterns, share with other users, embed within other apps, or schedule OLAP multidimensional and tabular data reports.

A quick guide on OLAP/Tabular Analysis and Reporting features

Discover new concepts of OLAP/Tabular analysis and reporting available for the Microsoft SSAS environment. The ways to collaborate or subscribe to OLAP data reports. Learn about Embedded analysis, Multitenant BI environments, Decomposition analysis, Mobile BI, and more. With improvements in browser engines and HTML5/CSS3 technologies, new concepts emerged on how we analyze OLAP data...

OLAP Analysis & Reporting
OLAP/Tabular Analysis and Reporting evolved with time. Check out new OLAP tools and technology improvements that will improve the adoption of OLAP Analysis and Reporting possibilities in your business intelligence environment. Discover new OLAP analysis/reporting features and analytic approaches you can quickly utilize in your BI environment. As opposed to traditional stand-alone OLAP client software, web technologies bring more opportunities to the task of OLAP BI analytics and visualizations. Find out new concepts on how to collaborate or subscribe to OLAP data analysis. Learn about Embedded analytics, Multitenant BI environments, Decomposition analysis, Mobile BI, and more.

Powerful OLAP analysis grid

The most common concept is the grid analysis where you can drag and drop multiple dimensions to the category or series axis. At the same time, you can add any number of measures, which results in rich insights into your data that can be drilled down in many ways to get relevant perspectives. Such powerful grid analysis is nothing new, but today you should expect ease of sharing such analysis with authorized users by just sharing the analysis URL. Users can collaborate on the analysis or clone it to make their analytics and reports without starting their analysis from scratch.

  • Drag and drop multiple measures on the OLAP analysis grid.
  • Drag and drop dimension attributes and hierarchies on the Category or Series axis.
  • Slice the current view by dragging and dropping dimension attributes and hierarchies on the filter axis.
  • Drill down any dimension member to the deeper levels to discover new insights on the category or series axis.
  • Drill through the current cell to learn its leaf-level details.
  • Save analysis and share with other users.

Decomposition OLAP Analysis

Another concept is the decomposition analysis . In this approach, we analyze data visually from top to bottom. After we add an initial dimension that is displayed as a chart, we add any number of drill-down dimensions. As soon as we select the item in the upper-level dimension, all child dimensions are immediately filtered with the upper item selection. This is a visually intuitive and interactive approach to OLAP data analysis. Decomposition OLAP/Tabular analysis presents data analysis in multiple steps, while the user can see all the steps at the same time and has the ability to change each step definition at any time.
  • Select a measure and add two or more dimension attributes for the decomposition analysis.
  • As soon as you click on any members in the higher dimensions chart levels, all lower charts and dimension levels will be automatically sliced with the upper member selection.
  • Change decomposition filters to slice the whole view with another insight.
  • Continue with grid analysis from any dimension-level chart.

Dashboard OLAP/Tabular analysis

The most usable concept of the modern OLAP analysis is to use it on the dashboard with other charts based on previously created analyses. Such OLAP data-based charts on the dashboard are interconnected. Selecting an item in one chart could immediately slice the data in other charts. Such dashboards support various analytic actions to drill down, drill through, slice, quick explore, export data in a PDF file, and many other useful analytic options.

  • Drag and drop an empty chart on the dashboard surface and set its position and dimensions.
  • Connect the chart with the existing analysis previously created with 'Analysis Grid' or 'Decomposition Analysis'.
  • Arrange any number of charts on the dashboard.
  • Add dashboard filters based on OLAP dimension members.
  • Use one of the charts as a slicer for other charts data.
  • Export to PDF or share with other users.

OLAP data KPIs and Alerts

To compare results of the OLAP analysis with expected business goals create KPIs and Alerts based on the OLAP data. In this concept, you can define success and fail thresholds, which will be evaluated in real-time to give the current status of a KPI. The KPI can be visualized on the dashboard, organized into scorecards, or you can create an alert that will send you an email if the KPI reaches the required status.
  • Create a new KPI that will be connected to the existing OLAP analysis.
  • Set 'Success', 'Evan', and 'Fail' thresholds for the KPI.
  • When the KPI is opened on the dashboard, its values will be automatically resolved with real-time data, and show the success and trend indicators.
  • Organize KPIs to Scorecards.

OLAP/Tabular Calendar Picker

The most often used dimension in the analysis is the Time dimension. It is hard to imagine serious analysis without using a period of time to which it refers. Even if the time dimension consists of hierarchies of Date-Time structures such as the year > quarter > month > day, modern OLAP analysis offers a calendar picker that is more user-friendly for date selections than checking items in the hierarchy. Furthermore, relative date periods such as 'last month', 'this quarter’, or 'year-to-date' can easily be selected to slice the data.

  • Set up OLAP time intelligence on the OLAP cube data source within the Kyubit application.
  • After initial OLAP time intelligence is set up on the Time dimension, all users could use the same dimension with the Calendar Picker.
  • After the same time dimension is added to the analysis, report, or dashboard, a user can choose to set its values with the calendar picker. Much more convenient than picking members from the list.

Embedded OLAP Analysis

If you have a web application where you need to integrate data from the OLAP data source, you can use the embedded analytics concept and quickly integrate previously created OLAP analysis into the environment of your web application using an Iframe HTML element, analysis URL, and customize the visual details of the analysis to match your environment. You can also embed a blank analysis into your environment where users can start analyzing the data which will visually appear as if the analysis was done in the hosting application.

  • Set up OLAP analysis grid view to be opened from another web application or website.
  • An embedded OLAP analysis grid could display existing analysis or an empty grid to start new analytics.
  • Embed a dashboard that is based on OLAP analysis charts to another web application or website.
  • Embed individual charts within another web application that opens existing OLAP analysis from the Kyubit application.

Published Analysis

OLAP technology with Microsoft Analysis Services requires Windows credentials to authenticate the user and authorize access to data. There might be cases where you create a data analysis or a visualization and would like to share it with users who do not have access to the underlying OLAP data source. Here comes the concept of Published Analysis. A published analysis has defined a Windows account to be impersonated within the analysis itself. A published analysis with customized visual details could be opened on a given URL by any anonymous user.

  • Mark the selected OLAP analysis for publishing in the report > publishing view.
  • Customize visual details.
  • Set impersonation details, so the chart will be opened for public view with specific user credentials.
  • Share the published analysis URL that can open published OLAP analysis from anywhere.

Ad-hoc 'Named Sets', 'Calculated Measures' and 'Drillthroughs'

Ad-hoc Named sets and Calculated measures are another very useful concept for analysts. While primarily named sets and calculated measures are created in the OLAP cube, analysts can create a new named set or a calculated measure directly in the analysis application which does not need changes in the OLAP cube structure, reprocessing of the cube, nor any services from other IT professionals. Similarly, modern OLAP analysis brings the concept of the Ad-hoc 'Drillthrough columns'.

  • While inside the OLAP grid analysis, create new Named sets, Calculated Measures, Calculated Members and Drilltrhough actions.
  • Give permissions to other users on the created content, so they can reuse it.
  • Use Named sets, Calculated Measures, Calculated Members and Drilltrhough actions to prepare OLAP reports and to be used on the dashboard OLAP analysis.
  • Export created drillthrough results into the Excel file.

Schedule OLAP report delivery

If you do not want to go to the application to open the important analysis every day, there is a concept of scheduled OLAP report delivery in which you can define the time interval in which the results of analyses will be delivered to you by email at the scheduled time. This way you can receive your analytics embedded in an email message or attached as an Excel or PDF file.

  • Open the analysis/report for scheduled email delivery.
  • Select scheduled time preferences (including scheduling in various time zones)
  • Select the email template to be used with the scheduled OLAP analysis.
  • Select contacts and contact groups that will be recipients of the scheduled OLAP analysis.
  • Optionally, set conditional sending which occurs only when the defined condition is met.

Mobile OLAP analysis

Perform OLAP analysis Drill-Down and Drill-through actions to find more in-depth details of your data with the touch of your fingers from Dashboards Mobile BI view. Select the OLAP dimension level to Drill current data with multiple Drill-Down steps or get back to the previous state of analysis. Select a predefined Drill-through action that will return row details of the current OLAP visualization. Open Dashboard with charts based on OLAP data. Touch chart segment you would like to explore with new details (Drill). Select OLAP action: Drill-by, Drill-Down, or Drill-Through. Select the OLAP dimension to drill. Select OLAP dimension hierarchy - level to drill, experience the power of our multi-tenant solution, chart on the dashboard mobile view transforms to display drill-down dimension level. Repeat this step multiple times to reach analysis data of interest. Select 'Back' to return to the previous states of the OLAP analysis

Cell Quick Explore View

Any cell of the OLAP analysis on the grid could be analyzed more deeply by using the Quick Explore view. In this mode, the value from a given cell is further visually analyzed by any other dimension. For example, if the cell value displays the 'Sales Amount' for a certain product, the quick explore view immediately shows this value distributed on the time dimension so that we can see how this value has changed over time. You can also select any other dimension to explore data for this cell or convert quick explore to a full grid analysis for further analytics.

Multitenant OLAP Analysis

Experience the power of our multitenant BI solution, designed to cater to multiple organizations or users, with each one’s data meticulously isolated from the others. This system is perfect for scenarios where numerous clients need access to the same report, but each client has their own unique OLAP cube serving as the data source. The prerequisite here is that all clients maintain OLAP cubes with identical structures. Rest assured, the analysis and all other application data are entirely segregated among clients, ensuring complete data privacy and integrity.

OLAP technology still rocks...

Despite being a relatively old technology, Online Analytical Processing (OLAP) continues to be widely used in many industries and sectors. The enduring popularity of OLAP can be attributed to its robust capabilities and the unique advantages it offers. While newer technologies have emerged, OLAP remains a reliable and proven tool for data analysis, capable of handling large datasets and delivering quick, insightful results. Its longevity attests to its effectiveness and adaptability in meeting the evolving needs of data analysis.
  • Multi-Dimensional View of Data: The first benefit of OLAP is the multidimensional view of data. This allows users to analyze data from different perspectives in a business. For example, a user can view sales by product, then by region, then by time period. This ability to drill through data provides a powerful tool for identifying trends or problems that may not be visible with a simple two-dimensional view of data.
  • Trend Analysis: OLAP reports provide trend analysis at a glance. They allow users to see patterns over time and can provide valuable insights into business performance. This can help in forecasting future performance and making strategic decisions.
  • Advanced Calculations: OLAP tools can perform complex calculations on the fly, even those that the original database designers did not anticipate. This gives users the flexibility to create their own calculations within the confines of the multidimensional model.
  • Speedy Data Retrieval: OLAP’s ability to pre-calculate and store aggregated data ensures rapid retrieval of data. This is particularly beneficial for large datasets where real-time calculations can be time-consuming and resource-intensive. With OLAP, complex calculations are not only possible but also returned quickly to the user.
  • User-friendly Interface: Finally, OLAP tools often come with an intuitive, user-friendly interface that enables users with little or no technical skills to generate and analyze reports. This democratizes data within an organization, allowing for more informed decision-making at all levels.
  • Data Consistency: OLAP uses a centralized approach to data management, which helps ensure that users across the organization are working with the same numbers, definitions and assumptions. This consistency is crucial for accurate reporting and analysis.
In conclusion, OLAP analysis and reports offer significant benefits in terms of data analysis and decision-making capabilities. They provide a multi-dimensional view of data, speedy data retrieval, advanced calculation capabilities, trend analysis, a user-friendly interface, and consistent data across the organization.

Step-by-step Tutorials introducing the main Kyubit Analysis concepts with instructions on how to start and use application features ...

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