Rich grid analysis
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.
Decomposition OLAP Analysis
Another concept is 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 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.
Dashboard OLAP 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.
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 evaluate 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.
OLAP Calendar Picker
The most often used dimension in 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 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.
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 analysis 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.
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 the customized visual details could be opened on a given URL by any anonymous user.
Ad-hoc 'Named Sets', 'Calculated Measures' and 'Drillthroughs'
Ad-hoc 'Named sets' and 'Calculated measures' is 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'.
OLAP data subscriptions
If you do not want to go to the application to open the important analysis every day, there is a concept of analysis
Subscription in which you can define the time interval in which the results of analyses will be delivered to you by email at a scheduled time.
This way you can receive your analytics embedded in an email message or attached as an Excel or PDF file.
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 OLAP dimension level to Drill current data with multiple Drill-Down steps or get back to the previous state of analysis.
Select 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 OLAP dimension to drill.
Select OLAP dimension hierarchy - level to drill
Chart on the dashboards mobile view transforms to display drill-down dimension level.
Repeat this step multiple time to reach analysis data of interest.
Select 'Back' to return to previous states of 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
The multitenant OLAP analysis is a more advanced concept where you have many clients that access the same report,
but every client has its own OLAP cube as a source of data for the report. In this scenario the assumption is that all clients have OLAP cubes of the same structure.
The analysis itself and all other application data are completely isolated among the clients.