Non-administrative users can only view and use objects in the public folder, but they cannot delete or create new objects in the public folder.
To access the public folder, log into the MicroStrategy developer as an administrator and go to the Public objects option. By expanding the button, the following screen opens, showing various public objects available in MicroStrategy.
MicroStrategy - Schema objects
Schema objects are MicroStrategy objects that are a logical representation of structures in a data warehouse. These are the objects that are decided upon when creating a MicroStrategy project.
Log in to the MicroStrategy developer as an administrator. Go to the MicroStrategy tutorial and expand the Schema Objects option. The following screen opens and displays the different schema objects.
Here are the different schema objects with their deion.
Facts - These are the numeric values, which can be aggregated to represent the value of some company data.
Attributes - They represent the granularity of the data in the fact table. This is usually the company's data deives.
Hierarchies - These represent the relationship between
Functions and Operators - These are the various built-in math functions and operators available in MicroStrategy for applying calculations to data.
Tables - They simply represent data in tabular form (columns and rows).
Transformations - These are the data transformation features used for data analysis based on time series.
Partition Mapping - This feature is used to create a
MicroStrategy - Report Objects
Each report in MicroStrategy is built using certain underlying objects that represent the scenario of business. These objects together represent the data set requested by the usercore of the report as well as the relationship between the different data elements.
To get the report objects for a report, open the report and click on the report object icon as shown in the following screenshot.
The screenshot above shows the report objects used in the report .
In the current example we have three report objects -
Category - These are of a report attribute indicating the category of products sold.
Region - This is a report attribute showing the region of the products sold.
Year - This is an attribute that contains two metric objects (profit and income).
Report objects are very important in spective report design when deciding which fields from the data source go into the report as well as the calculations applied to those fields.
MicroStrategy -Report Types
Reports created in MicroStrategy can be viewed from a different perspective. Some can only be viewed as numbers and text, while others as graphics. We can also combine textual and graphic visualizations.
Reports created in MicroStrategy can be viewed from a different perspective. Some can be thought of as just numbers and text. While some others only in the form of graphics. We can also combine textual and graphic visualizations.
These are the three types of reports used in MicroStrategy Desktop.
Grid reports - These reports only display text information in the form of grids showing rows and columns of data .
Graphical reports - These reports show
Combined reports - These reports can show the combination of Grid and Graph reports.
Let's discuss these types of reports in detail.
Consider the report created from employee data previously. As we only display the textual information showing the employee ID and salary of each of the departments, this is an example of a report grid.
We can choose an appropriate graphical visualization of the data from the gallery of visualizations available in MicroStrategy. In the capture Next screen we see the bar chart created for the above dataset by just clicking on the histogram visualization available in the right pane.
We can combine both the grid and the graph charts by adding both types of visualizations to a single screen.
MicroStrategy - Slicing
The operation of slicing a dataset involves Creating a smaller dataset by filtering a dimension. It helps analyze the relationship between a given dimension and all remaining variables in the dataset.
Consider the dataset, All sales, which contains the following dimensions -
- Product line
The following screenshot shows a chart with the dataset projecting all variables.
Now let's see the Sales value for each value of the category dimension. , we can go to Editor → Visualization, and keep the Category dimension in the vertical axis.
Next, keep Sales on the horizontal axis. Choose alsoAlso the option Color by as sales.
This will produce the following screenshot with the chart showing the sales data for each category.
MicroStrategy - Dicing
The operation of slicing a dataset involves creating a smaller dataset by fetching multiple values of one dimension relative to a value of another dimension. For example, we get the sales values for different subcategories of products relative to a single category. Here there is a hierarchical relationship between the category and the subcategory of products.
Consider the data set supermarket which contains the following dimensions -
- Customer segment
- Product category
- Product sub-category
Following screenshots show the steps to follow to split the data according to the dimensions segment of customers and sub-category of products.
First, let's create a grid report with the customer segment and product subcategory dimensions. We can also add the Profit metric.
Next, let's create a filter using the segment dimension of For this filter, we choose the value 'Customer Segment '. However, we get the profit value for all the values of the subcategories of this customer segment. Here, the data is diced into the sub -categories for a given customer segment.
MicroStrategy - Pivoting
Pivoting data in tables is done when we want to swap the position of columns and rows. It is also called data rotation . Changing this structure produces different types of data summaries.
The sales value for the All_sales table is summarized pfor each sector of activity. In the following screenshots, each row represents a Business Line and Sales value for each product line in different columns.
However, if we want to see the result as Product Line in each row and Business Line in each column, then we need to apply the pivot. Here are the steps to apply the pivot.
Create the table with the required dimensions and measurements as shown in the following screenshot. Here the sales are summarized and displayed for each industry in each row.
Using the visualization editor, swap dimensions in rows and columns. Use the swap button as shown in the following screenshot.
As we can see, the sales summary is now displayed for product line in each line.
MicroStrategy - Drilldown
Drilldown is the process of dropping down a hierarchy of dimensions to get more granular values from the measures. In a dataset with multiple dimensions, is related to each other in a hierarchical way, we start with one dimension at the top, then gradually add other dimensions to get new granular values.
Explore options provide a better understanding of how different level values are aggregated.
In the all_slaes dataset, consider the following 3 dimensions applied to the Sales measure.
- Product range
Here is the steps to zoom in.
Create a visualization with dimension - product line and measure sales as shown in the following screenshot.
Addr the dimension category when viewed under the product line. As you can see, the value in the sales column changes, reflecting the values in each category below the product row.
Next, add the dimension subcategory under the dimension category and further modify the values in the sales column.
MicroStrategy - Rollup
Rollup is the process of moving up the dimension hierarchy of a dataset As we progress the metric values become less granular and more summarized. This is the opposite of drilldown. For example, in the Zone → region → country hierarchy, we go from one zone to a country and finally the values are summarized at the country level. This process is called Rollup.
In the dataset named All_Sales, consider them following dimensions for a rollup.
- Gproduct range
Create a visualization with the three dimensions mentioned above above and sales as a measurement value.
Let's remove the dimension subcategory from the visualization above. Now the output shows the summary at the level of the category. To delete, right click and choose Delete from the options.
The result now shows the value of sales at the category level.
MicroStrategy - Creating Metrics
Metrics in MicroStrategy are the calculations performed on the data. are the derived columns that display results such as the sum or the average of certain numeric values of a column in the source data.
They are useful for creating custom calculations required by the business The creation of a metric involves the use of integral functions.are already available in MicroStrategy. The Formula Editor is used to create the formula for a metric.
In this example, we aim to find the average sales for each subcategory under each category from the sales data. This can be done by creating a metric that uses the Avg function to find the average sales. The steps to create and use this metric are as follows.
Create a report with category and subcategory as its two columns. Next, right-click anywhere under the Data Source tab and near one of the measure fields. A pop-up window will appear and display the option to create metric.
In the metric editor, write the formula for average sales. Save the metric by giving it a name, say "AvgSales ".
Now the AvgSales metric appears underdashboard data as a metric. It can be dragged to the classified metric and then appears in the report.
MicroStrategy - Nested Metrics
Nested metrics in MicroStrategy are calculations where one aggregate function is included in another. They are useful when in the design of the data warehouse we do not have data stored at the required level of granularity. In this case, we create an internal formula and an external formula. Their combination creates the nested metric.
In this example, we aim to find the average sales for each sub-category compared to the total sales for each category.
Create a report with category and subcategory as its two columns. Then click the right button doesn 'anywhere under the ' tab of the data source and close 's one of the fields of measurement. A pop-up apappears and displays the option to create a metric. We create the first metric with the following formula and name it sum_subcat_sales.
Next we create another metric with the name Category_sales. In it we write the internal formula of the sum of sales for each category ory and the outer formula giving the average sales for each category, corresponding to the subcategory.
Finally, drag the two newly created metrics to the report to see the result.
MicroStrategy - Creation of derived metrics
We often need calculated metrics that are not already available in the data source. In this case, the metric values can be calculated from the existing metrics, using the Create Metric option. Thus, creating a derived metric is an approach to create values that we will need frequently in the report but whichdo not exist in the data source.
In this example, we are going to calculate the total shipping costs and unit price of a product in the sales data of large stores. Here are the steps to calculate it.
Let's create an analysis report using big box sales. The report contains the product subcategory as an attribute and unit price as well as the shipping cost as a metric.
Next, right-click next to one of the metrics and choose the Create a metric option . This gives us a window to write the formula for the new metric. Here, write the formula we use in the existing metrics. The formula is the one shown in the following screenshot.
The new metric appears below the list of metrics from the data source. We drag it to the grid reportexisting.
MicroStrategy - Metric Comparison
Metrics are the numerical values on which we can apply mathematical calculations and also compare them numerically. MicroStrategy desktop provides functionality to compare the values of two metrics using filter functions. If needed, we can also create a derived metric to make complex comparisons based on a specific calculation.
Here are the steps to create a comparison between two metrics.
Create a visualization with the grid report using superstore.xlx as a sample dataset. Then drag the two metrics - Unit Price and Shipping Cost - under the filter tab as shown in the following screenshot.
Enter specific values in the filter condition of the two metrics, so that we can compare their values ina beach. The foll The screenshot below shows the result after entering the values.
MicroStrategy - Creating filters
Data filtering is a very important part of data analysis and visualization. MicroStrategy Desktop provides a variety of options for filtering data in a report. It has simple filters, which retrieve data based on values selected by user. It also has features to create complex features, which will filter data based on calculations.
In this chapter, we will learn the steps of base to create a filter on a column with non-numeric values.
In this example, we aim to create a filter on the field subcategory in a report of grid composed of the field category, the sub-category and the sales.
Create a new visualization bychoosing the field category, the subcategory as rows and sales as the metric. The visualization is shown in the following screenshot.
Go to the Filter tab next to the Editor tab. Drag the field subcategory to this tab. It will automatically create a filter of type Dropdown as shown in the following screenshot. Also notice that the number of values for this is shown in parentheses (25).
Now tick the specific values on which we want to filter the results in the report. By checking these values, only the respective results are visible in the report.
MicroStrategy - Advanced filters
The advanced filter function is useful for applying conditions of filtering, which will otherwise involve complicated steps. In MicroStrategy desktop, we access these features after the filter is created and applied to the report.
We have the following additional options in addition to the checkbox option.
- Search field
- Radio button
- Drop-down list
In this chapter, we will take a look at the search box option in detail.
Using the search field
The search field option is available by choosing the already existing checkbox filter. Right click on it to get the display type option as shown in the following screenshot.
Start writing the first letters of the subcategory we want to filter. It automatically fills in the different values of the dataset. We choose specific values by selecting them with a few clicks.
Completing the selection, we get the result in the report as shown in the following screenshot.
MicroStrategy - Built-in Shortcuts and Filters
In MicroStrategy, we can create shortcuts to filters. For this we need to use the results of an existing report as a filter for another report. The first report itself becomes a filter inside a new report. This type of filter is called a report shortcut filter.
This is part of the MicroStrategy server edition and we will take some examples of datasets built into the MicroStrategy server. Here are the steps to create a shortcut to a filter.
Open the filter editor. Choose the filter definition area and double-click it. This will open the dialog box showing the option "Add shortcut to filter".
On the next screen a filter dialog will appear. Enter the name of the filter we want to use or click Browse and select thefilter to use.
Finally the following screenshot opens with the name of the filter and the definition of the filter which is now a shortcut to a filter.
MicroStrategy - Report Refresh
Users repeatedly access reports created in MicroStrategy servers to find new results based on additional data collected in the source of the report. Therefore, the report data should be refreshed both periodically and on user demand.
The report Desktop versions of MicroStrategy can be refreshed by flagging just redo the data. This is done using the refresh button available in the menu.
Consider the All_sales report. Currently, the report displays the data as shown in the following screenshot.
Let's add some data to the source. We add the categoryrie aquatic animals. By clicking refresh button we get the new result as shown in the following screenshot.
MicroStrategy - Intelligent Cubes
When we run reports created in MicroStrategy, they retrieve data from the warehouse to apply the calculations and generate a report. When multiple users request the same report but with different value ranges or different filter conditions, the warehouse has to repeat similar calculations for each of the reports and this affects performance.
To avoid this, MicroStrategy uses smart cubes , which is an object located in the middle layer between the reports and the warehouse.
The following diagram illustrates the role of the smart cube.
The Intelligent Cube is shared as one in memory copy, among the various reports created by many users. dataset is returned from thedata warehouse and saved directly to Intelligence Server memory. Several reports are created to collect data from the Intelligent Cube instead of querying the data warehouse.
These are the features that make smart cubes useful.
- Supports dynamic aggregation.
- Can be scheduled for refresh.
- Supports creation of derived metrics.
- F better performance than querying the warehouse directly.
- Multiple cubes can be used in a single dashboard.
MicroStrategy - Creating a dashboard
A dashboard is made up of several visualizations. It shows many attributes grouped together in separate visualizations. When we place a common attribute or metric in multiple visualizations, it is easy to study the variations between them.
In theNext example, we'll create a dashboard showing some common attributes among visualizations.
Create a grid visualization using superstore.xlsx as an example data source. We drag the product attributes - Subcategory and Shipping costs - into the rows area. Then we insert the second visualization into the report as shown in the following screenshot.
Add all of the above attributes plus an additional attribute named unit price to the newly inserted visualization as shown in the following screenshot.
Finally, apply different types of visualization to these grids. We apply a pie chart to the visualization of the top and the heat map graph to the bottom visualization as shown in the following screenshot. The result shows a dashboard with some common attributes used din both visualizations.
MicroStrategy - Formatting a dashboard
A dashboard is made up of several visualizations. Different parts of the dashboard can be formatted for a better look using the available dashboard formatting option.
In the following example, we will format a dashboard using additional colors and boxes highlighted.
Consider the dashboard visualization we created in the last chapter. Choose the dashboard formatting option as shown in the following screenshot.
Then in the screen that appears with the formatting options such as font selection, fill color and style etc. the selections as shown in the following screenshot.
Finally, the formatting is applied to the dashboard. The formatting is reflected in the two visualizations present in the dashboard.
MicroStrategy - Graph visualizations
MicroStrategy Desktop provides 10 standard charts which are readily available to be plotted with a data source. Each of them gives a different view of the data depending on how many attributes or metrics we're going to use. The coloring features of each of them will make it easy to understand the different pieces of data present in a single data visualization.
In the rightmost window of MicroStrategy Desktop, there is a Visualization Gallery, which shows options for 10 different chart types.
Grid - Represent data as a data grid in the form of rows and columns.
Heat Map - Displays rectangles of differentThe colors showing a range of values.
Bar Graph - Shows vertical bars of varying lengths indicating the strength of the parameter being measured.
Line chart - Displays lines indicating the change in the value of one variable relative to another.
Area chart - Displays areas of different colors corresponding to different values.
Pie chart - Displays the slices in a circle, with the size of the slice corresponding to the value of the variable being measured.
Bubble chart - Represents many bubbles corresponding to the range of the variable's value.
Combo Chart - Combines bar chart and line chart into one visualization.
Map - Displays data as map markers on an interactive map.
Network - Used to identify relationships between related items and groups of values.
The following screenshot shows different visualizations of graphs.
MicroStrategy - Grid visualizations
Grid visualization is the most common form of visualization. simpler than MicroStrategy, but a very powerful analysis method. Here the data is presented as a grid with rows and columns as well as column headers. It provides features like sorting and sorting. 'data mining.
Creating a grid visualization
After loading the required dataset into the MicroStrategy environment, we pull the required fields to the editor panel. This automatically creates the Grid visualization. In the following example, as shown, we extract the appropriate fields from the dataset and create a grid.
OpOperations in the grid visualization
The following operations can be performed in a grid visualization.
- Sorting data on multiple columns
- Swapping columns and rows
- Explore an attribute
Sorting data on several columns
The grid visualization allows you to sort simultaneously on several columns. Right click on a column name and choose the advanced sort option. This brings us to a screen where we c and select all the columns and their order to perform the sort.
Swap columns and rows
We can swap columns and rows in the grid visualization to create a pivot report. Just drag and drop the columns into the rows as shown in the following screenshot.
We can explore an attribute on the visualization ofthe grid to descend to the values of the next attribute in the hierarchy. Right click on the column name and choose the drill option as shown in the following screenshot.
MicroStrategy - Heat map visualization
A heat map visualization shows adjacent colored rectangles, each representing an attribute of the dataset. It allows you to quickly enter the state and impact of a large number of variables at once. For example, heat maps are often used in the financial services industry to examine the condition of a portfolio.
Rectangles feature a wide variety and nuance of colors, which emphasize the weight of the various components. a Heat Map visualization -
The size of each rectangle represents its relative weight.
The color of each rectangle represents itsrelative value. For example, larger values are green and smaller values are red.
Large areas represent different groups of data.
The small rectangles represent elements of individual attributes.
In this example, we will create a heat map visualization for the product subcategory in terms of the profit that they generate.
Create a blank visualization and choose the heat map from the list of available graphics. As you can see, it requires at least 1 metric and 1 attribute.
Let's add a sub-category of products to the groupings tab and take advantage of the size and tabbed color. This produces the heat map rectangles. Green color indicates a profit value of over 50% while red color indicates a profit value of less than 50%.The stronger the green tint, the higher the profit. Likewise, the stronger the red tint, the lower the profit.
It is possible to add more attributes to the Grouping clause and this will produce a large number of rectangles. In this example, add a customer segment and a product container. Passing the pointer of with the mouse over each rectangle, we can see the deion of all the attributes that make up that rectangle.
MicroStrategy - Network Visualization
Network visualization is used to quickly and easily identify relationships between data associated items. For example, visualizing a social network. Attribute elements are displayed as nodes in the visualization, with lines (called edges) drawn between nodes to represent relationships between elements. Once the visualization is created, users can visualize the characteristics of the nodes and relations between them, using display options such as node size, edge thickness, and edge color.
In this example, we will create a network visualization between the customer segment and the product subcategory in terms of profit. Here, the customer segment and the product sub-category are the nodes, while the profit is the edge representing the re relationship between them.
Create a new visualization by choosing a network as an option. As indicated, at least 1 attribute is needed to add.
Add a customer segment in the "From article " field and a product subcategory in the "To article " field. Additionally, the attribute profit is added to the Size of edge box. The following diagram shows the created network diagram. The thickness of the edge is proportional to the size of the profit.
Adding profit to the color of the edgesgives a better diagram which shows different colors of the edges, depending on the percentage of profit it represents for a given product subcategory of a given customer segment.
Visualization with multiple datasets
So far we have seen reports with one data source as the source. we can also add multiple data sources to the same report. In this case, we can use the attributes and metrics from both sources to create the visualization. The result appears as if it was a single source This happens because MicroStrategy combines these two sources and treats them internally as one.
Here are the steps to combine two source datasets and create a visualization.
Create a report with a data source. We will use All_sales.xlsx in the example. Then click on the New Data menu asshown in the following screenshot.
You can now see the two data sources available under the dashboard. The attributes and metrics of both of these sources are available under their respective names.
Then drag the 'Business Line attribute from All_sales.xlsx to the rows area Drag the attributes “customer segment” and “product category” from the second data set to the rows area. The grid visualization appears with the data from both datasets.
MicroStrategy - Data filtering in the dashboard
A dashboard is a document with many visualizations showing the results simultaneously. When analyzing the data we may need to apply a filter which will show the results. 'effect of the filter on each of the visualizations present in the dashboard.In addition, all the results musthave a synchronized value. This is possible by creating a normal filter and applying it to the dashboard.
Here is an example of applying a filter to the dashboard.
Let's take the dashboard we created in the last chapter. Let's create a filter as shown in the following screenshot.
Click on the Select target option and apply the filter to visualization2. This will change the values displayed in visualization2, however visulaization1 will display a synchronized result.
After applying the filter, click on some of the product category values visible in the filters section of the top bar. This will change the diagrams, depending on the value selected. In the following example we have selected multiple values and you can notice how the pie chart changes as each of the values is selected.onnae.
MicroStrategy - Adding Web Content
Besides data from different sources, we can also add data from the web in a MicroStrategy report. It becomes part of the visualization. The visualization shows the entire web page, which appears integrated into it.
Here are the steps to get content from the web.
Go to the + menu and choose the HTML container option as shown in the following screenshot.
Now an Iframe box appears asking us to enter the URL of the website we want to display. Enter the URL complete as shown in the following screenshot.
Finally, the web page appears as shown in the screenshot next.
MicroStrategy - Conditional Formatting
Conditional Formatting in MicroStrategy involves highlighting parts of the visualization, which meet certain criteriapredefined in their values. Usually, in the case of metrics, we want to highlight values that are greater than a certain percentage. There may also be examples of certain categories of product names being highlighted, etc.
In MicroStrategy desktop, we can achieve this by using the threshold function. In this example, we'll define the color to use to highlight certain values when a certain threshold is met. Here are the steps.
Create a grid report with all_sales.xlsx as a sample dataset. Put the attributes Business line, Category in the grid with th Sales metrics. Right click on the metric sales and we have the option to choose the threshold as shown in the following screenshot.
The capture of next screen shows options to choose different colors according to the valueas a percentage of sales.
Finally, the result of applying the threshold is shown in the following screenshot. In the Sales metric , the values are highlighted in different colors based on the percentage value of sales over total sales.
MicroStrategy - Custom groups
Custom groups are a type of virtual attributes useful by grouping many attributes and presenting them as one attribute. For example, if we want to analyze the sales result every 4 months instead of each quarter, we need to create a complex formula to pick those months and apply them in the calculations. Instead, we can create a custom group by grouping the required months together and use that custom group as a single attribute.
Here are the steps to create a custom group.
Open the custom group editor and drag un object from the object browser to create a custom group.
The following window appears at the end of the step above. Choose the Add attribute qualification option.
Then browse and choose the required attribute ibutes to create the custom group.
MicroStrategy - Report Cache Feed
A report cache is a data store that contains information that has been recently requested from the data source to be used in a report. a report is run for the first time, a cache is created. Report cache contains results retrieved from database, files or web sources.
Benefits of Report Cache
These are some of the benefits we get from using MicroStrategy's caching feature.
A cached report returns results faster because the dataThese are already available in MicroStrategy software.
Execution time involving calculations and derived metrics is faster because cached reports do not need to be run on the source of data.
In a cache, the results of the data source are stored and can be used by new work requests that require the same data.
Types of cache
Three types of cache are used in MicroStrategy.
Report caches - These are the results that are precomputed and preprocessed. They are stored in the Intelligence Server machine memory or on disk. They can be retrieved faster than by repeatedly rerunning the query against the data warehouse.
Item Caches - These are frequently used table items, which are stored in the memory of theIntelligence Server machine. They can be retrieved quickly when users browse the attribute element views.
Object Caches - These are data objects stored in memory on the Intelligence Server, so that they can be retrieved quickly in subsequent requests.
Cache can be enabled, both at the report level and at the project level. This is done using g the project configuration editor.
Project level activation
If caching is enabled at the project level, all reports in the project will use the caching feature.
Report level activation
When activating at report level, only specific reports will use the cache. Even if the report is disabled at the project level, it will work at the report level, when it is enabled at the report level.t.
Disadvantage of cache
Cached data is not always the most up-to-date because it has not been run through the data source since the cache was created . This can be avoided by deleting the report cache before running the report. This forces the report to be run through the data source again, thus returning the most recent data from the data source. However, it needs administrative privileges to delete a report cache.
MicroStrategy - Data Marts
Data Mart is a smaller form of data warehouse, which meets specific data analysis needs. It is typically derived in a small part from the larger data warehouse. The main purpose of creating data marts is to perform analysis, which is difficult to do in normal warehouse due to different level of data granularity in warehouse or application of calculatedls complex.
In MicroStrategy, a data mart is created by following the following steps.
Open a report in edit mode. Choose Datamart → Configure Datamart. And the following window appears.
Choose the appropriate location from the drop-down menu of the database instance.
Choose the option to create a new table, if the table needs to be recreated each time the report is executed. Or you can choose to append to an existing table so that the data is appended to the result of the previous execution.
If successful after completing the three steps below above, the data mart is added to the report.
MicroStrategy - Predictive Models
Predictive modeling is a mathematical approach to building models based on existing data, which helps to find the value or future trend of a variable.s models require very heavy mathematical and statistical analyzes.
Here are some examples where predictive modeling is used.
A university tries to predict whether a student will choose to enroll by applying predictive models to applicant data and admission history.
Go to a retail store to find out which two items are most likely to sell well together.
In the airline industry, to estimate the number of passengers who will not show up for a flight.
MicroStrategy can help you perform predictive modeling because its data mining services are fully integrated with its BI platform.
Predictive Analysis Using MicroStrategy
MicroStrategy has data mining services, which allow users tos Import Predictive Model Markup Language (PMML) from third-party data mining tools, which can then be used to create predictive reports.
PMML is an XML standard that represents data mining models developed and trained by the data mining tool. PMML supports a number of different data mining algorithms including regression, neural networks, clustering, decision trees, and association. It integrates data transformation and statistics deives.
The following diagram describes the process for reporting predictive data models in MicroStrategy.
Once imported into MicroStrategy, we can improve the model using the following features.
Features for predictive modeling
Here is the list of features that highlight the strengths of MicroStrategy for use.é as a predictive modeling tool.
Built-in data mining functions - There are 250 basic functions, OLAP, math, finance and statistics that can be used to create KPIs.
Data mining integration using PMML - It allows users to import PMML from within 'third-party data mining tools, which can then be used to create predictive reports.
User Scalability - Hundreds of thousands of users, internal and external to the organization, can access this functionality.
Data Scalability - MicroStrategy relational OLAP architecture (ROLAP) associated with its Intelligent Cube technology can handle any size of database while delivering high performance.