Explanation of MicroStrategy architecture, metrics, visualization, and predictive analysis

MicroStrategy MS features a metadata-inspired architecture. This metadata may be a central repository, which stores all the objects employed by it.

MicroStrategy is a lead for business intelligence.

The metadata employs by any of the MicroStrategy products. This has guaranteed agreement within the objects' values. The objects stored within the metadata are reusable.

Layered Object Metadata:

Object Layers represent the various layer of objects created and stored in MS metadata.

●       Administration Objects: This Object layer establishes the safety, user grouping, and performance parameters that govern the MicroStrategy apps.

●       Report Objects assembles the building blocks from the object Layers to supply textual analysis and visual analysis.

●       Analysis Objects: This object layer provides the building blocks for classy analysis. The analysis objects built on the objects developed within the schema layer.

●       Schema Objects: This object layer provides a logical abstraction of the database schema that tailor the business model.

ROLAP Architecture

ROLAP provides data for warehouses, cube and operational databases, and flat files.

MicroStrategy architecture describes how it reaches data from various sources using the metadata objects.

Online Analytical Processing (OLAP) may be a multidimensional analysis of business data. It provides the potential for complex calculations, analysis.

●       OLAP Services is an expansion of the MStrategy Intelligence Server.

●       It uses in Business Intelligence.

●       OLAP helps the BI platform to improve performance and analysis extensively.

●       The following may be a brief description of varied OLAP features available in MicroStrategy Desktop.

1.     Aliasing: This feature employs to rename any object on the report grid, like attribute names, union names, custom group names, and metric names.

2.     Banding − will not color groups of rows or columns in the order that they form bands of knowledge that are easy to locate and analyze.

3.     Page-by reports attributes, metrics on a 3rd axis called the Page axis.

4.     Pivoting will not rearrange the columns and rows during a report back.

5.     Sorting offers quick sort, advanced sort, and hierarchical kind of row or columns.

6.     Subtotals − it is wont to add, remove, and edit the subtotals at different levels for the report's metrics.

7.     Thresholds: A threshold highlights data that meets conditions defined by the user.

●       These features do not cause the report to be executed against the warehouse and have a faster reaction time.

●       Following is an example of applying thresholds.

Consider the worker report created within the previous chapter using an excel file. Within the report, we will apply threshold colors to varied salaries using the subsequent steps.

1.     Dynamic MDX Engine − gives control to cube databases from SAP, MAS.

2.     Dynamic SQL Engine generates SQL for accessing data warehouses.

MicroStrategy - Nested Metrics

●       Nested Metric is the calculations during which one aggregation function encloses inside another.

●       They are useful when we do not have data stored at the specified granularity level within the data warehouse design.

●       In such a case, we create an inner formula and an outer formula. Combining them creates the nested metric.

Example

We aim to seek out the typical sales for every sub-category compared to the entire sales under each category.

Step 1

●       Firstly, prepare a report with a section and sub-section then, right-click anywhere under the info source tab.

●       After it shows the create metric option. We create the primary metric with the following formula.

Step 2

●       Secondly, we have to prepare another metric with the name category_sales.

●       We write the inner formula for the sum of sales for every category.

●       Therefore, the outer formula giving average sales for each.

Step 3

●       Finally, drag both the newly created metrics to the report back to see the result.

MicroStrategy - Graph Visualizations

●       It provides ten standard graphs that are readily available to be plotted with a knowledge source.

●       Each of them gives a unique view of the info, counting on the amount of attribute or metrics we are getting to use.

●       The coloring features in each of them will make it easy to know the various chunks of knowledge present. This is during a single data visualization.

Visualization Gallery

A visualization gallery is in the rightmost window of MStrategy Desktop, which shows options for ten different graph types.

1.     The grid represents data within the sort of data grid as rows and columns.

2.     Heat Map shows rectangles of various colors showing a variety of values.

3.     Bar Chart presents vertical bars of various lengths, showing the strength of the parameter measured.

4.     Line Chart shows the lines indicating variation useful of 1 variable about another.

5.     Area Chart shows areas of various colors like different values.

6.     Pie Chart: Shows the slices during a circle, with the slice's dimensions like the worth of the variable measured.

7.     Bubble Chart represents many bubbles like the range of the worth of the variable.

8.     Combo Chart combines bar graph and Line chart into one visualization.

9.     The map shows markers on an interactive map.

10. The network wants to identify relationships between related items and clusters of values.

Different Graph Visualizations

MicroStrategy Predictive modeling:

●       Predictive modeling may be a mathematical approach to creating models that support the prevailing data.

●       Helps to find the longer-term value or trend of a variable.

●       It involves mathematical analysis to make such models.

Examples:

●       Weather forecasting.

●       A university predicts the student’s admission number by applying predictive models to applicant data and admissions history.

●       In the airline industry to estimate the number of passengers who will not show up for a flight.

●       Micro Strategy can help complete predictive modeling as its data processing services integrate into its BI platform.

PA Using MicroStrategy

●       Users try to import Predictive Model Markup Language (PMML) from a third party.

●       PMML is an XML standard that represents data processing models developed and trained by the data processing tool.

●       It supports various data processing algorithms, including Neural Networks, Clustering, Decision Trees, and Association.

●       Data transformation and descriptive statistics get included.

●       The method of making predictive data model reports is as follows:

Features for Predictive Modeling

1.     Built-in data functions - having 250 essential, OLAP, mathematical, financial, and statistical functions that will be wont to create key performance indicators.

2.      Data Mining - allows users to import from third-party data processing tools, which may be wont to create predictive reports.

3.     User flexible − many thousands of users, internal and external to the enterprise, can access this feature.

4.     Data flexible - combined with its Cube technology that handles any database sizes while delivering high performance.

Uses of MicroStrategy:

●       Business Intelligence

o   Build consumer-grade intelligence applications,

o   empowers data discovery, and

o   Provide employees, partners, and customers content in minutes.

●       Hyper Intelligence

o   Deploy analyst by actions into the applications and browse websites for your people who use every day.

●       Embedded Intelligence

●       Cloud Intelligence

o   It is the fastest, most comfortable, and effective way to run your enterprise.

●       Mobile Intelligence

o   Deploys solutions for every user on any device.

o   This feature is customized for your organization/entity with no coding required.

Conclusion:

●       In this article, we came to know about MicroStrategy’s layers of metadata that display different objects in different layers.

●       ROLAP architecture which gave you a clear explanation of its structure, Nested Metrics is explained with an example. And also given steps for creating new metrics to test them to report.

●        Graph visualization and predictive analysis will help you in visualizing and predicting products review and reports.

To learn more about this Strategy follow to MicroStrategy Online training.