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.
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.
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Report
Objects assembles the building blocks from the object Layers to supply
textual analysis and visual analysis.
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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.
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It uses in Business Intelligence.
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OLAP helps the BI platform to improve
performance and analysis extensively.
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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.
