MicroStrategy abstracted the SAP HANA data schema, along with other data warehouses and multi-dimensional sources, into one unified system of record, hiding the underlying complexity from end users.

1. SAP HANA INTEGRATION WITH
MICROSTRATEGY

2. WHAT IS HANA?
 SAP HANA, short for "High-Performance Analytic Appliance" is
an in-memory, column-oriented, relational database management
system developed and marketed by SAP AG.
 Designed to process structured data from relational databases and
capable of using three styles of data replication depending on the
source of the data - log-based, ETL-based and trigger-based.
 The in-memory computing engine allows HANA to process data
stored in RAM as opposed to reading it from a disk.
 It allows the processing of massive amounts of real-time data in a
short time.

3. WHY SAP-HANA?

4. RDBMS IN MPP ARCHITECTURE

5. “Typical” Business Intelligence Today

6. IN-MEMORY COMPUTING
 Disk is 1Mx slower than main memory.
 In-Memory computing cost has plummeted drastically.
 It uses a technique called sophisticated data compression to store
data in the random access memory. HANA's performance is
10,000 times faster when compared to standard disks, which
allows companies to analyze data in a matter of seconds instead of
long hours.
 Has a ability to cache countless amounts of data constantly. This
ensures extremely fast response times for searches.
 It stores session data, allowing for the customization of live
sessions and ensuring optimum website performance.

7. STORAGE
 Conventional databases store records in rows.
 Storing data in columns enables faster in-memory processing of
operations such as aggregates.
 Columnar layout supports sequential memory access.
 A simple aggregate can be performed in a single linear scan.

8. TOP REASONS FOR USING HANA
 Access data faster using any column as index,and by accessing
only relevant columns via dictionary-encoded column store.
 It runs both transactions and analytical applications on a single
model.
 Process any data,in any combination instantaneously with SQL.
 Bring ur own code to an open platform.
 Predictive analysis across multiple datasources
(Example Unstructured Text,Geospatial,Hadoop)
 Reduce TCO by consolidating hetrogenous servers into Sap
HANA servers to reduce hardware,lifecycle management and
maintainence.

9. AS A BUSINESS INTELLIGENCE
 Pervasive reporting.
 Faster Deployment.
 Robust,manageable and scalable.
 Source-agnostic data access & integration services allow
accessing and indexing external data from across the entire
organisation and adding them to existing analytical models.
 Views of business information can be persisted in a Persistent
Data Repository, and reconstituted in case of a crash.
 Volume-Provide 3600x faster reporting speed.
 Value-460B Data records analysed in a second.

10. MICROSTRATEGY
 Integrating SAP-HANA with Microstrategy reporting capabilities
we developed Healthcare Management software.
 We made analytical views and calculation views in SAP-HANA
based on our requirements and exported to Microstrategy.
 Using data insight visualization capabilities we are able to display
visually appealing dashboards and insightful reports.
 We have developed 3 dashboards displaying various ways of
visualizing HealthCare Management data.

11. ECOSYSTEM

12.  Key Performance Indicator displays the total number of
issuers,employes,employers,brokers and enrollments.
 It also displays aggregated calculation of employee
income,premium/month and percentage.
 Service area displays US-statewise information of total
count using image layout widget.
 Enrollment displays heatmap of total enrollment count
corresponding to each US state.
 Employee segmentation displays grid graph display of
number of employes per segments.

13. TICKETING TRENDS

14.  In the Ticketing dashboard,Overall Ticket Workload section
displays information about total count of support persons,open
tickets,average response days and backlog percentage.
 Open Tickets section describes waterfall widget describing total
open counts as per the issuer-type.
 It contains heatmap corresponding to average closure time and
ticket issuertype.
 It contains gauge widgets of closure time in days corresponding to
year,quarter,month and week.
 It also displays microcharts displaying count of current-status
based on issuertype.In microcharts we used sparkline and bar
mode to anaylse in different ways.

15. EXCHANGE-INTERACTIVE DASHBOARD

16.  It is an interactive dashboard.
 Key Performance Indicator displays information about total
service area and enrollment count corresponding to
issuername.
 By using issuername as selector it targets heat map of
enrollment displaying information of total enrollments
corresponding to each state.
 By using issuername as selector it also targets the US map
image layout widget displaying total service area count
corresponding to each state.

17. STOCK ANALYSIS

18.  Here we took the raw real-time stock data of NASDAQ and
NYSE for analysing as per our requirement.
 In the above screenshot there are 4 selectors namely
Sector,Industries,Symbol and Year.
 Industry is filtered by Sector selector and Symbol is filtered by
Sector and Industry respectively.
 All the 4 selectors will filter data to the below panel displaying
stock volatility by year,quarter,month and week.
 Panel describing grid and graph view limiting to 50 data at a time
as shown in below screenshot.

20. CONCLUSION
 MicroStrategy abstracted the SAP HANA data schema, along with
other data warehouses and multi-dimensional sources, into one
unified system of record, hiding the underlying complexity from
end users.
 It made the retrival time of data faster and boosted our agile
business analytics for a Healthcare and Stock domain software.