Building Performant Dossiers:Tips and Best PracticesAlejandro OlveraTechnology

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Factors that could contribute to such differences include: the Company’s ability to meet product development goals while aligning costs with anticipated revenues; the Company’s ability to develop, market, and deliver on a timely and cost-effective basis new or enhanced offerings that respond to technological change or new customer requirements; the extent and timing of market acceptance of the Company’s new offerings; continued acceptance of the Company’s other products in the marketplace; the timing of significant orders; competitive factors; general economic conditions; and other risks detailed in the Company’sForm 10-Q for the three months ended September 30, 2018 and other periodic reports filed with the Securities and Exchange Commission. By making these forward-looking statements, the Company undertakes no obligation to update these statements for revisions or changes after the date of this presentation.

Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved.

| Analytics and Mobility M icroStrategy C onfidentia l. C opyright © 2019 M icroS trategy Incorporated. A ll R ights R eserved.

Agenda • Who’s responsible for performance?• Learning the Dossier Execution

Workflow• Potential bottlenecks• Strategies to achieve good

performance

ENTERPRISE ASSETS(150+ D R IVER S + G ATEW AYS)

TECHNOLOGYOPPORTUNITIES

C loud C om puting

M achine Learning

M obile C om puting

B lockchain

B ig D ata

Internet of Things

D igita l Identity

MARKETDISRUPTORS

Apple

Am azon

G oogle

Facebook

A libaba

Tw itter

M icrosoft

REGULATORYREQUIREMENTS

D ata Privacy and Security

Advertis ing and M arketing

C orporate G overnance

Em ploym ent and Labor

F inancia l

Environm ental

H ealth and Safety

COMPETITIVEFORCES

N ew Entrants

Supplier Bargain ing Pow er

Buyer Bargain ing Pow er

Threat of Substitute P roducts

Intensity of R ivalry am ong C om petitors

INTELLIGENCE CENTER

SQL

DATAFederated, certified collections of data published to enterprise

architects and departm ental analysts, data scientists, and developers.Dictionaries Lineage Views Cubes Marts

SCHEMAA sem antic graph contain ing reusable objects, captured in business term s, that

m ap to enterprise assets, and abstract com plexities of the underly ing data.

Models Attributes Metrics Templates Filters Sets Visualizations Prompts Forms

DRIVERS AND GATEWAYSO ut-of-the-box gatew ays and drivers that m ake it easy to connect

to a lm ost any enterprise inform ation resource.

Relational OLAP Big Data EMM PACS Logical Application

APPLICATIONC ontainers of actionable inte lligence, packaged and published

to the enterprise and departm ents.

Dossiers Dashboards Documents Distributions Custom Apps Web Services Data Services Cards

Sales Marketing Customer Service Manufacturing Field Service Finance HR IT VendorsFacilities Customers

BUSINESS USERSE xplore and interact w ith published analytics. E nhance applications using

self-service data d iscovery to create custom groups, derived m etrics, and dynam ic filters. Foster adoption through collaboration and sharing.

EXECUTIVESThe B usiness E xecutive sets the analytics and m obility strategy for the

function. They establish the function’s priorities, program s, budget, and plan, w hilst m ainta in ing justification for the p latform assets and resources by tracking and publishing adoption, im pact, and ultim ately return-on-investm ent.

ANALYSTSC reate, share, and m ainta in inte lligence applications for the

departm ent using enterprise security, data, and application objects to help ensure a s ingle version of the truth.

DATA SCIENTISTSB uild and publish advanced statistics, predictive m odels, and m achine

learning algorithm s using libraries such as TensorFlow , R , P ython, and M A TLA B , that are leveraged by A nalysts and D evelopers.

DEVELOPERSIn ject, extend, and em bed inte lligence into custom and th ird-party applications using

program m ing languages such as JavaS cript, Java, P H P , P ython, S W IFT, O bjective-C , C #, .N et, and other com m on languages.

DEVICES

FUNCTIONS

USERS Management Sales Reps Workforce Vendors Influencers VIP Clientele Customers CitizensExecutives

Enterprise Analytics

Enterprise Reporting Big Data Data Discovery

Embedded Analytics

Enterprise Mobility

Mobile Analytics

Mobile Productivity External Apps

Mobile Identity, Security, and Communications

Mobile Telemetry and IoTAPPLICATIONS

Wall TV Desktop Web Tablet Phone Watch Voice

TOOLS Email Google Search Excel PowerPoint Power BI Qlik Tableau Alteryx Paxata Trifacta Datawatch Jupyter Matlab R Studio SAS SPSS Eclipse Python IDE Visual Studio xCode

ROLES

ApplicationsP ublish an application fram ew ork and best practices that enable a ll functions to build consistently im pactfu l applications.

E stablish a foundation of shared com ponents to speed departm ental application developm ent.

PR O G R A M S

MobilityE stablish a fram ew ork, d iscip line, and architecture so A nalysts and D evelopers can build and deploy m obile applications.

E stablish processes, protocols, and program s so the Inte lligent E nterprise can consum e apps on m obile devices.

IntelligenceIn ject artific ia l in te lligence, m achine learning, deep learning, and predictive analytics a lgorithm s into enterprise applications

and federated datasets. P ublish and curate a library of m odels for A nalysts and departm ental D ata S cientists.

ServicesC onvert datasets and application com ponents into published services for D evelopers to in ject in te lligence into their custom

applications. P ublish sam ples and docum entation and em pow er D evelopers to use their preferred program m ing tools

and languages.

DepartmentalE m pow er departm ents to rapid ly build applications on federated trusted data using M icroS trategy or other tools (i.e.

Tableau, P ow er B I, E xcel). O rchestrate collaboration w ith the inte lligence center so datasets are continually appraised,

optim ized, and updated.

Enterprise

H arness the pow er of your other B I investm ents and

extend the value to a ll constituents on various devices. M igrate legacy S A P B usinessO bjects and IB M C ognos

applications onto a m odern platform .

DEVELOPMENT

PlatformsA rchitect, insta ll, configure, and deploy the Inte lligent E nterprise architecture. D esign the optim um

architecture to deliver security, stability , scalability , and econom y by com bining platform capabilities w ith

on-prem ises, c loud, and/or hybrid services.

AnalyticsD esign the optim al federated enterprise data layer and publish it to A nalysts, D ata S cientists, D evelopers, and

A rchitects. W ork w ith D epartm ents to evolve the data architecture to m eet changing business needs.

AdministrationM onitor, support, and m ainta in the Inte lligent E nterprise architecture to facilita te ongoing security, stability , and

econom y. M onitor system use, autom ate tasks, and im plem ent upgrades to help ensure an optim al, re liable, and m odern user

experience.

IdentityD esign and publish a d ig ita l identity architecture that enables enterprise-w ide m ulti-factor geo-specific

d ig ita l authentication for internal and external users. Im plem ent the dig ita l identity architecture and

gatew ays on top of a ll logical and physical assets.

Database C onfigure the inte lligence platform to optim ize perform ance against various database technologies (i.e.

O racle, S Q L, S now flake, H D FS ), including re lational, O LA P , b ig data, unstructured, vector, and stream ing

platform s. Track throughput and perform ance, and provide architecture design and optim ization

recom m endations to the database adm inistrator.

SystemsIntegrate data from enterprise system s (i.e. E R P , C R M , M R P , H R ) and blend w ith other data sources to build

custom analytics and m obility applications. D esign, im plem ent, and optim ize an integrated architecture to

overcom e the reporting lim itations and extend the capabilities of enterprise system s of record.

FOUNDATION

A R C H ITEC TU R E PER SO N A S

Intelligence DirectorC reate Inte lligence environm ents by deploying the Inte lligence A rchitecture, supervis ing the Inte lligence C enter and running

Inte lligence P rogram s to support enterprise and departm ental analytics and m obility applications for a ll constituents.

Application ArchitectC reate, share, and m ainta in inte lligence applications for the enterprise.

P ublish standardized application objects, and prom ote departm ental applications from

self-service into the enterprise environm ent.

Analytics ArchitectC reate, publish, and optim ize a federated data layer as the enterprise’s s ingle version of the

truth. B uild and m ainta in the schem a objects and abstraction layer on top of various, changing

enterprise assets.

Mobile ArchitectB uild, com pile, deploy, and m ainta in m obile environm ents and applications. O ptim ize the

user experience w hen accessing applications v ia m obile devices. Integrate w ith preferred V P N ,

S S O , and E M M protocols.

Identity ArchitectB uild, com pile, deploy, and m ainta in d ig ita l identity applications, integrated w ith

enterprise d irectories. D ig ita lly secure a ll existing and new logical and physical assets.

Integrate authentication, com m unication, and te lem etry into other applications.

Services ArchitectIn ject, extend, and em bed analytics into porta ls, th ird-party, m obile, and w hite-labeled

applications. P ublish w eb services and data services for use by D evelopers in build ing

departm ental applications.

Database ArchitectD esign and m ainta in database enterprise assets. O ptim ize database perform ance and utilization

based on query type, usage patterns, and application design requirem ents.

Platform AdministratorInsta ll and configure the Inte lligence A rchitecture on-prem ises and/or in the c loud. M ainta in the

security layer, m onitor system usage, and optim ize architecture in order to reduce errors,

m axim ize uptim e, and boost perform ance.

System AdministratorS et up, m ainta in, m onitor, and continuously support the infrastructure environm ent

through deploym ent on A W S , W indow s, or L inux, a ll w hile optim izing perform ance and

contro lling costs.

D EPLO YM EN T

D evelopm ent U ser A cceptance Testing

(U A T)

D epartm ental

E nterprise

O n - p r e m is e s , c lo u d , o r h y b r id

E n v i r o n m e n ts d e p lo y e d in m in u te s

V e r t ic a l a n d h o r iz o n ta l s c a l in g

M u l t i - n o d e c lu s te r in g

M u l t i - r e g io n s u p p o r t

H o t , w a r m , a n d c o ld fa i lo v e r

R e l ia b le d r a g - a n d - d r o p p r o m o t io n

In te g r i ty te s t in g o f a p p s a n d u p g r a d e s

3 6 0 d e g r e e m o n i to r in g b u i l t in

Amazon Web Services On-Premises Microsoft Azure

Alibaba Cloud CenturyLink Google Cloud IBM Cloud Oracle Cloud Rackspace

Docker Kubernetes vmware

Linux Windows

An Intelligence Platform helps make every device, application, and person more intelligent.

It creates an enterprise semantic graph that connects, indexes, abstracts, and federates your organization’s data, telemetry, and usage. Users can rapidly build contextual applications and deploy them anywhere, anytime, and on any standard device—delivering trusted answers to constituents based on who they are, where they are, and what they need.

M E T A D A T A

S E R V IC E S

APPLICATION SERVICESA suite of enterprise-caliber add-on services available for

architects to quickly and easily integrate into any application.

Intelligence Analytics Transaction Distribution Telemetry Identity Collaboration Geospatial Language

xArchitect Desktop Web Reporter Mobile Communicator Badge Hyper ApplicationCLIENT PRODUCTSC lients that enable intu itive, fast, and enjoyable analytics and m obility

experiences across w eb, desktop, and m obile interfaces.

IN TELLIG EN C E

PLA TFO R M

SECURITYC apabilities that enable the developm ent of personalized and secured

applications incorporating m ulti-factor, m ulti-layer authentication.

Library Users Groups Badges Privileges Permissions Roles Security Filters

PLATFORM SERVICESC ritical capabilities to deploy analytics and m obility applications w ith h igh

perform ance and scalability on top of enterprise assets.

Optimized Multi-Source Connectivity

Parallel Processing

Multi-node Server Cluster

Multi-Level Caching

Dynamic Sourcing

Platform Analytics Multi-Tenancy Usage

TelemetryCompute Elasticity

H IER A R C H Y O F N EED S

PopularityS u r fa c e in te g r a te d c o l la b o r a t io n a n d s h a r in g c a p a b i l i t ie s w i th in a p p l ic a t io n s th a t

p r o m o te v i r a l a d o p t io n a n d u s a g e b y d e s ig n .

EconomyM a n a g e m u l t ip le e n te r p r is e e n v i r o n m e n ts u s in g a s in g le p o w e r fu l w o r k s ta t io n to o l

fo r o n e - ta p d e p lo y m e n t , ta s k a u to m a t io n , a r c h i te c tu r e m a n a g e m e n t ,

a n d s y s te m m o n i to r in g .

SimplicityD e l iv e r u s e r e x p e r ie n c e s , l ik e v o ic e , c h a tb o ts , a n d n a tu r a l la n g u a g e , th a t fe e l

fa m i l ia r a n d e f fo r t le s s e v e n u p o n f i r s t u s e , s u p p o r te d w i th to o ls th a t r e q u i r e

z e r o t r a in in g .

ScalabilityS h a r e s o p h is t ic a te d p e r s o n a l iz e d a p p l ic a t io n s , b u i l t o n b i l l io n s o f r o w s o f d a ta ,

w i th 1 0 0 ,0 0 0 s u s e r s w h i le m a in ta in in g s u b - s e c o n d r e s p o n s e s .

StabilityD e p lo y a p p l ic a t io n s o n a r o b u s t a r c h i te c tu r e w i th h o t , w a r m , a n d c o ld fa i lo v e r

s t r a te g ie s , c lu s te r in g , a n d r e l ia b le g o v e r n o r s c o m b in in g to a v e r t s y s te m c r a s h e s

u n d e r p e a k u s a g e a n d d a ta lo a d s .

SecurityF a c i l i ta te s e a m le s s m a n a g e d a c c e s s to e n te r p r is e a s s e ts , c o n t r o l le d v ia a

g r a n u la r s e c u r i ty m o d e l , v ia c o n v e n ie n t m u l t i - fa c to r d ig i ta l c r e d e n t ia ls .

FunctionalityE n a b le B u s in e s s U s e r s , A n a ly s ts , a n d D e v e lo p e r s to p e r fo r m a l l ta s k s ( b u i ld ,

d e p lo y , a n a ly z e , s h a r e ) a c r o s s v a r io u s d e v ic e s ( d e s k to p , w e b , m o b i le , v o ic e )

w i th o u t r e s t r ic t io n o r l im i ta t io n .

MDX HADOOP NOSQL APPLICATIONS

The Intelligence Center

Aim at a 5s response time, keeping a maximum of 10s (end-to-end)

Every role of the Intelligent Enterprise matters

Building performant dossiers

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Developing a mindset is the key!

• Systems Administrator• Platform Administrator• Analytics Architect• Applications Architect• Developers, Analysts, Business Users

• Thinking of performance implications with every dossier design decision• Intelligence Center working together with users to improve the overall system

Systems and Platform Administrators

7

Enable performant (network) access to enterprise datasets

Ensure enough Server resources to leverage In-Memory capabilities, including Caching

Manage environments for enterprise and departmental applications

• Enterprise environment requiring 99% uptime and meeting all

company performance Service Level Agreements (SLAs)

• Departmental environment with own governance allowing ad-hoc

reporting & self-service

ü Use job prioritization mechanisms already in

place to isolate users, including User Fencing and Workload fencing on clustered

environments

Applications Architect

8

Enterprise Applications standards and

guidelines

Performance tuning and troubleshooting

• Criteria for application datasets

• Application caching strategy

Work with business users, analysts,

developers, data scientists

Understanding the dossier execution

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Populate Filters

Visualizations

Datasets Execution• Reports and View Reports• In Memory Cubes or Cached Reports• Live Connection

Dossier Schema

• Joins and Global Lookup Tables• Calculate Derived Attributes and Groups

• Get Visualization data• Visualization level calculations (e.g. DM’s)

• Rendering

Running Datasets

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Datasets that require time to query data

• Reports run SQL against the Warehouse (unless already cached).

• View Reports run CSI against a Cube (unless cached – new!)

Datasets that do not consume time at this stage

• In Memory Cubes (already loaded / published)

• Reports that were pre-cached• Live Connection datasets

Dossier Schema

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The layer that organizes the data available to the dossier globally

• Deals with Attribute linking (data blending across datasets)• Attribute relationships• Creation of Lookup Tables• Derived Attributes• Groups

Attribute Element List Filters

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Avoiding heavy attributes on Filter styles that get populated with a list of elements

Running visualizations

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What goes into executing each visualization?

Getting the data• In-Memory datasets: Data is subset from the

Cube (CSI Engine)

• Live datasets: Data is queried directly from the external source (SQL Engine)

Performing In-Memory calculations• Calculating Derived Metrics

• With Live datasets, Derived Metrics are pushed to the external data source. Some Derived Attributes calculations too.

JSON Generation + Transfer through Network

In-Memory vs Live Connection

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What goes into executing each visualization?

Rendering visualizations

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Rendering time depends on number of data points and your machine resources

• Grids have incremental fetching, so only the first 100 rows render

• However, other visualizations may attempt to render every data point

Strategies to achieve good performance

Dataset Caching & Dossier Caching (Cache Subscription)

Narrowing down the data for the dossier

Blending your Data the right way

Using In-Memory calculations wisely and remove unused objects

Limiting the amount of individual data points rendered in each visualization

Dataset Caching

Dataset Caching for Reports and View Reports (new!)

Dossier Caching

Caching all the dossier data and definition

Caching in Library

Configurable from Workstation (highly personalized!)

Caching Subscriptions

Schedule a recurrent cache update subscription to speed up first response time

Narrowing down the data

Dataset-level filters vs dossier-level filters

Filtering data at the individual visualization level

If using Reports, having a dataset-level Report Filter limits the data volume from the start

Using Prompts

Narrowing down the data

Dataset-level filters

• Report Filters

• Filters in View Report

Dossier-level filters

• Filter Panel

• Advanced Filter

• Filters at the Metric level (or Conditional Metrics)

Narrowing down the data with Prompts

Prompting Reports to limit the data they must fetch from the source

Using Data Blending the right way

Splitting your data in several datasets vs single consolidated dataset

Multiple joins across datasets and their performance implications

Data Blending and Global Lookup tables. How does this matter?

Controlling join behavior across datasets

Multiple Datasets vs a Single Consolidated Dataset

Consider implications of blending data from multiple datasets

OLAP vs Multi-Table Data Import Cubes

Two types of Cubes:

• OLAP: Governed project schema, typically created by BI department. Data is denormalized.

• MTDI Cubes: Sometimes created by business users. Mini-schema in-memory. Should be certified by Admin so they are governed (that way this data can be federated to others). Data can be normalized.

Using Data Blending the right way

One OLAP Cube (denormalized data) vs many smaller datasets

Using Data Blending the right way

Blending through a Multi-Table Data Import Cube (aka Super Cube)

Data blending between multiple datasets

Data Combination settings

• Dossier-level lookup tables for linked attributes: this allows populating full sets of distinct elements present in all datasets.

Load Chapters on Demand

• Run visualizations only of the currently viewed Chapter. Switching Chapters may be slower.

OLAP vs MTDI

OLAP Cubes vs Multi-Table Data Import Cubes

Multi-Table Data Import Cubes

What can impact performance? (Hint: joins)

• One-to-many relationships. Correctly set relationships to avoiding more expensive joins.

• Number of tables joined in query. Retrieving metric data from many tables in the MTDI cube.

• Security filters. CSI/SQL engine may “auto-join” table with user-requested data with necessary tables that contain secured attributes.

• Complex filters that will result in multiple passes and joins; especially if based on multiple attributes not present in the same table.

• Many-to-Many relationships usually unnecessary, so avoid defining them in the MTDI cube.

Cube Partitioning

Leverage parallel aggregation on multiple CPU’s by enabling partitioning

Data Blending on multiple datasets

Join Settings controlled via each dataset context menu

In-Memory calculations vs pre-calculated Columns

Derived Attributes and Groups are useful for ad-hoc reporting and self-service, but how do they scale?

How to make a dossier more scalable and better performing by moving these calculations to an In-Memory dataset, so they don’t have to calculate at the dossier execution time.

Performance considerations of Derived Attributes vs Data Blending (when they get calculated against a Global Lookup)

Using data wrangling instead of Derived Attributes, and why this may perform better.

Considerations with Derived Metrics (mention of “Use Lookup for Attributes” setting)

In-Memory calculations vs pre-calculated Columns

Using Data Wrangling instead of Derived Attributes

Visualization rendering time

Incremental fetch on Grid, but not in other visualizations

Performance considerations with Maps. Using clustering.

Performance considerations of Bar Charts, Scatter plot, Heatmap and other visualizations. Use filters to limit the volume of data points that the visualization will render.

Using Image (png or jpg) thresholds

In a nutshell

Watch for joins! Especially between unrelated attributes. Prefer inner joins.

Prefer In Memory cubes or cached Reports as datasets. Consider cache subscriptions.

Clean up your dossier. Remove unused objects and datasets.

One big dataset generally better than multiple smaller ones.

Use Derived Attributes, Groups and Derived Metrics only if necessary. Prefer having these calculations built on the dataset (pushed to DB, or done when publishing the Cube).

Try to avoid complex functions. Watch for “Smart Metrics”.

The more tables have to be accessed, the longer it takes to run.

Tips for Troubleshooting

Simple “binary search” on on Chapters, Pages, Visualizations, Derived Calculations, Filters

and Datasets

Capacity Planning Tool (Administrators)

Using Chrome’s Developer Tools to break down the response time (time on Server side vs

time on client/browser used for rendering)

Intelligent Server logs that can be used to further break down the response time.

Turning on Performance Statistics on MicroStrategy Web

• Datasets execution time• Time spent on calculations of Derived Attributes, Groups• Time spent on each individual visualization, including Derived Metrics calculations and other.• MCE Trace for Data Blending

Platform Analytics and other Tips for Troubleshooting

Platform Analytics

Captures telemetry from the MicroStrategy Platform in real-time and makes it available to administrators, developers and analysts to help them optimize performance

MicroStrategy Consulting

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Application Performance Tuning Advisory

Best practice guidance to ensure your application performs seamlessly.

MicroStrategy.com/Services

Visit microstrategy.com/request-benefits to explore consulting services custom-built to help you become a more Intelligent Enterprise—and available at no cost to you.

Enterprise Support ProgramBecause we are vested in your success

Reinvesting in you.

Q + A

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