1. How to Quickly and Easily Draw Value
from Big Data Sources
Moacyr Passador
Senior Sales Engineer

2. DevelopersAnalystsConsumers Data Scientists
Business Users IT Users
Types of BI Users
The Old BI World
Today’s BI World
Business Users are getting more involved in producing analytical content
The Role Of Business Users In BI Today Has Greatly Evolved

3. Relational
Databases
MapReduce
& NoSQL
Multi-Dimensional &
Other BI Tools
Cloud
Applications
Departmental
Data
Social Media
Business Users Today Want Direct Access To More Data
To make insightful decisions on their own, business users demand instant access to Data from multiple enterprise sources
&

4. >100x
More content
creation and
consumption
5-10x
More Content
5-10x
More Content Creators
5-10x
More Sharing
More productive
More content per creator
More producers
More users can create content
More collaborative
Peer-to-peer sharing
&
Adoption Of Self-service Analytics By Business Users Increases Productivity

5. Introduction to Big Data

6. What is Big Data, Really?
The Three Vs of Big Data According to Gartner
Volume
• Orders of magnitude bigger than conventional data (Terabytes, Petabytes,
Exabytes)
• Cost-prohibitive or practically impossible to store, manage or analyze in
typical database software
Variety
• Structured, semi-structured, unstructured formats
• Diverse sources - complex event processing, application logs, machine
sensors, social media data
Velocity
• Speed of ingesting incoming data streams
• Processing and real-time analysis of streaming and complex event data
Volume
Variety
Velocity

7. Four broad categories of Big Data sources and their value
Traditional sources
becoming bigger
Company, Government, Financial sector, Business and
consumer studies, Surveys, Polls
All business performance drivers – Operational
efficiency, Revenue management, Strategic planning
SOURCE
VALUE
Digital exhaust
from interactions
Online click-stream, Application logs, Call/service
records, ID scans, Security cameras
New revenue sources, Consumer promotions, Risk
management, Fraud detection
SOURCE
VALUE
Web 2.0
phenomenon
Content generated from social media posts, tweets,
blogs, pictures, videos, ratings
Customer engagement, Customer service, Brand
management, Viral marketing
SOURCE
VALUE
Internet of
things
Machine generated sensor data and “connected
device” communication
Operational efficiency, Cost control, Risk avoidance
SOURCE
VALUE
Business Oriented Use Cases for Big Data
SOURCE
VALUE

8. Data Lakes and MicroStrategy

9. • Manicured, Often Relational
• Known Data Volumes
• Expected Formats
• Little to No Change
DATA SOURCES ETL DATA WAREHOUSE BI & ANALYTICS
• Complex Structures
• Rigid Transformations
• Extensive Monitoring Required
• Transformed Historical to Read Structures
• Flat, Canned Access to Data
• Report Chaos
• Extensive Data Load Delays
• Inflexible with new sources
Traditional Approaches And Current State Of A DWH

10. • Increase in Data types
• Rising Data Volumes
• Pressure on the DWH
• Constant change
DATA SOURCES ETL DATA WAREHOUSE BI & ANALYTICS
• Delay in reacting to new sources
• Monitoring is abandoned
• Transformations cant keep pace
• Repaid, Adjust and Redesign ETL
• Reports become invalid
• Delay in updates
• Users seek silos
• Business is disconnected w/ IT
Challenges With Traditional DWHs With Growing Data Demands

11. A storage repository, usually in Hadoop, that holds a vast amount of raw data in its
native format until it is needed
• Low cost
• Flexible and Loosely governed
• No need to decide up front what data to store or for how long
• Contains a mix of structured, semi-structuredand unstructured data
• Allows for freeform data exploration without having to wait for ETL
“If you think of a datamart as a store of bottled water – cleansed and packaged and
structured for easy consumption – the data lake is a large body of water in a more natural
state. The contents of the data lake stream in from a source to fill the lake, and various
users of the lake can come to examine, dive in, or take samples.” -- James Dixon
What is a Data Lake?

12. Data is an asset
• Today many organizations discard data due to cost of storage even though business
value may be mined from the data in the future
• A Data Lake allows you to store and process data essentially indefinitely
Unification
• Data Discovery, Data Science and Enterprise BI are treated as silos in many
organizations today
• A Data Lake can help unify these concepts and allow cross team collaboration
Utility
• A Data Lake creates the possibility of answering future questions of your data without
knowing the question in advance
Why Have A Data Lake?

13. Building the lake is easy / using it is hard
Governance is still key
• Without a meaningful strategy for maintaining
data quality this strategy can quickly fail
• You can quickly create a Data Swamp (dirty)
or a Data Graveyard (useless)
Sandbox strategy
Beware Of The Swamp
• Segment portions of your lake for experiments, testing and data
that may fall outside of your standard governance process

14. Analytical
Datamarts
Relational
Databases
MDM
ETLand
Governance
Data Warehouse
Structured
Enterprise Data
Operational
Data
Enterprise Metadata
MicroStrategy
Analytics Platform
Enterprise
Applications
Traditional Data Architecture

15. Data Lake
Enterprise Metadata
Prime
ELTwith
Governance
ETL
Relational Data
Enterprise
Applications
Cloud-based data
Web Logs
Flat Files
Analytical
Datamarts
MicroStrategy
Analytics Platform
In Comes The Data Lake

16. ⎸07062016
Build Analytics and Mobility Applications Using Data Stored with Big Data
Hadoop Vendors SQL on Hadoop NoSQL Databases
Elastic Map Reduce
The MicroStrategy platform empowers organizations to build
applications that leverage big data and Hadoop distributions. All of
the major Hadoop distributions are certified to work with
MicroStrategy and once connected, data stored in Hadoop
becomes just like any other data source.
Users can connect using Hive, Pig, or proprietary SQL-on-Hadoop
connectors like Cloudera Impala or IBM BigInsights.
The MicroStrategy big data engine can natively tap into HDFS,
generating schema on-read and making Hadoop suitable for ad-
hoc querying. It also enables parallel loading of data from HDFS,
resulting in high-performance data loading.
MicroStrategy’s native connectivity saves users from the tedious
process of ETL from HDFS to Hive and helps to overcome the
ODBC overhead associated with Hive.
MicroStrategy’s data wrangling capability lets users cleanse and
refine their big data directly in MicroStrategy’s data discovery tool.
Big DataEnterprise assets
Mobility apps
that source information from
multiple locations, and submit
transactions to your ERP
systems
Analytics applications that
blend data from databases and
big data and deliver insights to
users via reports, dashboards,
and apps
16
MicroStrategy Platform

17. ⎸07062016
MicroStrategy allows organizations toeasily accessandanalyzedatain all
shapes andsizes, from a singleplace. Business and IT users caneasily blend
multiple data-sources includingbig data sources in seconds.From personal
spreadsheets to cloudsources,to evenHDFS,big data access is madequick
and easy withnativeHDFS connectors or viaanyflavor of hiveproducts
includingCloudera,Hortonworks, MapR,Spark andmore.
Batch SQL: Fulfill your batch processing needs with certified
Hive/ODBC drivers from different Hadoop distributors: Cloudera, Hortonworks, MapR, and Amazon EMR
Interactive SQL: Leverage advanced SQL on Hadoop technologies for interactive queries such as
Cloudera Impala, MapR Drill, Apache Spark, IBM BigInsights, Pivotal HAWQ, and Facebook Presto
No SQL: Connect, query, and analyze data from No SQL sources such as HBase and Cassandra
Search: Dynamically search on semi-structured and unstructured data with MicroStrategy’s integration
to Apache Solr and Splunk
17
MicroStrategy Analytics Platform
Distributed File Systems (HDFS, Amazon S3, GFS…)
No SQLBatch SQL Interactive SQL Search
Big Data Analytics for Most Common Scenarios
Big data is just another data source

18. Product Demonstration

19. 5 Differentiators for Big Data Analytics
“Leverage MicroStrategy's improved data discovery capabilities, as an alternative to
augmenting your BI portfolio with products such as Tableau and Qlik, to lower cost of
ownership and improve enterprise self-serviceviaa single, broader solution.”
Gartner Research Note – December 11, 2015

20. Enterprise data access with complete data governance
Self-service data exploration and production dashboards
User accessible advanced and predictive analytics
Analysis of semi-structured and unstructured data
Real-time analysis from live updating data
1
2
3
4
5
Five Differentiators for MicroStrategy in Big DataAnalytics
The MicroStrategy Analytics Platform enables every business user to get these capabilities

21. Optimized Access to Your Entire Big Data Ecosystem as If It Were a Single Database
Data Warehouse
Appliances
MapReduce &
NOSQLDatabases
Relational
Databases
Multidimensional
Databases
Columnar
Databases
SaaS-BasedApp
Data
HANA
BigInsights
Parallel Data Warehouse
Elastic Map
Reduce
Analysis Services
Redshift
BringAllRelevant
DatatoDecision
Makers
Distribution
Zendesk
HDFS
Generic Web
Services SOAP REST
User /Departmental
Data
1. Enterprise data access with complete data governance

22. Stunning Visualizations Instant Query Results Effortless Dashboards No IT Needed
Lightning fast insights, easy for everyone
2. Self-service data exploration and production dashboards

23. User / Departmental
Data
Data Warehouse
Appliances
MapReduce
Databases
Relational
Databases
Multidimensional
Databases
Columnar
Databases
SaaS-Based
App Data
MicroStrategy
Multisource Engine
2 & 3
Join data on-the-fly.
No need to move it to a
staging database first.
Access your entire Big Data ecosystem as if it were a single database
Combine Data from Multiple Sources
2
3
1
2
1
2
3

24. MicroStrategy Supports Data Discovery at Scale
Start with Departmental teams and grow exponentially to publish to 1000s of users without fear
24
PUBLISH
Team
Department
Enterprise
Value Chain
10s 100s 1,000s 10,000+
MicroStrategy Desktop
Proven Scalability
Built-in clustering, failover, and
comprehensive administrative tools
for performance optimization
In-Memory Performance
Tested sub-second response
times on web and mobile, even
at highest user volume
Advanced Monitoring
Admin tools to automate, report
and alert on system utilization
Content Personalization
Users only see relevant data, and
only access functionality they are
authorized to use

25. Extend MicroStrategy’s Sophisticated Analytical Capabilities with 3rd party statistics toolkits
25
Industry’s most powerful SQLEngine and 300+ native analytical functions
Projections
RelationshipAnalysis
Benchmarking
TrendAnalysis
Data Summarization
AnalyticalMaturity
What is likely to happen based on past history?
What factors influence activity or behavior?
How are we doing versus comparables?
What direction are we headed in?
What is happening in the aggregate?
Optimization What do we want to happen?
World’s most popular
advancedanalytics tool.
Free, opensource.
More
Specialty Tools
3. Business User Friendly Predictive and Advanced Analytics

26. Streaming AnalyticsInteractive Search Text Analytics
Quickly investigate:
• Website logs
• Application usage
• Surveys and free form text
fields
• Event and error monitoring
logs
80% of data in most businesses is unstructured and this proportion will keep on rising
4. Analysis of semi-structured and unstructured data
Find keyword and event occurrences
in any data
Apply semantic and syntactic
models to text data
Assess rapidly changing data
streams
Extract relevant information to:
• Optimize search engine
marketing
• Understand sentiment on topics
• Get a 360 degree view of
customers
• Detect fraud
Analyze an array of data from:
• Sensors and devices
• Images, audio, and video
• Email and document
management systems
• Other operational and
transactional data

27. New live data update technology
5. Real-time analysis from live updating data
2
7

28. ⎸07062016
Native HDFS
Connector
Native Big Data
Wrangling
Support for search
sources
In-memory parallel
architecture
MicroStrategy
Tableau
Qlik
Power BI
IBM Cognos
SAP BOBJ
Oracle OBIEE
MicroStrategy enables organizations to quickly harness the value of big data by deploying analytics at scale
Big Data Analytics: Product Differentiators
28

29. Product Demonstration

30. Powerful data preparation for more accurate analysis
Empowering data analysts to deliver deeper insights with intuitive and integrated data wrangling capabilities
Data integration in the hands of every user
Access and combine data from multiple sources “on-the-fly” to drive more productivity
MicroStrategy Prime – Analyze more data in memory
In-memory engine tightly coupled to the underlying DB
MicroStrategy Multi-Source
Effectively navigate data across multiple data sources
MicroStrategy Enabling Technologies for Big Data

31. Empowering data analysts to deliver deeper insights with intuitive and integrated data wrangling capabilities
31
Streamlined workflows
to parse and preparedata
Hundreds of inbuilt functions
to profile andcleandata
Multi-Table in-memory support
from different sources
Automatically parse and prepare
data with every refresh
Create custom groups
On the fly andwithout coding
Local /URL Files
Hadoop
Data Preparation
NewIntegratedDataPreparationCapability
Salesforce
Twitter/Facebook
Powerful Data Preparation For More Accurate Analysis

32. Source 1
Source 2
Source 3
Source 4
Public Data
DataBlending
Data Access
Live Connection
In-memory Data
OR
Other BI Tools
SaaS Data
Native HDFS
Access and combine data from multiple sources “on-the-fly” to drive more productivity
Data Integration In The Hands Of Every User

33. Data Upload
4x Faster
Server
With MicroStrategy 9.x
Serial access to in-memory data
Database
OLAP Cube
With MicroStrategy 10
Multi-threadedaccess to in-memory data
Database
PRIME
Server
2B 2B | 2B . . . 2B
Data Volumes
80x Larger
………Core 1 Core 2 Core 16
CPU
………Core 1 Core 2 Core 16
CPU
Data Interactions
50% faster
Bottleneck
…… Up to 8 parallelthreads
In-memory engine tightly coupled to the underlying DB
MicroStrategy Prime – Analyze More Data In Memory

34. Seamlessly traverse multiple DBs
End user agnostic
Effectively use aggregates
Automatic navigation
Works with different source types
Move from Hadoop toRelational
Metadata driven
No need towrite SQL
Effectively Navigate Data Across Multiple Data Sources
MicroStrategy Multi-Source

35. Data lakes are a powerful tool
MicroStrategy is ready to support your hybrid architecture
Meet the needs of multiple user personas
Enterprise BI capabilities combined with ad-hoc data discovery
A scalable solution for all workloads
Highly scalable in memory engine to combine data from multiple sources
MicroStrategy Multi-Source
Effectively navigate data across multiple data sources
Summary

36. Questions

37. Thank you
Moa Passador
mpassador@microstrategy.com