This document provides an overview of the Microsoft Power BI platform for data analytics. It describes how Power BI can bring together data from various sources like SQL Server, Excel, and SaaS applications. It then discusses how Power BI allows users to prepare, explore, and share reports and dashboards. The document also outlines some of the key capabilities of Power BI like live dashboards, custom visualizations, and content packs for reporting.

1. Your Data Analytics Platform

3. Data Sources Power BI service
SaaS solutions
e.g. Marketo, Salesforce, GitHub
On-premises data
e.g. SQL Server Analysis Services
Organizational content
Corporate data sources or external
data services
Azure services
Azure SQL, Stream Analytics…
Excel files
Workbook data/models
Power BI Designer files
Data from files, databases, Azure, and
other sources
146.03K145.84K145.96K146.06K 40.08K38.84K39.99K40.33K
146.03K145.84K145.96K146.06K
Bring data together and use it in new ways with Power BI
Power BI REST APIsPower BI Pro
Prepare Explore ShareReport
Data refresh
Visualizations
Live dashboards
Content packs
Reports
Datasets01001
10101

4. With Power BI on the web,
monitor your important
data from across your
organization and from all
of the apps you rely on.
Power BI Desktop gives
you tools to transform,
analyze, and visualize data.
Share reports in seconds
with your organization
using Power BI on the web.
With SQL Server Analysis
Services on-premises and
Azure Analysis Services in the
cloud, you can easily build
robust, reusable data models.
Publish Power BI reports to
SQL Server Reporting
Services.
Deliver stunning
interactive reports in
your app with the
Power BI Embedded
service.
Analytics solutions for your whole organization

5. Cloud
Unstructured /
Semi-structured
Azure SQL
Azure Analysis Services
Azure Data Lake
Azure Data Factory
Azure Machine Learning
Batch
On-
Premise
Data Sources Manage Consume
Non-MSFT
Apps &
Data sources
Non-MSFT
Apps &
Data sources

6. Social
LOB
Graph
IoT
Image
CRM
INGEST STORE PREP & TRAIN MODEL & SERVE
Data orchestration
and monitoring
Big data store Analytics engines Data warehouse
Big Data & Data warehouse
Azure Data Factory Azure Blob storage
Azure Data Lake Store
Azure Databricks
Azure HDInsight
Azure Data Lake
Analytics
Azure SQL Data
Warehouse
Azure Analysis Services
Advanced Analytics
Real Time Analytics
BI + Reporting

7. Modernized: Data  Intelligence  Action
Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Intelligence
Future Phases
Dashboards &
Visualizations
Cortana
Bot
Framework
Cognitive Services
Power BI
Information
Management
Event Hubs
Data Catalog
Data Factory
Machine Learning
and Analytics
HDInsight
(Hadoop and
Spark)
Stream Analytics
Intelligence
Data Lake
Analytics
Machine Learning
Big Data Stores
SQL Data
Warehouse
Data Lake Store
Data
Sources
Apps
Sensors
and
devices
Data

8. Analytical Maturity Curve
WHAT
HAPPENED?
WHY DID IT
HAPPEN?
WHAT MAY
HAPPEN?
WHAT IS HAPPENING? WHAT ACTION SHOULD
WE TAKE?
OPERATIONAL
MANAGEMENT
DATA
SCIENCES
•Batch
•Limited Adhoc
•Single Version of Truth
•Large Adhoc
•Scorecarding
•Foundational Analytics
•Closed Loop Forecasting
•Temporal Analysis
•Continuous Update
•Rule-based Alerting
•Event-based
•Data Discovery
DATA SOPHISTICATION
WORKLOADCOMPLEXITY
Basic
Reporting
Self-Service
Reporting
Exploratory
Analytics
Real-time Analytics
Real-time Predictive
Modeling & Mining
Descriptive
Diagnostic
Predictive
Prescriptive
Technical Capabilities
Should Facilitate
Movement Along the
Maturity Curve
DECISIONSUPPORT

9. Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study
Provides Businesses with
an average of
145% Return on
Investment
Business
Intelligence
+ Predictive
Modeling
Provides Businesses with
an average of
89% Return on
Investment
Business
Intelligence
Alone
• Both predictive and non-predictive projects yielded
high median ROI, 145% and 89%, respectively.
• The major benefits of business analytics projects that
employed predictive analytics centered on business
process enhancement, especially improving the quality
of operational decisions.
• Predictive analytics projects required higher
investment levels and yielded significantly higher
overall returns over five years, implying that these
projects tackled problems of greater scope and
complexity.
• Working with Microsoft will allow you to increase
your ROI by (~56%)

10. S M A R T E R I N C E N T I V E C O M P E N S A T I O N
Faster insights at ¼ cost with SQL Data Warehouse
DATA MANAGEMENT // DATA WAREHOUSING
CHALLENGE
Incentives company needed to consolidate and
analyze employee behavior data at scale to
create customized offerings
IMPACT
75% reduction in storage costs and 70% cut in
time spent on data collection
Maritz rapidly scales up unified data model 2.5x
and scales down to minimize costs
DW DW DW
DW
ETL
Unification of
disparate DWs
Prolonged and faulty
data consolidation

 Elastic scale
 Managed environment
 Fast provisioning
Azure SQL DWBefore

11. M A N U FA C T U R E R R E S P O N D S T O C U S T O M E R S FA S T E R
Insights in real-time at a fraction of the cost with Power BI
DATA MANAGEMENT // BUSINESS INTELLIGENCE
CHALLENGE
Engine part manufacturer struggled to forecast
sales and customize marketing due to time-
consuming and onerous BI reporting
IMPACT
Complete BI solution at 40% less than the
competition delivers savings
Elimination of manual reporting saves
employees up to 65 hours a month
Automated reporting

Real-time
Self-service analytics

65 hours per month
Reporting with Power BIBefore

12. E X T E N D I N G P O W E R B I C A PA B I L I T I E S
T O O N - P R E M I S E S
DATA MANAGEMENT // BUSINESS INTELLIGENCE
Power BI Premium
Get started quickly
Natural language query
Live dashboards
Custom visualizations
Power BI reports
Power BI Report Server
SSRS reports
Power BI reports
Custom
visualizations

13. P R E D I C T I V E M A I N T E N A N C E T H R O U G H B I G D A T A
Maintenance costs reduced 90% with HDInsight
DATA MANAGEMENT // BIG DATA
CHALLENGE
Industrial automation business outgrew data
capacity and required a full managed Big Data
solution
IMPACT
Full oil and gas pipeline visibility with reduced
maintenance at 90% less cost
Development time cut 80% for shorter time-
to-market, reduced costs, and improved
customer responsiveness
Fixed capacity and
limited insights
Complete data
visibility for less

 Big Data storage
 Azure Data Factory
 AA & machine learning
HD InsightBefore

14. I N T E L L I G E N T W A T E R U T I L I Z A T I O N S A V E S $ 5 . 2 M
Power of Advanced Analytics and AI
DATA INSIGHTS // ADVANCED ANALYTICS & AI
CHALLENGE
Cruise line struggled to accurately predict water
usage onboard ships, leading to costly water
storage or production
IMPACT
Advanced Analytics model optimizes onboard
water storage, saving $200k per ship annually
Historical and real-time data analysis enables
predictive maintenance
Inaccurate water
estimations
Simple
calculations
Reduced storage and
production costs
Diverse data sources
 Advanced Analytics
 Machine learning
 Intelligence APIs

Cortana intelligenceBefore

15. *Gartner “Magic Quadrant for Business Intelligence and Analytics Platforms,” by Rita L. Sallam, Cindi Howson, Carlie J.
Idoine, Thomas W. Oestreich, James Laurence Richardson, and Joao Tapadinhas February 16, 2017
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of
the entire document. The Gartner document is available upon request from Microsoft. Gartner does not endorse any
vendor, product or service depicted in its research publications, and does not advise technology users to select only those
vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's
research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or
implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Microsoft is placed furthest for
Completeness of Vision within the Leaders
Quadrant
{ }