This document discusses trends in data usage and analytics. It describes the different types of analytics from descriptive to prescriptive and how advanced analytics can help answer "why" and predict outcomes. The rest of the document provides examples of Azure services that can be used for data processing, analytics, and machine learning to help businesses solve common problems like optimizing field agents, predicting demand, and getting personalized recommendations.

2. 1 zetabyte

3. 2013 2014 2015 2016 2017 20192018
20
15
10
5
PC, tablets, Phones
Connected devices usage
1 billion
0

5. Advanced Analytics
The broad industry term
VALUE
DIFFICULTY
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
What
happened?
Why did
it happen?
Traditional BI Advanced Analytics

9. Fully managed service to support orchestration of data movement
and processing
Connect to relational or non-relational data that is on-premises or in
the cloud
Single pane of glass to monitor and manage data processing
pipelines.
Publish to Power BI
Compose and orchestrate data services at scale
C#
MapReduce
Trusted data
BI & analytics
Hive
Pig
Stored Procedures
Azure Machine Learning

10. Enable enterprise-wide self-service data source registration and discovery
A metadata repository that allow users to register, enrich,
understand, discover, and consume data sources
Delivers differentiated value though
‒ Data source discovery; rather than data discovery
‒ Support for data from any source; Structured and
unstructured, on premises and in the cloud
‒ Publishing, discovery and consumption through any tool
‒ Annotation crowdsourcing: empowering any user to
capture and share their knowledge.
This, while allowing IT to maintain control and oversight

12. Process real-time data in Azure using a simple SQL language
Consumes millions of real-time events from Event Hub collected from
devices, sensors, infrastructure, and applications
Performs time-sensitive analysis using SQL-like language against
multiple real-time streams and reference data
Outputs to persistent stores, dashboards or back to devices
Point of
Service Devices
Self Checkout
Stations
Kiosks
Smart
Phones
Slates/
Tablets
PCs/
Laptops
Servers
Digital
Signs
Diagnostic
EquipmentRemote Medical
Monitors
Logic
Controllers
Specialized
DevicesThin
Clients
Handhelds
Security
POS
Terminals
Automation
Devices
Vending
Machines
Kinect
ATM

13. EXAMPLE SOLUTIONS

14. How can I optimize the
productivity of my field
agents?
How can I predict the loads
on my energy grids so I can
balance?
How can I heat and cool
my buildings based on usage
and weather?
How can I fix equipment
proactively before it fails?
How can I stock my trucks
appropriately for the day
and week?
What demographic is my
most valuable?
What customer needs a
salesperson to call on them?
What product(s) is this
customer most likely to
buy next?
What fraudulent behavior
requires follow up?
Which payments need to
go to a human collector
to pursue?
What customers are likely
to leave and why?
What online customers
are most likely to visit
the dealership?
Who are my most valuable
customers and what are
they likely to buy?
What marketing or sales
motion leads to the most
revenue?
Business problems we’re solving now

20. Power BI dashboards and KPIs for
monitoring the health of your business
New data visualizations and touch-
optimized exploration in HTML5
Power BI mobile apps across devices
including iPad and iPhone
Support for new data sources including
SalesForce.com, Dynamics CRM online
and SQL Server Analysis Services
Dashboard
Tree Map

21. ML Algorithms are best of breed and embrace OSS
• MS + R + Python + BYOA
ML Studio for productive development
• Faster experiments results in faster improvements
• Visual Workflows & ML Experiments
ML Operationalization to remove deployment friction
• Build entire ML Apps & Deploy as Cloud APIs
ML Gallery
• Provide ML applications like apps in an ‘app store’
• Publish/consume APIs in a 2 sided market
Help organizations eliminate undifferentiated heavy lifting
Powerful predictive analytics in Azure
Azure Machine Learning

22. From zero to finished, analytical apps and scenarios
Pre-Configured
Allows customers to accelerates the process of building analytical apps
Go from zero to sample app in minutes, from sample app to finished solution in a week

28. Learn More on the Main Page
Microsoft.com/cortanaanalytics
Try CA services today
Microsoft.com/cortanaanalytics
Links on “What’s Included” page
Find (or become) a trained partner
cortanaanalyticspartners.microsoft.com
Stay tuned to our blog
http://blogs.technet.com/b/machinelearning/
How do you get started?
microsoft.com/cortanaanalytics