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© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Presented by
Bridge IT and OT with a
process data warehouse
Franco Camba, OSIsoft
Matt Ziegler, OSIsoft
Frank Ruland, SAP
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Audience Poll
How many of you are running or considering a Big Data Project?
Have you invested or are you looking into Business Intelligence tools ?
What do you believe is the hardest part when it comes to use operational data within IT Systems?
a) Data Preparation
b) Lack of context/metadata
c) Business Case not well defined
d) Performance
e) Ease of access of Operational Data
Of these tools/technologies, which are the ones you are looking to integrate Operational data into?
a) PowerBI
b) Qlik
c) Tableau
d) Tibco Spotfire
e) SAP
f) Hadoop
g) Other
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
What you will hear during this talk:
4
– Why IT-OT convergence?
– PI Integrator for Business Analytics: Product details
– SAP HANA IoT Integrator by OSIsoft – Frank Ruland
– Streaming pattern
– Roadmap
– Q&A
Empowering Business in Real-Time. © Copyright 2016 OSIsoft, LLC
OT IT
StrategyArchitecture
SecurityGovernanceHardware
ERP PLM
Esri
SCADA MES
HMI
Use
r In
terf
aces
User In
terfaces
OperationalInsights
Business Insights
ERP
Big Data
Unstr. DataD
ata
/ A
sset
Data / A
sset
SCADAPLC
LIMS
PI Data Arch.
Typical information landscape.
OT – Operation Technology
IT – Information Technology
The Convergence of Information and Operational Technology
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Problem Complexity Drives the Need for Integration
Real-time visibility
6
Monitoring
Process Optimization
Benchmarking
System Optimization
Disparate assets or interacting one-by-one Interacting with common assets as a fleet
Real-time & historical view across any plant
asset
Fleet-wide performance comparison
Large scale multi-variate analysis
Co
mp
lexi
ty
Business Intelligence
Big Data Analytics
Machine Learning
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
How can I do this?
7
Predict Outages
Estimate RUL
Material Management
Compare asset
performance
Production Forecasting
ShiftAnalysis
Predictive Maintenance
Fleetwide BI reports Root-Cause
Analysis
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Is it a smooth Journey?
8
o Knowledge
o Time
o Support
o Technology first
o Scope
o Flexibility
More than 50 % of Big Data projects are unsuccessful !
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Getting process data analysis-ready
Enhance
Calculate
Correlate
Apply Algorithms
Enriching the raw data by calculating KPIs, aggregations and different analysis
Using statistics and machine learning to find insights in data across multiple variables
Wrapping a layer of context around the data, assets and events
Collecting high-fidelity high-frequency data from a variety of sources and systems
Finding patterns and relationships between variables across multiple datasets and datatypes
Collect
OT
IT
Data Preparation … feels like
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Data Scientist is the sexiest job of 21st century, but…
10
Source: http://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#5481f6037f75
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY 11
Time series Data is….complex!T
urb
ine 1
Speed
Torque
Bearing Temp
Oil Temp
Manufacturer
Last Service
Turb
ine 2
Time
Vestas
Siemens
June 20, 2013
Comm Failure
Additional Measure
Uneven SpacingSpike / Out of Range
Bad Sensor
Different Archive Start Times
Speed
Torque
Bearing Temp
Oil Temp
Wear Factor
Manufacturer
Last Service
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
What do we need to approach this problem?
Enhance
Calculate
Transport
Correlate
Apply Algorithms
Enriching the raw data by calculating KPIs, aggregations and different analysis
Using statistics and machine learning to find insights in data across multiple variables
Wrapping a layer of context around the data
Collecting high-fidelity high-frequency data from a variety of sources and systems
Transform the time-series data into row-column format and push it right to the desired tools
Finding patterns and relationships in data sets that aren’t revealed in one data set
Augment Increase the information content by adding statistics and summary calculations
Collect
OT
IT
Align & Cleanse Synchronize multiple data sources so they are comparable and purify the raw data using filters
Shape Building a data model to respond a specific business question or analysis
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
PI Integrator for Business Analytics/SAP/Azure
13
CLEANSE
AUGMENT SHAPE
TRANSMIT
PULL
PUSH
Quality / Validate
Model your data structure
Send the information
directly to the tool
Increase information content
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Situation: We are tracking compressor process data and we are able to track downtimes as they happen
Problem: We want to compare different compressors in terms of downtimes and understand which ones are offsetting from the baseline. We want to predict potential new outages to maximize asset availability
Specific Capabilities: Prepare and craft data model for Business Intelligence on downtimes, answer a set of questions in terms of asset performance.
Prepare and craft data model for Machine Learning analysis, bring the predictions back to PI and analyze the predictions to identify potential downtimes.
Demo: how are our compressors performing?
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Demo: how are our compressors performing?
15
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Demo Recap
16
I was able to:
• Build a data model to answer a specific question
• Provide Large amount of information in context
• Quickly consume the data in BI/Machine Learning and get results
Key Benefits:
• CAST
• Self-Service
• Performance
• Supports for multiple targets
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Operational Reporting & Analysis Architecture
17
17
… All BI tools that support ODBC
Tableau
Spotfire
SAS
MSFT BI
PI Server
PI Integrator for Business Analytics – Business Intelligence Edition
System of Record
Visualization & Analytics
Data Preparation and Integration
Layer
I want to analyze operations data stored in the PI System using
modern BI tools
PI Integrator for BA: Business Intelligence Edition
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Enterprise Data Warehouse Architecture
18
18
PI Server
PI Integrator for Business
Analytics
Tableau Spotfire
System of Record
Visualization & Analytics
Data Preparation and Integration
Layer
Oracle DW, SQL Server, TeradataEnterprise Data
Warehouse / Data Mart / Data Lake
Custom Applications
SASMSFT
BI
Custom or 3rd Party Data Management and ETL
EAMSalesCRM
ERP HR …
Hadoop
I need to fit operational data into my existing company
IT information architecture
PI Integrator for BA: Data Warehouse Edition
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Integration with SAP HANA: High level Architecture
19
EAMSalesAggregation PI Server
SAP HANA IoTIntegrator by OSIsoft
Systems of Record
Visualization, Analytics, & Business Process Applications
Data Preparation and Integration Layer
SAP HANAEnterprise Analytics and Applications Platform
SAP HANA Enterprise Information Management and Data Provisioning Agent
Partner Applications
PI Integrator Framework
CRM
ERP HR …
SAP BW
SAP LOB Solutions & Applications
Lumira GISBOBJ
SAP HANA IoT Integrator by OSIsoft
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
SAP HANA IoT Integrator by OSIsoft
20
Frank Ruland, SAP
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Solution architecture - PullPI data into SAP HANA via SAP HANA Smart Data Access
SAP HANA
HANA SDI
Aggregation PI Server
PI Asset Framework (AF)
SAP DP Agent
HANA Studio
PI Integrator FrameworkWeb UI Shape Designer
Windows
Windows
Linux
Microsoft SQL
Component sold by SAP
Component sold by OSIsoft
Legend:Included with SAP HANA IoT Integrator by OSIsoft (no fee). Provisioned by OSIsoft
User creates PI View in Web UI Shape Designer via PI Integrator Framework
PI View definition is stored in PI System (AF). PI View data is stored in optimized format in AF-managed SQL Server
SAP HANA user configures virtual tables in SAP HANA Studio using SAP HANA SDI and SAP HANA IoT Integrator by OSIsoft
PI SQL DAS controls access to PI Views
Windows (All Java)SAP HANA IoT Integrator by OSIsoft
PI JDBC
PI View
SAP HANA IoT Integrator by OSIsoft retrieves data from PI View located in SQL Server via PI JDBC driver
1
1
2
3
4
5
2
3
4
5
PI Data Archive
PI SQL DAS
Note:- This is the heart of the Integrator. Prepare the time series data via CAST into a row/column
format for consumption in a relational dbase environment
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Solution architecture - PushPI data into SAP HANA via SAP HANA Client
Aggregation PI Server
SAP HANA
PI Data ArchivePI Asset
Framework
HANA ClientHANA Client
PI Integrator FrameworkWeb UI Shape Designer
Windows
Windows
Linux
Microsoft SQL
Windows (ODBC)
1
2
User creates PI View in Web UI Shape Designer via PI Integrator Framework1
2 PI Integrator Framework pushes data to HANA via HANA Client (ODBC)PI
View
Component sold by SAP
Component sold by OSIsoft
Legend:Included with SAP HANA IoT Integrator by OSIsoft (no fee). Provisioned by OSIsoft
Note:- This is the heart of the Integrator. Prepare the time series data via CAST
into a row/column format for consumption in a relational dbase
environment
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
In-memory Processing Engines
Streaming
Engine
Graph
Engine
Spatial
Engine
Text
Engine
Calculation Engine
PAL APL AFL R Scripts
SAP HANAReal-time in-memory predictive analytics platform**
Predictive Analysis
Library (PAL)
Accelerated predictive
analysis and scoring with
native in-database
algorithms for both data-at-
rest and for streaming data
R integration
Execution of R scripts via
high- performing parallelized
vector based connection;
R scripts embedded as part
of overall query plan
Application Function
Library (AFL)
Application Function Library
(AFL) framework allows SAP,
partner, and customers to
develop, deploy, load, and
leverage their own advanced
analytic custom functions in
SAP HANA
Automated Predictive
Library (APL)
The predictive analysis
capabilities of SAP
Predictive Analytics
automated analytics
engine (formerly KXEN
/ II) in SAP HANA
SAP Custom Open Source
Streaming Algorithms*
Adaptive Hoeffding Tree
Denstream
Data-at-rest Algorithms
Association Analysis
Cluster Analysis
Classification Analysis
Time Series Analysis
+60 Native Algorithms
R Engine
Application
Function Modeler
SAP HANA
Studio
SAP HANAIn-memory In-database Predictive Analytics
* Predictive Algorithms for Streaming come with Smart Data Streaming License
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
SAP HANA In-Memory Predictive Analytics
– Predictive Analysis Library (PAL) Native predictive algorithms In-database processing for powerful
and fast results Quicker implementations Support for clustering, classification,
association, time series etc…
– R Integration for SAP HANA Enables the use of the R open source
environment (> 3,500 packages) in the context of the HANA in-memory database
R integration enabled via high performing parallelized connection
R script is embedded within SAP HANA SQL Script
Combine the depth and power of in-memory analytics within SAP HANA with the
breadth of R to support a variety of advanced analytic and predictive scenarios
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Compressor Demo video
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Product Roadmap
26
Matt Ziegler, OSIsoft
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Problem Complexity Drives the Need for Integrators
Real-time visibility
27
Monitoring
Process Optimization
Benchmarking
System Optimization
• BI App (i.e. Tableau, Spotfire, Lumira)
• PI Integrator for Business Analytics
• PI Integrator for SAP HANA
• Machine Learning (Azure ML, R)
• PI Integrator for Business Analytics
• PI Integrator for SAP HANA
• HMI
Disparate assets or interacting one-by-one Interacting with common assets as a fleet
Real-time & historical view across any plant
asset
Fleet-wide performance comparison
Large scale multi-variate analysis
Co
mp
lexi
ty
• PI ProcessBook• PI Coresight• PI Datalink
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
More integration options, more systems
Business
Intelligence &
Data Warehouses
Available Today
Scalable BI for the PI System
v1.0
• Fleet Asset Reporting
• Reduce Reporting Time
• Integrate w/ Data Warehouse
Available Today
Expanded Systems and Events
v1.1
• + Oracle
• + Hadoop (HIVE & HDFS)
• Event Frames
Streaming
Systems
Research
Streaming Pattern
• Enabling computations in real-
time with an external compute
engine
Planned (1H 2017)
Stream Systems
• Azure Event & IoT Hub
• Kafka
• Custom json
2015 1H-2016 Future
Planned (2H 2016)
Cloud Platforms
• Microsoft Azure
• Azure SQL, SQL DW
• Azure Data Lake
• SAP HANA Cloud Platform
Partner Platform
Research
Enable partners and customers
to build applications and interact
programmatically using PI
Integrator Framework
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Tables vs Streams
29
• Business Intelligence
• Human readable
• Batch / Bulk Process
• Normalized data
• Regularly scheduled
• Large data, few messages
• In-line (Streaming) Analytics
• Computer readable
• Specific Data / Targeted Process
• Raw or “Packages” of data
• Triggered
• Small data, many messages
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Integration Patterns
30
Tables
Files
Databases
Streams Other Patterns
Metadata
Programming
On-Demand
Workflow & Transactions
Files
Queues
Messaging
External Analytics Engines
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
2016 Technical Roadmap - Specific Planned
Enhancements SAP HANA IoT Integrator by OSIsoft
Version 1 (Dec 15) Version 1.5 (May 16) Version 2 (Q4 16)
Leverage event frames
(batches) of PI System
data published into SAP
HANA for ad hoc projects
and analysis in memory
Publish PI System data to
SAP HANA or HANA
Cloud Platform using SDI.
Retrieve live information
from PI Views using SDI.
Batch-cleanse, filter and
aggregate PI System data
into federated tables
within SAP HANA using
SDA
Protect your technology investment!
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY 32
SAP HANA / HANA Cloud
Platform
SAP HANA IoT
Integrator 2015
(Smart Data Access)
Lumira
S/4 HANA
PdMS
AnalyticsPull Data
Access
Publish
Data (Push)
SAP HANA IoT
Integrator 2015 SP1
2016 (SDI)
Query Data
(API)SAP IoT Integrator
2016
Planned 2017
Stream Data
(Stream
Events-SDS)
Receive Data
(Predictions)
Receive
Metadata
(Assets / PM)
Road Map Databases & Applications
Planned 2017
Research
Virtual Tables in HANA
Smart Data Streaming
S4 (PM) AIN
Example Roadmap - SAP HANA IoT Integrator by OSIsoftS
AP
HA
NA
Ecosyste
m
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Contact Information
Franco [email protected]
Systems Engineer
OSIsoft ltd UK
3333
Matt [email protected]
Product Manager
OSIsoft, LLC
Frank [email protected] of Industry Ecosystem for Energy and Natural Resources
SAP SE
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Questions
Please wait for the
microphone before asking
your questions
Please remember to…
Complete the Online Survey
for this session
State your
name & company
34
http://ddut.ch/osisoft
© Copyright 2016 OSIsoft, LLCEMEA USERS CONFERENCE • BERLIN, GERMANY
Thank You