Operational Intelligence: Real-Time Business Analytics for Big Data Most business intelligence (BI) implementations are focussed on analysing what has happened, sometimes why things have happened. Often Business Intelligence provides no more than Business Insights. . Even so called “real-time” BI reports are generated using historical data. While this historical view of data has some value, it does not represent the complete picture. Fully-informed business decisions require real-time insight into high-volume streaming data sources, current events, and ongoing business processes. Operational intelligence (OI) correlates the real-time analysis of big data in motion with historical data, delivering complete business insight in milliseconds. By continuously analyzing big data in motion, events, and business processes as they are happening, OI enables fully informed decisions that maximize business impact. In this presentation, you will learn: • What operational intelligence is and does, plus what technologies enable it • Business and technology requirements that are driving user organizations toward true real-time analytics and reporting with big data • Real-time applications for operational intelligence

1. Responding to your environment

2. Operational Intelligence
01 October 2015 | BIG DATA Expo Utrecht

3. UREASON
A Dutch company based in Delft founded in 2001
Main offices in the Netherlands and the UK (Maidenhead)
A provider of a expert systems with a.o. machine learning
capabilities
Data scientists specialized in increasing operational intelligence
Strong company DNA in expert systems and industrial
automation
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4. Customers
Customers in Europe, North America and the Middle East
Industries: Oil & Gas, Petrochemical, Chemical, Traffic, Energy,
Telecom and Utilities
Some Examples:
Sabic, Imtech, Halliburton, Siemens Power Generation, Vestolit,
Shell Global Solutions/NAM, BASF, BP, Shell, KPE, OMV, SAFCO,
Siemens Oil & Gas, LyondellBasell, Total E&P, Anglian Water,
Tronox, Fluxys, Isover, Maersk, EMMI, SIAAP, Clough Amec,
Cameron, ThamesWater, Telia, …
From Data Science to Solution Development
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5. What is Operational Intelligence?
Operational Intelligence: A category of real-time dynamic, business analytics that delivers visibility and insight into data, streaming
events and business operations (Wikipedia).
Predictive Analytics enables Operational Intelligence ….
Predictive Analytics: A variety of statistical techniques from modelling, machine learning, and data mining that analyse current and
historical facts to make predictions about future, or otherwise unknown, events. I.e. a set of techniques to extract a model from data
to understand what will happen.
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Low High
Business Values & Complexity

6. Value of Operational Intelligence?
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1869–CharlesJosepMinard

7. Analytics … Analytics … Analytics
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Descriptive & Diagnostic Analytics Predictive & Prescriptive Analytics
Objective
Understand the Past
Observe Trends
Catalyst for Discussions
Gain Insights
Make Decisions
Take Actions
View Historical & Current Future Oriented
Metrics Lagging Indicators Leading Indicators
Users
Middle & Senior Management
Analysts & End Users
C-Level & Senior Management
Strategists, Analysts, Managers
Descriptive Analytics: Using data to find out what has happened
Diagnostic Analytics: Using data to find out why it happened
Predictive Analytics: Using data to find out what could happen
Presriptive Analytics: Using data to prescribe the best course of action for the future optimization

8. Data .. Data, Data .. Leading to more Insights
"Data is the new oil!" Clive Humby, ANA Senior marketer’s summit, Kellogg School.
Data Vs
- Volume – Scale of data: Every 2 days we create as much data as we generated up to
2003
- Velocity – Speed of data
- Variety – Different forms of data: structured, unstructured, semi structured, complex
structured data
- Variability – Data constantly changes
- Veracity – Uncertainty of data: data needs to be assessed if quality is good
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IBM 2012

9. Not Without Knowledge !
Data is just like crude. It’s valuable,
but if unrefined it cannot really be
used. It has to be changed into gas,
plastic, chemicals, etc. to create a
valuable entity that drives profitable
activity; so must data be broken
down, analysed for it to have value
(Michael Palmer).
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Knowledge
Design
Operational
Custom Knowledge
New Knowledge
Knowledge Engineering
Knowledge Discovery
Deterministic approach
Elicitation
- Retrospective
- Interviews
Machine Learning
Qualitative reasoning
Rule discovery
Patterns & Correlation discovery
1
2
Expertise

10. Typical Approach – Predictive Analytics Programs
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5
Updates &
Tuning (3)
4
Model
Deployment
3C
Model
Validation
3B
Model
Training
3A
Feature
Selection
2B
Data
Preparation
2A
Data
Understanding
1
Business
Understanding
Heavy end-user involvement
Knowledge Intensive Heavy end-user involvement
Data Collection Data Science
Process: ~ 3 - 5 Weeks – Feasible YES/NO
Based on results of the POC and business adoption possible rollout plan can be established
(3) Loopback to improve deployed models

11. Requirements - Ingredients
Streaming data/events
Historical data
Classified (known) issues
Understanding of the context in
which the data/events can be used
Tooling & Data Scientists 
UREASON & USE
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12. U.S.E. – Swiss Army Knife for Operational Intelligence
Data: Real-time: OPC DA, RDBMS, WITSML; Historical:
RDBMS/Files/Replay WITSML
Dimension Reduction: PCA and SAX parsing and clustering methods
such as DBScan
Model: Soft Class Model , Connectivity & Containment ship, full CRUD
access
Machine Learning: Supervised Neural Networks, Unsupervised Neural
Networks, Symbolic Anomaly Detection (unsupervised), Multivariate
Symbolic Anomaly Detection (unsupervised), SVM (supervised)
Rules: Diagnostic Rules, Control Flow, Reusable Rule Blocks, Event Fault
Trees, RT FFT, Bow-Tie Models, Decision Support Agents, Fuzzy
Reasoning, Topology Reasoning, etc.
Results: WYSIWYG UI: Data Displays, Real-Time Trends, Alarm & Event
Browser, SOP in Event etc. Instant web-dashboards – no programming
Deployment: High Availability support, data & event replication,
public/private modules, remote deployment and updates.
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13. Predictive Analytics - Examples
Provide Insight .. reducing risks and/or cost
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Industry Application area Techniques Results
Power generation Outage prevention Expert & Fuzzy rules Cost reduction
Chemical Gas leak detection Expert rules, neural
networks
24/7 monitoring, increased
safety
Drinking water Pollution assessment Rules, Topology and
Probabilistic Risk models
Pollution spread and risk
Oil & Gas ESP failure prediction Pattern analysis Early insights
Power generation Theft detection Anomaly detection Indication
Insurance Loyalty SVM and deterministic
rules
Early tipping risk
identification

14. Contact
UREASON International BV
Drie Akersstraat 11
2611 JR, Delft
The Netherlands
Telephone:
General: +31 85 273 49 20
Fax: +31 85 273 49 29
Email:
General: info@ureason.com
Support: support@ureason.com
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