Dr Steven P. Pratt, PhD., Chief Technology Officer, CenterPoint Energy, Inc. delivered his presentation entitled Time Machines: The Evolution and Application of Predictive Analytics at the marcus evans CIO Summit 2016 held in Los Angeles, CA

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CenterPoint Energy
Time Machines: The Evolution and Application of
Predictive Analytics
Dr. Steven P. Pratt
Chief Technology
Officer

2. CenterPoint Energy Proprietary and Confidential
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CenterPoint Energy

3. CenterPoint Energy Proprietary and Confidential
The Last Seven Years
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 From 12 to 28 State
Operation
 From Analog to Digital Grid
 From 80,000 to 221,000,000
meter reads/day
 From 700TB to 5.8 PB
 From Data Reactive to
Decision Proactive
 From Routine Operations to
Disaster Recovery

4. CenterPoint Energy Proprietary and Confidential
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Vendor
IT
Provider
Hybrid
IT
IT/OT
PT
Technology and technology related
services are built on a foundation
of global, geographically dispersed
and standardized elements
delivered and supported through
partnerships
FUTURE
Digital services have evolved
from a purely vendor
provisioning model to a
symbiotic and codependent
delivery of business
functionality
PRESENT
The Metamorphosis of Digital Services

5. CenterPoint Energy Proprietary and Confidential
Continuing Technology Operation’s Areas of
Opportunity
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• Competition for resources
• Return on technology investment
• Application rationalization
• Cloud deployment
• Operationalization
• Automation
• Resiliency
• Solution Quality
• Incident reduction and response
• Balanced project portfolio
• Operational complexity
• Software rationalization
• Data management
• Technology governance
• Standardization
• Innovation management
• Strategic continuity
• Technology obsolescence
• Mobile maturity
• Metric measurement

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Business Drivers
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 Safety
 Customers
 Employees
 Society
 All
 Innovation for Growth
 Intelligent Meters
 Smart Grid
 Business Transformation
 Information Technology
 Operations Technology
 Consumer Evolution
 Access
 Expectation
 Preferences
 Strategic Consistency
 Single Reference Architecture
 Encapsulating Frameworks
 Execution
 Operational Optimization
 Service Catalog
 Automated Operations
 Consolidated Portfolios
 Innovation Agenda
 Modernization
 Integration
 Constituency Focus
 Customer Vision
 Employee Satisfaction
 Societal Benefit
 Regulatory Compliance
 Value Realization
 Corporate Data Management & Control
 Technology Rationalization
 Big Data
 Secured Assets
Technology Challenges

7. CenterPoint Energy Proprietary and Confidential
Estimated Five Year Data Growth
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0 TB
2,000 TB
4,000 TB
6,000 TB
8,000 TB
10,000 TB
12,000 TB
14,000 TB
1 Yr 2 Yrs 3 Yrs 4 Yrs 5 Yrs
Business As Usual EOY Usable Disk Stg
Tier 0/1 Tier 3

8. CenterPoint Energy Proprietary and Confidential
Changing Emphasis on Data
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Virtualization:
Data Structure
How much data you have
Where your data is
What we know
What happened
What’s next
Data persistence
Realization:
Data Interoperability
How much you can do with your data
Where your data is used
What we don’t know
What could happen
What’s now
Data Dynamism
“The difference between Data Virtualization and Intelligence Realization is
analogous to that of saving money or adding value”

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Transformation From Data Driven to
Intelligence Driven
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 Operation
 Independent
 Internal Centricity
 Customer Driven
 Data Driven
 Architectures
 Divergence
 IT/OT
− Dark Data
− Complex
Data
Management
− Information
Centricity
− Data Compression
− Data Derivatives
− Complex Events
− Complex Analytics
 Innovation
 Co-dependent
 External Centricity
 Consumer Driven
 Intelligence Driven
 Architecture
 Convergence
 CT (CenterPoint
Energy Technology)
Strategic shifts require changes
in constructs, methods, and speed in Data
innovation

10. “Big Data” as the Basis for Predictability
 How an organization chooses to manage “big data” differentiates increased cost from
derived value and distinguishes between liability and asset
 “Big Data” should be evaluated from three perspectives: Management, Governance, and
Insight
 “Big Data” requires efficient, effective, and economic management. Advanced
compression technologies, automation, archiving, and storage tiering reduce costs and
lessen dependence on specialized skill sets
 Data growth without bound creates a costly and unnecessary burden. Organizational
governance structures ensure data created and maintained is relevant, meets business
needs, and follows processes for creation, use, and retirement of data resources
 Organizations must recognize data as an asset to be mined for its residual value. “Big
Data” provides a broad sample space for knowledge and value creation beyond that for
which the data was originally created. Analytics and Advanced Analytics provide for
practically unlimited data analysis yielding insights such as situational awareness,
operational decision making, customer knowledge, and potential new business
opportunities
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Hypothesis
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Time travel does not require us to be present in the past or the future but
simply understand the context of either.

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Historical Context
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 Everything that possibly could
happen has likely already
happened.
 There is a taxonomic classification
of historical events.
 Every historical event can be
defined in terms of the four
dimensions.
 Historical events are necessarily
directly or indirectly related.
 Future state is a composite of
historical elements, taxonomic class,
dimensional context, and
association.
 Mathematics determines the
accuracy of the future state
representation
 The amount, order, structure,
connectivity, and type of data is the
basis for predicting future state
H.G. Well’s Depiction of a Time Machine

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Value of Data-in-Memory Computing
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Memory Data
Data-in-Memory
Transform
Load/Unload
Merge
Data is stored in a column store directly in
memory, naturally compressed and optimized
for high speed analytic processing
HANA
Application logic is pushed into HANA’s in-
memory predictive and calculation engine as a
strategic platform for all SAP future applications
Eliminates latency and significantly simplifies the
landscape including infrastructure, resource
development and maintenance of applications
Virtually transform or model data for direct
consumption by in-memory, embedded
predictive functions
Enables agility in development and support of
applications and Reduces resources required to
develop, maintain and support applications
Enables insight to ALL data with exponentially
greater performance and time to results

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The Predictability Horizon
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“If we have sufficient data from the past we can sufficiently predict the future”
HANA
BIG DATA
Load Study
Transformer Load Study
Demand Forecasting
Diversion Detection Asset Management
Usage Insight Regulatory Market Study
Gas Forecasting Event Aggregation
Financial Modeling
Customer Segmentation
and Sentiment

15. CenterPoint Energy Proprietary and Confidential
HANA as an Analytics Solution
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HANA established
as an interim
platform for data
aggregation,
applications, and
advanced reporting
HANA evolves to a
strategic solution
for data integration
and business
functionality and
analytics

16. CenterPoint Energy Proprietary and Confidential
A CRM Example
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BackOffice
Customers
HANA
Analytics
Marketing
Call
Center
Channel
One
Channel
Two
Channel
Three
Scenario: Customer contacts the call
center with the potential of one or
more of 40 potential reasons for
calling.
Utilizing a HANA based Predictive
Analytics Engine (PAE), the most
likely reason for the customer call is
predicted, either deflecting the call
entirely or reducing the call agent
handling time.
The same prediction can be used to
proactively communicate with the
customer.
This degree of predictability not only
increase customer satisfaction but
also the productivity of the call center
agents.
Solution: The PAE combines historical data
from multiple sources to create
approximately 14,291,200 data points
resulting in the most likely reason for a
customer call.
The calculation is completed in 1 second and
represents a 9000% reduction in the time
required over prior methods of prediction.

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A Prediction Example
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HANA Application View
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SELECT DRILL-DOWN PREDICT

19. CenterPoint Energy Proprietary and Confidential
Predictive Analytics serve as a Roadmap for
Deriving Value from Data
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Deliver maximum value from our combined information assets through 2020 and beyond:
 Optimization of the data resources we create and maintain
 Development of an optimal cost and support model to balance exponential growth with
resultant data value
 Return on our investment in data and information assets through application of
advanced analytics and automated operations decision-making
 Institutionalizing long-term data management through a strong and sustainable
governance model
The Application:
 Building an “active” Smart Analytics System that captures, virtualizes, interrogates, and
realizes data outcomes (Intelligence Realization)
 Assembling a framework of interoperability between technology including system
management, complex processing, real-time analytics, and Service Orientation
 Creating a Decision Services team to develop and support both business and
operational analytical context
 Transforming to an Industry specific data model for consistency of data constructs
across the Enterprise.
 Evolving from data virtualization to Intelligence Realization

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Thank You on Behalf of CenterPoint
Energy
Steve.Pratt@CenterPointEnergy.com