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CenterPoint Energy
Time Machines: The Evolution and Application of
Predictive Analytics
Dr. Steven P. Pratt
Chief Technology
Officer
CenterPoint Energy Proprietary and Confidential 2
CenterPoint Energy
CenterPoint Energy Proprietary and Confidential
The Last Seven Years
3
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
CenterPoint Energy Proprietary and Confidential 4
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
CenterPoint Energy Proprietary and Confidential
Continuing Technology Operation’s Areas of Opportunity
5
• 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
CenterPoint Energy Proprietary and Confidential
Business Drivers
6
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
CenterPoint Energy Proprietary and Confidential
Estimated Five Year Data Growth
7
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
CenterPoint Energy Proprietary and Confidential
Changing Emphasis on Data
8
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”
CenterPoint Energy Proprietary and Confidential
Transformation From Data Driven to Intelligence Driven
9
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
“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
10
CenterPoint Energy Proprietary and Confidential
Hypothesis
11
Time travel does not require us to be present in the past or the future but
simply understand the context of either.
CenterPoint Energy Proprietary and Confidential
Historical Context
12
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
CenterPoint Energy Proprietary and Confidential
Value of Data-in-Memory Computing
13
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
CenterPoint Energy Proprietary and Confidential
The Predictability Horizon
14
“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
CenterPoint Energy Proprietary and Confidential
HANA as an Analytics Solution
15
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
CenterPoint Energy Proprietary and Confidential
A CRM Example
16
Ba
ck O
ffic
e
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.
CenterPoint Energy Proprietary and Confidential
A Prediction Example
17
CenterPoint Energy Proprietary and Confidential
HANA Application View
18 SELECT DRILL-DOWN PREDICT
CenterPoint Energy Proprietary and Confidential
Predictive Analytics serve as a Roadmap for Deriving Value from Data
19
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