1. Utility Transformations: TBD
July 22, 2013

2. Contents Introduction 3 Methodology 4 Executive Summary 5 Findings 6
Our Take
Contents

3. Introduction
As Smart grid deployments increase across the industry, utilities with new programs are grappling with the resulting influx of data, while those with fully deployed programs work to improve their management, analysis and ability to leverage smart grid data.
Somewhere in this data deluge lies the path to a more efficient tomorrow, but:
How have utilities progressed over the last year?
Where are they headed next?
How can they a Smart Infrastructure around Smart Metering and Smart Grids?
The “TBD” study surveyed senior North American utility executives to:
Gauge their perception on how “big data” is impacting their businesses
Measure perception of their data growth and preparedness to handle it
Identify where utilities are in the process of implementing solutions to help deal with unprecedented data volumes, as well as where they expect to be in six to 12 months
Determine utilities’ plans to extract optimal business value from the data to improve network and service reliability and better target, engage with, and serve their customers
Identify how they are using new data streams and analytics to maintain assets, streamline distribution management, and better control costs/optimize limited budgets

4. Methodology
Oracle conducted telephone interviews with 151 North American senior-level electrical utility executives in April and May The sample size consists of 136 U.S. and 15 Canadian responses.*
Sample Demographics
Respondent Title:
3% Owner, Partner, President, CEO, or COO
3% CFO, Controller, or Treasurer
1% CIO or CTO
1% EVP or SVP
3% VP, Assistant VP, or Principal
16% Director of Customer Service
12% Director of Distribution
31% Director of Smart Grid or Smart Metering
30% General Manager or Managing Director
Utility Size by Number of End-Use Metered Customers:
15% Fewer than 100,000 customers
43% 100,000 to 499,999 customers
15% 500,000 to 999,999 customers
27% One million customers or more
Current Smart Meter Status:
29% Completed a pilot program
24% Completed a partial rollout
47% Completed a full rollout to all customers/service points
*This report has a margin of error of ±7.95% at a 95% confidence level.

5. Executive Summary The Good News: The Opportunity:
Utilities are increasingly prepared for the smart gird data influx. Almost twice as many utilities say they are completely prepared this year compared to one year ago
Data security is an emerging success story. Nearly three out of four (72%) utilities give themselves an “A” in their ability to secure and safeguard data
Utilities are accessing valuable data from a variety of sources, including outage management systems, SCADA history and customer data and feedback
The Opportunity:
While utilities are collecting and using more data today, there is room to increase the translation of information into actionable intelligence and leverage it for strategic decision making
Despite increased preparedness and data usage, less than half of utilities are using smart grid data to improve customer service today. Opportunities exist to provide customers with information about usage patterns and alert them to usage spikes
Utilities see potential benefits in cloud-based solutions. More than one fourth (26%) of utilities are either planning, implementing, or maintaining cloud-based solutions for data management and analysis. Another 38% are in the initial discovery phase

6. Utilities are Starting to Move the Needle
Almost twice as many utilities say they are completely prepared for smart grid data today vs. one year ago. Utilities report slight improvements in information sharing and strategic decision making
How would you grade your utility’s preparedness to manage the smart grid/ smart meter data influx?
Percentage who said completely prepared:1
2012 – 9%
2013 – 17%
Where have utilities improved most?
Percentage doing an excellent job:1
2012
2013
Putting timely information into the hands of people who need it most 8%
20%
Making strategic decisions based on the information 4%
11%
TBD
TAKE AWAY
1Those who rated their utility a 10 on a scale of 1 to 10, where 1 was very poor and 10 was excellent
Note for Internal Review: After reviewing the data by utility size, analysts are confident that the year over year changes displayed on this slide are not due to the change in sample composition.
Results by utility size are as follows:
How would you grade your utility’s preparedness to manage the smart grid/ smart meter data influx?
Percentage who said completely prepared:1
Less than 100K = 22%
K = 9%
K= 18%
1mil+= 24%
Where have utilities improved the most?
Putting timely information into the hands of people who need it most:
Less than 100K = 26%
K = 12%
K= 27%
1mil+= 22%
Making strategic decisions based on the information:
Less than 100K = 13%
K = 6%
K= 14%
1mil+= 12%

7. Solid data policies may contribute to preparedness.
Confidence in Data Security Also Climbs
Ability to safeguard data remains utilities’ most effective response to handling the influx of data from smart grid / smart meter programs
72%
of utilities give themselves an “A” in their ability to secure/ safeguard data1
(up from 61% in 2012)
Additionally, three out of four feel their smart grid/smart meter data privacy policy is sufficient for protecting/securing their data.
Solid data policies may contribute to preparedness.
94%
have policy
Those who feel prepared for the data influx are significantly more likely to have a sufficient data privacy policy than those who say they are not prepared2
62%
have policy
Unfortunately, more than one in 10 utilities (13%) do not have a data privacy policy in place.
TBD
TAKE AWAY
1Those who gave themselves a 9 or 10 on a scale of 1 to 10, where 1 was very poor and 10 was excellent 2Prepared = 9 or 10 rating; Unprepared = 1-6 rating
Note for internal review: After reviewing the data by utility size, analysts are confident that the year over year changes displayed on this slide are not due to the change in sample composition.
Results by utility size are as follows:
On a scale of 1-10, where 1 is very poor and 10 is excellent, how would you rate your utility’s effectiveness in handling the data influx in the following areas:
Securing/safeguarding data
Less than 100K = 70%
K = 66%
K= 64%
1mil+= 71%

8. Utilities are Data Rich
95% of utilities indicated they gather valuable data from sources other than smart meters
In addition to smart meters, which of the following data sources provide the most valuable information to your organization?1
Outage Management Systems
SCADA History
Customer Data and Feedback
Alternative Energy Sources
59% %
44% %
Outage and Distribution Management Systems
Customer Data and Feedback
Valuable Data Sources*
Overall usage stats:
Outage Management Systems = 64%
SCADA = 58%
Customer Data = 54%
Alternative Energy Sources = 12%
TBD
TAKE AWAY
1Respondents asked to top three *The 2012 survey did not ask respondents to rate the importance of SCADA history
Full rollout Partial rollout Pilot
Outage management systems 68% 61% 60%
SCADA history 52% 67% 62%
Customer data and feedback  48% 64% 57%
Alternative energy sources 15% 17% 5%
Social media 16% 6% 10%
Weather-monitoring systems 13% 6% 12%
Other smart grid points 10% 17% 7%
Energy traders/market data 7% 17% 12%
Overall usage stats:
Outage Management Systems = 64%
SCADA = 58%
Customer Data = 54%
Alternative Energy Sources = 12%
Social Media = 11%
Weather-monitoring systems = 11%
Other smart grid points = 11%
Energy traders / market data = 11%

9. Many Say Data Ownership Overlaps
Utilities say a combination of metering, IT and the back office own smart grid data
Metering department IT
Back office
58%
47%
41%
15%
12%
17%
14% 3% 9%
Who owns or is responsible for smart meter and/or smart grid data?*
58% Metering
47% IT
41% Back office/billing
37% Customer service
23% Transmission & distribution
16% Business analysts
8% Other
TBD
TAKE AWAY
1Respondents asked to select all that apply.
Venn Diagram (revise if graphic colors change)
Percentages in black = The percentage of all respondents that indicated a department is responsible for smart meter and/or smart grid data.
Percentages in white = The percentage of all respondents that indicated a given combination of departments is responsible for smart meter data. All of the percentages in white within a given circle add up to the black percentage in black. For example, 17% indicated they thought the Metering and the IT departments are responsible for smart meter data and 15% indicated they thought the Metering department, the IT department, and the Back office are responsible for smart meter data.

10. The Usage Gap Remains
While utilities are collecting and using more information today than one year ago, they still struggle to take full advantage of the data for decision making and process support
Data Being Used
97% are collecting some from of smart grid data
85% are using some type of smart grid data
TBD
TAKE AWAY

11. Utilities Have a Customer Service Opportunity
Overall usage stats:
Provide customers their usage patterns = 57%
Improve compatibility with regulations = 53%
Implement/improve efficiency programs = 51%
Identify trends and forecast demand = 47%
Implement demand-response programs = 47%
Use predictive analytics = 42%
Target customers for new programs = 40%
Establish new pricing programs = 34%
Alert customers with usage spikes = 26%
In most cases, less than half of utilities are using smart grid data to improve customer service today – even those with full smart meter rollouts
How are utilities leveraging smart grid data to improve customer service today?1
While 57% provide customers with information about their usage, just 26% alert customers of usage spikes
TBD
TAKE AWAY
1Respondents asked to select all that apply

12. Percentage who gave themselves an “A” in the following measures:1
Utilities Have an Operational Opportunity
Overall usage stats:
Securing/safeguarding data = 72%
Ensuring storage capacity = 32%
Timely information to those who need it most = 34%
Delivering useful information to customers = 32%
Reporting on information = 25%
Translating information into actionable intelligence = 26%
Making strategic decisions = 25%
Finding/hiring the right talent = 19%
Visualizing data = 21%
Despite some year-over-year improvements, utilities still grapple with extracting data value. Only a quarter feel confident in their ability to translate information into actionable intelligence or use it for strategic decision making
Percentage who gave themselves an “A” in the following measures:1
TBD
TAKE AWAY
1Those who gave themselves a 9 or 10 on a scale of 1 to 10, where 1 was very poor and 10 was excellent

13. Necessary Expertise is in Short Supply
Only two out of five report that they currently have the sufficient expertise around smart grid data analytics or data science
Do you believe your utility has a skills gap around smart grid data analytics or data science?
62% Yes   
31% No
7% Unsure
Utilities in all phases of smart meter roll outs are equally likely to have a data analytics skills gap
Even those who gave themselves a 9 or 10 in overall preparedness see a skills gap – 58% said yes
TBD
TAKE AWAY
1Respondents asked to select all that apply

14. Nearly All are Working to Improve Talent
To address the skills gap, utilities are combining training and hiring with packaged solutions
Those with a gap:
Which of the following steps is your utility taking to address this skills gap?1
Those that have closed the gap:
Which of the following steps did your utility take to eliminate or avoid this skills gap?1
Training our current employees (90%)
Building our internal capability (60%)
Investing in pre-packaged analytics solutions (45%)
Outsourcing analytics to a third party (30%)
Training our current employees (89%)
Building our internal capability (53%)
Investing in pre-packaged analytics solutions (34%)
Outsourcing analytics to a third party  (21%)
TBD
TAKE AWAY
1Respondents asked to select all that apply.

15. Many See Potential in Cloud-Based Solutions
Two out of three utilities are considering cloud-based solutions for smart grid/smart meter data management and analysis
Those planning, implementing or maintaining: What do you expect to see as the top benefits of your cloud-based solution?1 #1 #2 #3 #4 #5
What is your utility’s status regarding cloud- based solutions for smart grid/smart meter data management and analysis?
38% Discovery
7% Planning
11% Implementing
8% Maintaining
36% None of the above – we are not currently considering cloud-based solutions
Improved speed of service and
application deployment (44%)
Improved information security (38%)
Flexibility/scalability to handle peak computing
needs on demand (34%)
Improved data storage capabilities/capacity (31%)
Reduced IT capital expenditure (24%)
TBD
TAKE AWAY
1Respondents asked to select all that apply.

16. What’s in it for the Utility
Utilities expect smart grid rollout and the corresponding implementation of predictive analytics to improve revenue protection and reduce asset maintenance costs
In what utility processes do you expect to achieve the greatest value from predictive analytics?1
How long do you think it will take your utility to realize a positive return on its smart grid analytics investment?
15% Within the year
17% 1-3 years
23% 3-5 years  
18% years
5% More than 10 years   
22% Unsure
Improving revenue protection (70%)
Reducing asset maintenance (61%)
Reducing asset replacement  (57%)
Reducing infrastructure costs (54%)
Analyzing distributed generation (50%)
TBD
TAKE AWAY
1Respondents asked to select all that apply.
Complete List
70% Improving revenue protection 
61% Reducing asset maintenance
57% Reducing asset replacement  
54% Reducing capital infrastructure costs
50% Analyzing and assessing distributed generation
41% Reducing generation planning costs
39% Reducing generation operations costs
26% Assessing electric vehicle impact

17. What’s in it for the Customer
Customers can expect smart grid infrastructure and data to enable more reliable energy, more intelligent usage information and custom pricing programs
The average utility with more than one million customers will invest approximately $180 million in the smart grid and smart metering over the next five years.
Short Term: Utilities’ top three plans for using smart grid data in the next 1-2 years:
Long Term: Utilities’ top three plans for using smart gird data in the next 3+ years: #1 #2 #3
Compare historical data to identify trends
and forecast demand
Provide customers with information about
their usage patterns
Use predictive analytics to minimize outages
or improve service delivery and reliability #1 #2 #3
Alert customers of usage spikes
Establish new pricing programs (i.e., time variable pricing)
Use predictive analytics to minimize outages
or improve service delivery and reliability
TBD
TAKE AWAY

18. Our Take …
Recruit the Right Talent: Currently, only two out of five unities report that they have sufficient expertise around smart grid data or general data science. Finding and hiring the right talent is the key to truly leveraging the power of smart grid data
Consider the Cloud: Many utilities are already considering cloud-based solutions to improve speed, security, and scalability of data management systems. As data volumes grow, so will the importance of fully leveraging cloud based solutions for data management.
Prioritize data security and privacy: Privacy and data security are at the forefront of customer’s minds. Establishing thorough security and privacy policies protects the customer and the utility.
Clarify Data Ownership: Smart metering and smart grid data is often housed or managed by multiple departments. Utilities must ensure they have an enterprise level data strategy in place that is clearly understood and adhered to within the organization
Consider the Customer: Utilities should expand efforts to use smart meter and smart grid data to improve customer service and the customer experiences

19. For media inquiries, please contact:
Caroline Vespi
Oracle
Gail Repsher Emery
O’Keeffe & Company