1. DATA for ACTION Edna Yohei Malaisé
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©2016  ITRON CONFIDENTIAL PROPRIETARY
March 2017

2. Too much data, kills right information
ALERT!
this is what I need to know
Years ago, in the early stages of Automated Meter Reading, I heard an expression « Data Graveyard », which made me confused and laugh at the same time.
Was not true that more data we have, more information we can create? Was not true that more data we have, better will be the accuracy of information? Maybe, but what was not clear or not said, was that, yes, it is better if we have more data, but the ones that are really relevant. The ones that after filtering, treating and analysed, we can take decisions on that. The ones that can lead us to right decisions.
That is true that Big Data and Cloud based platforms accelerated the accumulation of billions of data that are retrieved every year from different devices, but the dark side of this concept is that too much information are killing information. It seems very obvious what I am saying, but very few Water Utilities around the World are using all data or information they do receive from their assets in a holistic way so that they can solve real problems.
Also, a deep level of expertise in each domain is necessary to create the right algorithms to solve problems in water distribution, read the information and understand them.
Presentation Title |

3. Example of innacurate data in metering
Distribution
Transmission
Mains
Storage
Reservoir
Commercial & Industrial Meter ?
Production
Plant ?
Meter for Billing
Water
Treatment
Plant ?
District Meter or Network Meter ? ?
Domestic or Residential Meters ? ? ? ? ?
Catchment
Ok, this is very specific, but being in metering business, this is a classic example I bring up and wonder every time I see it – « how water utilities can reduce Water Losses problems if the basic data is not right??
Correct me if I say something completely absurd, but as far as I know, to calculate the hydraulic balance in a network, we need to know how much water we input in the system and how much water we are delivering to consumers. How can we make it right if the instruments we are using to measure the volume of water are not counting properly?

4. NON REVENUE WATER and DATA
Accurate, regular and medium frequency data
Approximative or estimate data
Accurate, regular and higher resolution data
… to achieve
results
To reduce from levels above 50%, no matter what is the action, the results are immediate and visible. So, approximative data is still acceptable to define actions
e.g. if we change massively the water meters, the revenue tends to increase slightly and immediately.
Now, when we go down to 30 % level, we need to consider more specific and targeted actions like choose between changing meters or changing pipes, or reduce pressure in the network are necessary. If the data we have is not accurate enough to tell us the root cause of losses, it is very difficult to define actions
When we go down to 20% and less, the challenge is not to reduce, but to keep and to do it, a consistent long term plan,), accurate and reliable technology to produce accurate and reliable data for decision making process.
Data Source: Guidelines for water loss reduction, GIZ, VAG, KIT and FHNW

5. Let’s stop « non revenue data »
The data coming from meters are the most precious ones if we combine them with other data avalaible in Utilities in general.
I have been hearing some « buzz » through different sources on WEB defending ferosciously the need of « real time data ».
It made me think for quite long time, why do I need « real time data » if I am not capable to act on them.
It is better have a reasonable amount of data that will give me enough information to act according to my capacity rather than having so many information that we will not be able to understand.
Obviously as long as we improve in the pace adapted to our human and technical resources, yes, we can increase the resolution or frequency of data collection. But step by step, consciously.
Just to give you an example of volume/ quantity of data we can accumulate: In a city with around 100k connections, if we have hourly readings, we can easily have a billion of readings in a year.
If we are not using them, they are only extra cost.

6. 22,11 m3 How utilities use this information? 1 2 5 9 7 3
Check the customer data base
Check if customer profile changed
Check the regular presence of customer in the house
Replace meter to avoid or reduce losses in revenue
Action
Consumption Historical dropping down
Analyse
Information
m3 February 2017
m3 January 2017 1 2 5 9 7 3
22,11 m3
m3 February 2017
Data
How utilities use this information?

7. CREATING DATA TO SOLVE CHALLENGES
Regulation
&
Water
Conservation
Tariffs
&
Customer
Engagement
Reading Productivity
Asset Management
Customer Satisfaction
Water Efficiency

8. PROBLEMS TO ADDRESS DATA SET COMPLETENESS Payload in bytes1’
DMA
Management
15’
NRW
Management
Frequency of read
Hour
Asset
Management
BILLING
& Customer
Service
Day
Month
BILLING
Occasional
Year
N xN xN xN >500xN
Before ask for real time data, it is important to define what it will be for.
If the objective is just to bill occasionaly, it does not make much sense to ask for so many information.
Remember, if there is no plan to use all data you collect to reduce cost or increase revenues, there is no meaning to increase the frequency we collect them.
Payload in bytes
Amount of Data

9. SMART METERING Solutions
DATA SET COMPLETENESS
24h index &
Consomption profile
1’ index &
Adv. info
15’ index &
Night vs Day 1’
Timed index &
Night vs Day
Daily index &
Adv. alarms
DMA
Management
24h index &
Conso profile
15’
Last index &
Basic alarms
NRW
Management
Daily index &
Adv. alarms
Hour
Asset
Management
Frequency of readings
Last index &
Basic alarms
Day
BILLING
& Customer
Service
Month
BILLING
Occasional
Year
N xN xN xN >500xN
Here I show from our experience, what is the ideal frequency to get meter data, depending on use case.
Residential Meter
C&I Meter
DMA Meter
Payload in bytes
Amount of Data

10. QUESTIONS?