IACS 24th JWG/CS meeting
Tuesday, 11 April 2023

What data quality recommendation do we need?
-from ship owner’s perspective2023年4月11日

Putu Hangga, Jungo Shibata
Monohakobi Technology Institute

NYK Group

A Member ofIntroduction
The future of ship system will be verymuch linked to other part of supply chain
system (Smart Ship).
The concept of Smart Ship is to utilize
various data and applications to achieve
optimum ship operation in terms of safety
and energy efficiency.
Effort to realize the open platform
concept for data sharing in maritime
industry is steadily progressing.
The quality of data will become asimportant as what data is being collected
and shared and how the data being
collected
In Japan, shipping companies have been discussing and responding to the question of how tomanage ship data quality for the past several years.
Today, as member of Smart Ship Application Platform project (SSAP) we would like to introduce ourefforts in ship data quality management from the viewpoint of shipping companies.

We are planning to use this initiative as a basis for discussions on data quality management in theSSAP, involving shipyards, shipbuilders, equipment makers, maritime solution providers as well asclassification societies.
We hope that the contents of today's presentation can be of reference in order to make the IACS
data quality recommendation guidelines more realistic and better in the future.

Standardized IoS Platform (ISO 19847, 19848)
As-Is

To-Be
Onshore Data Center / Onshore Apps

Cabling and interfacing one by one

Ship System

Ship Application

Ship System

Ship Application

Optimum
Trim

Optimum
Trim
VDR

VDR

Performance
Monitoring

Performance
Monitoring
ECDIS

ECDIS

WeatherRoutingEngine
Data Logger

OtherSystems

Onboard
DataServer

Weather
RoutingEngine
Data Logger

EngineMonitoring

RemoteMaintenanceEngineMonitoring

OtherSystemsRemoteMaintenanceSource : JSMEA

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Data can be used for many things, more when combined
Data Viewer Application

IoT Data

Data Analytics Application

Everyone knows the potentials

Crew inputted data
Many seems to knows what are the bottleneck(e.g e-UMS)

Many seems to not realize where the problem lies
Everyone want to solve the problem

Used for many value-added
activities
Digital SMS

Shore Analysis Tools

Onboard tools

5CHAPTER 2Pain points: actual data quality problem

What separates dreams from reality: Data Quality
Data Error Chain → Data collection and input errors in the top level affect Decision Making.

Effort required
to repair
Bad Master
Data &ReferenceEx：
Ship reference
data (drawing,particulars, etc)

Degree of impact
to businessBad sensor
data value

Ship masterdata

Bad Analysis
Data

Bad Analytical
Display

Bad Decision
MakingData-driven
analytics

Smart Viewer
For end-user

Analytical
Tools

Garbage In ≈ Garbage Out
If the data source is incorrect, theWrong and unnecessary data will accumulate day by day.
7Data quality issues (Bad Master data)
Even if the data sent from the ship is correct, the calculation data using it will also bederanged due to the mistake of the master data input.
For example, if MCR (kW) of main engine is wrong, value of the main engine load(%) isCorrect) Actual horsepower 16,000 kW ÷ 18,310 = 87%
False)

Actual horsepower 16,000 kW ÷ 28,310 = 57%

There will be a big difference.

Mistaken for 28,310kW

Mistaken unit for 6bar?

Mistaken for a six-cylinder?

5,800mm and single-digit typo?

8Data quality issues (Bad Sensor Metadata)① Both channels are named M/E No.1 AUX BLOWER (auxiliary blower), but channel40240
indicates “auxiliary blower is RUN or STOP”. The lower channel indicates “auxiliary blower isnormal or abnormal”. When we monitor whether the auxiliary blower is running or not, if
we connect/link the lower channel, it will show different movement.

①4024045011

M/E NO.1 AUX BLOWER
M/E NO.1 AUX BLOWER-Machinery Main Engine Aux. Blower
Machinery Main Engine Aux. Blower

RUN,STOPNORMAL,ABNORMAL② Both are same “M/E CRANKCASE OIL MIST DETECTOR”. But the top is thechannel that indicates whether this system is working normally and the bottom
is the channel that indicates whether the level of Oil Mist level has not increased.
Whether the system is Normal or Abnormal②4031240427

M/E CRANKCASE OIL MIST
DETECTOR
M/E CRANKCASE OIL MIST
DETECTOR

Machinery Main Engine Status

NORMAL,ABNORMAL

Machinery Main Engine Status

NORMAL,ABNORMAL

Whether the oil mist level is normal or high (Abnormal)

In this way, there are different channels with the same name, so it is necessary
to distinguish them carefully.

9Data quality issues (Units, Digits issues)
As the data, pressure unit may be MPa or bar, and number of digits such as flow meter,rpm, running hour, shaft horsepower, etc. varies, so it is necessary to unify them into one.

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Data quality issues: Just a bad sensor value
To develop solutions, we need to know what we are up against[1] Misleading ship information.

[2] Data transmission delays.

33%

36%

Data for condition
monitoring is not fresh

31%

Affect commercials decisionin scheduling[3] Data inconsistencies
between databases.
Number of complete records in eachdatabase

Affect robustness of data-driven solution[4] Decision-making becomes
more difficult
Root causes of unclear maintenancejudgment when using poor data
Reference Data
Error
3%

100%
50%
0%
Ship A
Upstream DB

Ship B

Ship C

Downstream DB

Confusion amongst different divisions< 6 hrs
6-24 hrs> 24 hrs

Thresholdproblem
60%

Vague Operation Mode
2%
Vague
Deceision

Sensor Data
Quality35%

Affect decision making for safety purpose

11Data quality problem along the pipelineIt seems that even if we fix in one end, data quality may come up in the other end
Data may be lostalong the way
Do we have standardof data model(metadata, attributes)

Extract, Transform,Load

VDR

DataLogger

OtherInternal
SystemETL

ETLTransform

How do wecontrol
Third party SLA?

Enterprise
DataModel

Is the same data
model being usedonshore?

Data may not be
consistent anymore

Enterprise
Data
Warehouse

Enterprise
Data
Warehouse

Individual costrequired to transform
disparate dataTDataMartT

DataMart

Significant risk
Of how data isunderstood & used
ApplicationTETLSHORE
SHIP AGGREGATOREach project/application
May have different
approach

Is SLA already in place
To ensure quality ofdata?

Data
Sharing/Trading
Platform

Other Company user

Are these analysis
reliable?

ACTORS
Platform User (i.eData collector)

Extract/Call API

Platform Provider(e.i. DataAggregator)

Solution Provider
Extract/Call API

Solution User
Extract/Call API

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CHAPTER 3

Pain points: standard and framework

Data Quality Management Framework are confusing
Especially for ship owner/operator, data quality improvement is requiring enormous
amount of effort to digest and implement,
due to various, often overlapping or conflicting rules, standard, guideline, andrecommendation available

Process A1
Ops A

BusinessOps B
Ops C

e.g Utilizes
Is there any guideline that are
Ship IoT Dataflexible to be used, yetcomprehensive enough?

Process A2
Process B1
Process B2
Process C1Process C2

Industry A guideline

Industry B guideline

Data quality
improvement onenterprise level

Industry C guideline

e.g Condition monitoring

Ship OneClassification A Guideline

Ship TwoClassification B Guideline

Data quality improvementon ship level14Now that we know the pain, where to startRather than reinventing the wheels,
There’s already several existing standard for Data Quality, e.g. ISO 250XX & ISO 8000-XX
And a guide to international data management standards and practices such as DAMA DMBoK

There’s already also several existing Data Quality guideline by classification societies

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Data quality metrics in various Standards: what to expect
ISO/IEC 25012 has 15 metrics

ISO/IEC 20547-3:
Big data reference architectureHas 18 metrics(mostly similar toISO 25012 Metrics)

Although there are many in ISO 8000-8.
8 Metrics was mainly recommended

There are many aspects of data quality, and somehow
Each maritime organization may decide on appropriate to use DQ
dimensions that impacted their business.
(Business-driven metrics)
However, industry player still need a guidance because metriccalculation alone do not solve data quality problem

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Example of how ship owner currently combine guidelines

Due to non-existence of flexible guideline: Ship owner tends to combine various standards bythemselves which are very business driven
ISO’s are usually referred
until this part

DQ Dimension（macro characteristic）

ISO 8000Characteristics

ISO/IEC 25012

Do you receive all records in one day?

All complete

Partly complete

All incomplete

Properties
Class guideline
Or decided by own

This part is particularly
flexible to be decided oneach use case

Basemeasures
Decided by own

Are business critical features missing values?

Business
Rule A

Business
Rule B

Business
Rule C

Tracker that are activity-linked•••

When did the data start missing?
Is it intermittent or continuous?
Which sensor type is missing?

•••

Which sea area did they disappear ?
What about incomplete trends over time?
etc.

© 2021. MTI Co., Ltd. All rights reserved.

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Conclusion
Data quality problem can emerge anywhere in the data flow pipeline, from master data
to data used by a software solution

There might be no one-size-fit-all standards to be referred to, but ship owner’s need aguidance still (some might already figure it out, some haven’t).
Data quality activity and the reference ISO does not belong only to one or two actors,
but rather all actors that utilizes the data.
• If it’s ship owner, maybe ISO 8000 (easier to link to ISO 9000)• If it’s software company, maybe ISO/IEC 25012• If It’s AI company, maybe ISO/IEC AWI 5259-2

Flexible and Action-Enabled Recommendation is needed

We from shipping company side are more than willing to contribute to this activity inorder to IACS data quality recommendation guidelines more realistic and better in thefuture.
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Thank you for your attention