Aeronautical Data 2-Day WorkshopAeronautical Data 2-Day WorkshopModule: 4Module: 4

Data Chain (from Source to Use)… or Data Cycle?Data Chain (from Source to Use)… or Data Cycle?

strategicairspace

November 2011

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Overview Overview of Data Chain

Refer to: WGS84 Manual (Doc 9674), FPD Quality Assurance Manual (Doc 9906 – Vol I), Guidelines for eTOD (Doc 9881)

How to control the data chain / data cycle SMS and QA System

Implementation of Standards;

Use of Metadata; and

Defined Control Processes

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Overview (cont’d) Metadata Details

General principles – how, what, when, where, why and by whom;

Provenance – information about the data source (or pedigree) ;

Custodianship – who owns the data and what is update cycle;

Audit Trail – tracking changes and usage; Quality – knowing the accuracy and ensuring

integrity; How is data used – requirements of different

applications; and Temporal aspects – when is data valid.

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Overview (cont’d) Processes

Verification – checking data for consistency and accuracy with source;

Validation – checking the data against real world;

Ensuring software accuracy –software validation;

Data Exchanged – transferring data to other users

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The Data Chain / Cycle Aeronautical Information Management

Ref: Doc 9906 Vol I (Quality Assurance Systems)

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• Data Chain in the IFPD Process

Source: ICAO Doc 9906, Vol 1

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Controlling Data Safety Management and Quality

Management Systems Procedures for Data Control should be part of

these systems

Implementation of Standards Provides benchmark to compare against Supports uniformity

Use of Metadata Data about the data provides:

Suitability for purpose; Assurance of quality;

Defined Control Processes Provides check points for data

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Metadata General principles:

Stores information about the data with data items Provides means of assessing quality (against standards),

appropriateness for purpose and facilitates data checking

Attributes: How - was data captured and processed (data product

specification), how was it input; What – is defined accuracy of data; When – when was it originally captured, when was it

processed and when was it imported into system, when was it changed, what period is the data valid for, when is data likely to be updated;

Where – what area does a dataset cover; Why - was the data captured; and Who – captured the data, processed it, owns it, provided

it, entered it, checked it, updated it.

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Metadata Attributes Data Provenance

General information about the dataset Where does it come from, how was it captured /

processed, when was it captured / processed / provided;

Provides general information about the data; Can be encapsulated in Data Product Specification

and Data Source;

Custodianship Who owns the data;

What are restrictions on usage if any (can it be provided to others);

Charges for usage – are there any costs; Security restrictions – is data restricted in any way.

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Metadata Attributes (cont’d) Audit Trail

Need to keep changes to data over time – to establish what was source data in previous usage;

Need to record when data was changed – to find links to data derived from old data

Need to record how data was changed and by whom – in case there are systematic or random errors, so corrective action can be taken;

May record why data was changed – simple correction (to typo) or updated / more accurate information;

May want to record when data was checked and by whom; and

Need to record what data used source data.

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• Metadata Attributes (cont’d)

Data Quality Resolution – number of decimal places; Accuracy – does it meet required

standards for purpose; Confidence Level – how

certain are we aboutaccuracy;

Integrity – can data be relied upon;

Criticality Definition of required data

integrity (in terms of probability of errors) for particular purpose.

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Metadata Attributes (cont’d) How is data used

What data is derived from particular data items Links with other data items

Temporal Information When will / did data become valid; When will / did data cease to be valid; and May or may not be tied to AIRAC cycle dates.

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Quality Processes Data Checking

Verification: checking consistency – is data item valid in context

of other data; checking appropriateness – is data item appropriate

(have right level of data quality) for purpose; and derived results – are calculation results consistent in

context. Validation:

checking against real world.

Software Checking Validation:

Checking results of calculations against external calculations.

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Data Exchange Best to use standards

However, different standards have different purposes AIXM ARINC DAFIF Proprietary (DGN, DXF, SHP, etc)

Need to ensure integrity during exchange Agreed field formats (length, accuracy,

separators) CRCs