Aeronautical Data 2-Day Workshop

1. Aeronautical Data 2-Day Workshop Module: 4 Data Chain (from Source to Use)… or Data Cycle? strategic airspace November 2011

2. 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

3. 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.

4. 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

5. The Data Chain / Cycle Aeronautical Information Management • Ref: Doc 9906 Vol I (Quality Assurance Systems)

6. Data Chain in the IFPD Process Source: ICAO Doc 9906, Vol 1

7. 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

8. 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.

9. Metadata Attributes DataProvenance • 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.

10. 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.

11. Metadata Attributes (cont’d) • Data Quality • Resolution – number of decimal places; • Accuracy – does it meet required standards for purpose; • Confidence Level – howcertain are we aboutaccuracy; • Integrity – can data be relied upon; • Criticality • Definition of required data integrity (in terms of probability of errors) for particular purpose.

12. 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.

13. 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.

14. 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