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Kim Duckworth New Zealand Ministry of Fisheries The application of standardised data quality improvement methodologies to data describing marine fisheries and biodiversity. Why this topic ?. - PowerPoint PPT PresentationTRANSCRIPT
Kim Duckworth
New Zealand Ministry of Fisheries
The application of standardised data quality improvement methodologies to data describing marine fisheries and biodiversity.
Why this topic ?Because it is easy to forget that disciplines other than our own also have information quality problems; and
Because information quality is what I am passionate about.
Content:The management of marine biodiversity and fisheries information in NZStructured information quality improvement methodologies:
A few definitionsThe main concepts
How we (NZ Ministry of Fisheries) have applied structured information quality improvement methodologies.
Fisheries and biodiversity information management in NZ
One group controls the majority of NZ’s fisheries and marine biodiversity information
Commercial catch logbook (“Catch Effort”)
Fisheries observer (about 20 types of information)
Distribution information (on GIS systems)
Trawl survey
Acoustic survey
Fish length frequency
Fish aging
Information brokerage
Information producers
Information Analysers (decision makers)
Fisheries and biodiversity information management in NZ
NZ effectively has a national archive of fisheries and marine biodiversity data. Possibly this has meant that accessibility and interoperability have been less of an “issue” in NZ then in many other countries.
The big issue for the management of NZ’s fisheries and marine biosecurity information has been improving information quality.
Information qualityIn New Zealand there are approximately 30 people employed (full time) on improving the quality of fisheries and biodiversity information.“Poor data quality is the norm rather than the exception, but most organisations are in a state of denial about this issue” (GartnerGroup, 1997)
The management and improvement of information quality is slowly becoming a discipline (and profession) in itself.
DefinitionsData -
A representation of a thing or event in the real world
Information – Data in context (the meaning of data)
Information quality –How closely the representation matches the thing or event in the real world,
DefinitionsData -
A representation of a thing or event in the real world
Information – Data in context (the meaning of data)
Information quality –How closely the representation matches the thing or event in the real world, given the purpose(s) for which the data is being collected.
ImplicationsA key aspect of our information quality improvement programmes is to establish and document the purposes for which the information will be used;
Data can simultaneously be of both high and low quality;
For us to provide someone with information we must give them with both data and context.
Definitions – characteristics of information quality
Accuracy
Precision
Completeness
Non-duplication
Timeliness
Currency
Format
Context
“Rightness”
The information production chain
DecisionStart of production
The information production chainA (simplified) commercial catch logbook example:
Create logbooks and create codes for use on logbooks,
Create explanatory notes & train fishers
Fishers fill in forms
Fishers post forms to a central location
Data entry staff enter data
Computer systems check and “correct” data
Humans check and “correct” data
Store data in database
Extract from database
Analyse and interpret data
Implications:
Planning and action needs to be on the basis that all weak links in the chain are identified and acted on. For example –
With regard to NZ’s fisheries observer data we have identified over 100 purposes for which the information is used, 482 issues with the status quo and 33 projects which (if implemented) should address those issues.
The methodology
Assess information quality
Clean existing data
Improve the processes that produce data
Assess cost/risks of non-quality
Improving the processes that produce data
Analyse root causes of errors. Minimise the things that produce errors. Prevent re-occurrence .
For example – In NZ we are redesigning catch logbook forms specifically to make them “harder to get wrong”.
Form redesignPrototype forms were tested on “real fishers”
Write the month and year on which you fished
Form redesignPrototype forms were tested on “real fishers”
Write the month and year on which you fished
Write the month (e.g. FEB) and year on which you fished
Context
Three examples from NZ of projects to help decision makers understand the context of data:
Reference library CD for commercial catch logbook dataInformation interpretation system for commercial catch logbook dataSchematic form used to represent species distribution data on the Ministry’s marine biodiversity GIS
Catch Effort reference library
Created because decision makers were having trouble getting hold of the documentation that they needed in order to make sense of the data.
The Catch Effort reference library:
is a website that runs off a CD
provides a “one stop shop” for everything that a decision maker might ever want to know regarding how Catch Effort data is collected, processed, stored and managed
contains the equivalent of 500 pages of documentation
Information Interpretation SystemArose as a consequence of implementing a decision maker query-able data warehouse, and concerns that decision makers would not understand the context of the data;
IIS is an application that stores (in a separate database) known “issues” with Catch Effort data, and retrieves relevant issues in parallel with extractions of data from the data warehouse;
Decision makers cannot turn IIS off. They can prevent individual issues being re-displayed within the next 6 months.
Information Interpretation System – example search
Information Interpretation System – example of results
NABIS
The National Aquatic Biodiversity Information System
A queriable internet based GIS storing information about “what lives where”
Aimed at:Decision makers who are not experts in marine bio-diversity
The general public
Scientists
Conclusions
One prerequisite for information quality improvement is knowing the purpose(s) for which the information will be used;It is important for decision makers to be provided with “context” as well as data;Measure information quality; Assess costs/risks of “non-quality”;Address root causes of problems.
The end
Questions ?