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 ?