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 NZ Structured information quality improvement methodologies: A few definitions The 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 quality In 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.

Definitions Data - 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,

Definitions Data - 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.

Implications A 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 Decision Start of production

The information production chain A (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 redesign Prototype forms were tested on “real fishers” Write the month and year on which you fished

Form redesign Prototype 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 data Information interpretation system for commercial catch logbook data Schematic 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 System Arose 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 ?