The Emissions Trading Scheme (ETS) is used by the New Zealand government to meet domestic and international climate change targets. The forestry component of the ETS is administered by MPI, who use the Climate Change Information System (CCIS), a bespoke web-based tool, to do this administration. The CCIS records and allocates carbon units based on registered forestry activity, and the CCIS is a key feeder of data for the forestry component of the ETS, the data quality of the CCIS is of great importance to MPI.
MPI is upgrading the CCIS to a tool that better meets their future state business requirements—an upgrade which includes a complex data migration. Harmonic were commissioned to perform an independent data quality assessment of the CCIS; specifically, to make recommendations in preparation for a system upgrade and data migration of the CCIS over the coming years. In addition to the typical review of data for this type of project, MPI also wanted Harmonic to assess the CCIS’s data structures and data flows.
Harmonic’s data quality assessment was conducted over thirteen weeks and tailored to MPI’s data scope, data structure, and business requirements. Harmonic’s approach involved three key stages:
Stage One: Initial data quality analysis
Stage Two: Detailed Analysis
Stage Three: Conclusions and Recommendations
Harmonic collaborated very closely with a MPI focus group consisting of stakeholders and subject matter experts to test any assumptions and check our progressing recommendations. Stage one captured the entire scope of the CCIS to understand all of the known data quality issues. The interviews and analyses of stage two enabled Harmonic to better understand and clarify any observed irregularities in the data. Stages one and two also involved several informal presentations of Harmonic’s evolving findings in a ‘show and tell’ manner, and interim reports to better refine our recommendations. Stage three presented Harmonic’s findings and recommendations through a presentation and as a written report. The recommendations addressed both data cleansing and data preparation for smooth future data migration, and each included: the priority; the effort; the cost; and the specific steps for implementation.
The benefits for MPI include:
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