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Perform Data Enrichment Deliverable Template

From MIKE2.0 Methodology

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This article is currently Under Construction. It is undergoing major changes as it is in the early stages of development. Users should help contribute to this article to get it to the point where is ready for a Peer Review.
This deliverable template is used to describe a sample of the MIKE2.0 Methodology (typically at a task level). More templates are now being added to MIKE2.0 as this has been a frequently requested aspect of the methodology. Contributors are strongly encouraged to assist in this effort.
Deliverable templates are illustrative as opposed to fully representative. Please help add examples to this template that are representative of the proposed output.

Overview

Performing Data Enrichment typically refers to the supplementing of an organisation’s internal data with data from external sources. Types of data that is typically used for enrichment data:

  • Personal data such as date-of-birth and gender codes
  • Geographical data
  • Postal Data, such as Delivery Point Identifiers (DPID)
  • Demographic information
  • Economic data
  • World event information
Center

After data has been standardised, corrected and matched, enriching data is basically the same as adding other source data.

Key Deliverables for Data Enrichment include:

  • Design for enrichment
  • Changes to data model and meta-model
  • New data load (refer to ETL solution approach)

Steps in the Process

Step 1 Determine requirements for Data Enrichment
Objective: Understand the business needs and the proposed benefit provided by external data
Input: Functional requirements that apply to needs for Data Enrichment
Process: Key Steps in the Process include:
  • Understand gaps from existing data in mapping to business requirements.
  • Define business value provided by supplementary data and how it maps into business requirements.
  • Define options for existing data feeds.
  • Provide estimated cost-benefit analysis from purchasing additional source data. This should be high-level and presented to the team.
Output: Selected sources for data enrichment



Step 2 Source Extract Definition and Design
Objective: Determine source to be used, how often it will need to be reloaded and whether it may contain any potential data issues.
Input: Sources for data enrichment
Process: Key Steps in the Process include;
  • Determine frequency of source data loads
  • Determine whether data loads will be primary set of information in the organisation or whether it needs to be checked against an authoritative source
  • Determine method for receiving data (extract load, external connection, etc.)
  • Determine frequency of source data loads
  • Refer to Data Investigation process for definition of source system extracts
Output: Source extract definition and logical design of extract



Step 3 Update Target Model Design
Objective: Update target data model to reflect any requirements for enrichment data
Input: Functional information requirements
Process: The target data model should be defined as part of the initial requirements, but may require some minor changes (especially at the physical level) to accommodate new data load.

Follow the data modeling process for this area.
Output: Extensions made to target data model



Step 4 Supplement initial data with enriched data
Objective: Load of enrichment data into target environment
Input:
Determination of source extracts
Target Data Model design
Extract Logical Design
Process: Follow the ETL solution process for this area
Output: Enrichment data added to core data



Examples

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