Data Re-Engineering

From MIKE2 Methodology

Contents 1 Activity: Data Re-Engineering 1.1 Objective 1.2 Major Deliverables 1.3 Tasks 1.3.1 Prepare for Data Re-Engineering 1.3.2 Perform Data Standardisation 1.3.3 Perform Data Correction 1.3.4 Perform Data Matching and Consolidation 1.3.5 Perform Data Enrichment 1.3.6 Finalise Business Summary of Data Quality Impact 1.4 Core Supporting Assets 1.5 Yellow Flags 1.6 Key Resource Requirements 1.7 Potential Changes to this Activity 1.8 References

Activity: Data Re-Engineering

Objective

Data Re-Engineering is a term used to describe a number of related functions for standardising data to a common format, correcting data quality issues, removing duplicate information/building linkages between records that did not exist previously, or enriching data with supplementary information.

The MIKE2.0 approach for Data Re-Engineering derives some of its ideas and language from the TIQM Methodology proposed by Larry English ^[1]for Data Re-Engineering. TIQM provides a very comprehensive approach to preventing and resolving Data Quality issues and is recommended as a complementary reference guide for users of MIKE2.0.

Data Re-Engineering is not always conducted within Phase 3 of the MIKE2.0 Methodology, it is an activity that may be conducted throughout the process. In many cases, however, it does make sense to try and address the major data issues before moving data into the target environment.

Major Deliverables

• Design, metadata assets and actual data changes that apply to the following steps:
  • Data Standardisation
  • Data Correction
  • Data Matching and Consolidation
  • Data Enrichment
• Business Case for addressing overall Data Quality issues

Tasks

Prepare for Data Re-Engineering

Objective:

In this task, the team prepares for Data Re-Engineering by ensuring that at least high level information requirements have been established, data extracts are available and the software development environment is ready. Data Profiling is typically a pre-requisite to this task, as it helps to quantitatively understand the data quality issues that exist beforehand and plan appropriately. Generally the same process for acquiring extracts for can be followed for Data Investigation.

Input:

• Detailed Business Requirements for Increment
• Data Profiling (typically)
• Configuration Management Baseline
• Development Environment Upgraded

Output:

• Relevant file extracts for Re-Engineering

Perform Data Standardisation

Objective:

Data Standardisation addresses problems related to:

• Redundant domain values
• Formatting problems
• Non-atomic data in complex fields
• Embedded meaning in data

The Data standardisation process is used to get data into an agreed-to atomic form, oftentimes mapping in data from complex fields using a vendor tool. Mapping rules from the standardisation processes are ideally fed into a metadata repository.

Input:

• Detailed Business Requirements for Increment
• Final Data Quality Assessment Report
• Preparation for Re-Engineering
• Target Data Model

Output:

• Metadata mappings from source to standardised model
• Standardised data from source systems in staging environment

Perform Data Correction

Objective:

Data Correction typically addresses problems related to:

• Missing data
• Value issues due to range problems
• Value issues related non-unique fields
• Temporal or state issues
• Name and address data that can be referenced against existing reference sets

Input:

• Detailed Business Requirements for Increment
• Final Data Quality Assessment Report
• Preparation for Re-Engineering
• Target Logical Data Model and Physical Data Model
• Standardised Data

Output:

• Cleansed Data
• Metadata Mapping Rules for data cleansing

Perform Data Matching and Consolidation

Objective:

In this task, data is associated with other records to identify matching sets. Matching records can then either be consolidated to remove duplications or linked to another to form new associations.

Input:

• Detailed Business Requirements for Increment
• Final Data Quality Assessment Report
• Preparation for Re-Engineering
• Target Logical Data Model and Physical Data Model
• Standardised Data
• Corrected Data

Output:

• Overall Match Criteria with Business Impact
• Matched/Consolidated Data
• Metadata Mapping Rules for data matching/consolidation

Perform Data Enrichment

Objective:

Data Enrichment typically refers to the supplementing on 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

This provides an overall more robust set of information to make key business decisions

Input:

• Detailed Business Requirements for Increment
• Final Data Quality Assessment Report
• Preparation for Re-Engineering
• Target Logical Data Model and Physical Data Model
• Standardised Data
• Corrected Data
• Matched/Consolidated Data
• Extracts of Enrichment Data

Output:

• Design for Enrichment
• Changes to Data Model and Meta-Model
• Existing data supplemented with enhanced data

Finalise Business Summary of Data Quality Impact

Objective:

This task provides a summary of the root causes of the data quality issues that impact the business and recommendations for resolution of these issues. Recommendations may involve changes to source systems, improvements to business processes, increased validation, etc. The report produced should also involve financials to build a business case around resolution of these data quality issues and whether it makes sense to address them from a business perspective.

Input:

• Data Quality Report
• Data Re-Engineering Results
• Project Plan, Project Costs for Data Investigation and Re-Engineering
• Proposed Cost to fix data

Output:

• Summary of Data Quality Impact

Core Supporting Assets

• Data Investigation and Re-Engineering Solution provides a holistic approach to Data Investigation and Data Re-Engineering and makes reference to specific process steps to Data Investigation. It makes reference to logical and physical best practice.

• The SAFE Architecture provides an overview on a Data Quality Architecture that includes conceptual component definitions, physical characteristics and high-level solution architecture best practices

Yellow Flags

• Root causes to data quality are not resolved - the core problems still exist
• The introduction of automated data quality rules could result in high degrees of load failures
• Re-engineering on one system will result in reconciliation issues with other systems

Key Resource Requirements

• Information Architect
• Business Data Analyst
• Technical Data Analyst
• Data Quality Manager
• Data Stewards
• Data Owners

Potential Changes to this Activity

A few tasks may need to be added to this activity that are more commonly applied for Search. In particular: lemmatisation and spell checking that are commonly applied through a human interface. Alternatively, may expand the definition of standardisation to cover this area.

References

1. ↑ Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits. Larry English (John Wiley & Sons Inc, 1999)