White Paper: Data Standardising & Loading
COSOL WHITE PAPER 2
Data Standardising & Loading
This paper is the second in a series summarising lessons learned, to provide insight for executives who are faced with a possible large-scale program of this nature within their own organisation.
Our point of view
Experience shows that one critical success factor for these programs to realise the value in their business cases is data migration. The axiom “garbage in, garbage out” remains as relevant today as it did when computers were first invented. COSOL strongly believes:
- That digital transformation is well underway, and every board is, and should be worried about how to become a truly digital enterprise.
- Strong enterprise data foundations will be required to enable adoption of digital solutions including advanced analytics, robotic process automation, machine learning and artificial intelligence which are the next frontiers to productivity and market competitiveness.
- Enterprise data is the glue, the fact base, that drives decision making and business improvement, allowing organisations to meet stakeholder expectations in a timely and efficient manner; and
- For organisations to succeed, data must be treated as a mission critical asset; it is the single biggest success factor in a digital transformation journey, and most organisations are ill prepared due to many islands of disconnected data that is of unknown and/or poor quality.

Introduction to data migration
Data migration can best be explained as three distinct phases
- Pre-migration: data profiling and remediation (White Paper #1)
- During migration: data standardization and loading (White Paper #2)
- Post migration: data reconciliation and archiving (White Paper #3)
Recapping the key takeaways from the pre-migration:
- Data will typically be exposed as a major risk and cost in large scale digital transformations due to unknown and/or poor quality
- Strong data ownership and data governance is critical; and
- Commence data profiling and data remediating as early as possible
With these insights, this white paper examines the phase during migration where data is standardised then loaded into the target system(s).
As can be shown in Figure 2: Data Standardising and Loading, the six dimensions of data quality covered in the previous white paper will continue to be remediated throughout this phase. This reinforces the importance of the key takeaway about strong data ownership and data governance, and serves as a reminder also from the previous paper that “this Is a business issue first and foremost and that the business must take ownership and accountability of its data as a strategic asset”. The project team must have empowered wwners in place to make decisions about their data for the program to succeed.

“As a “domain”, Data should and can be managed before, during and after a major program. This strengthens organisations overall digital capability and mitigates future risks and costs by ensuring data remains evergreen.” – White Paper #1
Program Governance
With the art of migrating data now covered, attention must turn to the key decision of how this “domain” is organised within transformation programs managing consolidation and standardisation of systems.
The pictures below elevate the focus from the data migration method, to the Organisational Units, and “Assets” of each unit within the scope of the program.
Mock | Objective(s) | Target |
---|---|---|
0 | Identify as many quality and performance issues as possible to inform the project plan | 100% of key load programs run with as large a dataset as possible |
1 | Successfully prove key load programs and test mapping for key objects on limited data | 100% of key load programs successful 10% of data loaded successfully |
2 | Test key data mappings, refine key relationships and define pre and post validation criteria | 50% of data loaded successfully |
3 | Go live dress rehearsal and confirm cutover timings and load sequencing | 100% of data loaded successfully |
Figure 3: Outcomes of each mock run improves with each cycle of remedies
Mock | Objective(s) | Target |
---|---|---|
0 | Identify as many quality and performance issues as possible to inform the project plan | 100% of key load programs run with as large a dataset as possible |
1 | Successfully prove key load programs and test mapping for key objects on limited data | 100% of key load programs successful 10% of data loaded successfully |
2 | Test key data mappings, refine key relationships and define pre and post validation criteria | 50% of data loaded successfully |
3 | Go live dress rehearsal and confirm cutover timings and load sequencing | 100% of data loaded successfully |