Successfully overcoming obstacles in data transformation

Data transformation projects are unique and stand out from other software implementation efforts due to several distinctive features and peculiarities. Transforming data is essential to achieving business objectives, gaining valuable insights, and improving data quality. This comprehensive project brings many challenges, making thorough preparatory work crucial.

Be Prepared for Changes and Downtime

Data transformation projects must be resilient to unexpected changes in data structure and format. This requires a specialized technical toolkit and an experienced team. Handling large volumes of data influences the time needed for data profiling, transformation logic definition, and the necessary downtime during the go-live phase. Data migrations often require temporary system downtimes during deployment, potentially affecting business operations. These are usually scheduled over weekends for a 48-hour period. During this time, users must typically log out, and regular system jobs such as archiving must be disabled to prevent data manipulation during the migration, optimize system resources, and ensure fast data processing. These factors highlight the importance of careful planning and coordination to avoid budget overruns, project delays, and other disruptions. Coordination among the various stakeholders, including system integrators, technical teams, business users, and compliance experts, is crucial for achieving alignment and success.

Keys to Success

Know Your Data
The principle of "garbage in, garbage out" underscores the critical importance of data quality for successful data transformation endeavors. Early data profiling is crucial for project success. The earlier our technical and functional team can analyze the system, the better. Before, during, and after the migration, Natuvion conducts technical and data-related reviews using the Natuvion Data Conversion Server (DCS) toolset. This provides detailed comparisons of table sets, data relationships, and financial KPIs.

Clean Your Data
Before embarking on a data transformation, it is advisable to clean and standardize the data. This practice helps minimize errors and ensures high data quality in the new system.

Structure and Scope
Creating and approving an initial project charter is essential to align expectations and lay the foundation for the project. This document should cover key aspects such as scope, milestones, governance, meetings and schedules, roles and responsibilities, and reporting formats. Defining these topics during the project's setup phase can effectively mitigate potential issues arising from misunderstandings. Detailed definitions of data selection and acceptance criteria are crucial. They serve as the project's backbone and guide decision-making processes to ensure the right data is selected for transformation.

Appropriate Expertise
There are various types of data transformation projects, each with its own requirements and challenges. Common types include carve-outs, deletions, mergers and acquisitions (M&A), and data anonymizations. Successful data transformation projects require a multidisciplinary team with a mix of functional and technical skills, aligned with clear project deliverables and governance structures.

A symbiotic relationship between project management and the technical architecture team is central to successfully executing data transformation projects. Project management ensures the alignment of project goals, timelines, and resources, while technical architecture staff provides the expertise needed to design and implement robust data solutions. Together, they navigate complexities, mitigate risks, and drive the project toward its goals.

Teamwork
Collaboration between global teams is crucial for project success. Clear planning and disciplined execution are key components of this process. Utilizing teams in different geographic regions and time zones can add significant value to projects but requires precise coordination, clear planning, and disciplined execution.

Planning
Detailed planning is essential in transformations and encompasses both project and go-live plans. Particularly important in transformation initiatives is the meticulous planning of timing and dependencies for the go-live phase, along with detailed measurements of the duration of each task involved. This plan serves as a foundational document, continuously refined and used in all migration runs to develop a mature and predictable strategy for the final go-live. Adhering to this structured approach helps mitigate potential risks and optimize project success. During the early planning phases, it is crucial to include a sufficient number of transformation runs and validations to effectively mitigate risks. This helps identify and proactively address potential issues and challenges early on. This approach not only minimizes the likelihood of errors but also ensures a smoother transition during the later phases of the project and generally reduces the overall project risk.

Data Tracking
It is crucial to meticulously document the number of records moved or deleted during the transformation process in a detailed format. This typically involves tracking changes at the table or object level and supplementing this information with comparable relationship tables and financial analyses to determine the "before and after" status of the data. This comprehensive documentation provides valuable insights into the scope and impact of the data transformation, enabling thorough analysis and review of the transformation's effectiveness and accuracy.

Testing
Establishing acceptance criteria for tests and reviews, such as reports, should be prioritized early in the project lifecycle. This ensures clarity on what constitutes a successful test or review and helps set the right expectations with stakeholders. Rigorous functional and process-related regression testing may be required to ensure the reliability and stability of the data transformation process. Different test cycles may require different validation methods tailored to the specific needs of each cycle. This approach helps ensure that the final outcome meets the desired quality standards. During initial transformation runs, technical-functional staff resources could be responsible for testing to evaluate the functionality and performance of the data transformation process. However, once the data has stabilized and reached a consistent state, it is important to involve business users in the formalized testing process. Business users can provide valuable insights into the accuracy and completeness of the transformed data from a practical perspective, ensuring it meets their specific requirements and expectations. Ultimately, it is crucial that business users validate the data quality to confirm its suitability for operational use and alignment with organizational goals.

System Performance
Performance should be a top priority for all stakeholders. Companies can begin by conducting performance measurements at the start of the project to assess metrics and validate the system configuration. They can also work with teams to monitor and optimize system usage throughout the project.

It is important to validate the system configuration both during the project and before go-live to ensure consistency and reliability in performance standards. The Natuvion DCS toolset enables the configuration and balancing of system resources against processing capacity to ensure efficient usage. However, support from the SAP Basis team is essential during processing to effectively monitor and adjust system usage. Their expertise is invaluable in optimizing system performance and addressing any technical challenges during data transformation projects.

Change Management
Companies should not forget to establish a change management process and agree on which changes will incur costs and which will not. Documenting all changes, regardless of cost, ensures transparency and accountability within the project. This comprehensive approach helps optimize decision-making, ensure proper resource allocation, and minimize the risk of overlooked or undocumented changes.

Formal Approvals
Obtaining formal approvals is essential to ensure clear documentation and alignment throughout the project. This process ensures that all stakeholders are aware of and agree on the project's goals, scope, and requirements. Obtaining formal approvals also provides a mechanism for accountability and helps avoid potential misunderstandings or disagreements later on.

Escalation Management
Setbacks in a project are inevitable. A proactive approach ensures the project stays on track despite unexpected challenges and allows the team to overcome obstacles efficiently.

Deployment Phase (Go-live)
The go-live must be carefully planned and executed, especially when different countries and time zones are involved. Creating a comprehensive go-live plan is very helpful and should start early in the project timeline, refined through multiple test runs of the migration. During the final rehearsal before the actual deployment (known as the general rehearsal), all aspects, including system configuration, team plans, and task schedules, must closely align with the planned go-live. During the go-live phase, it is important to be prepared for potential malfunctions. An emergency plan should be established and communicated in a timely manner before deployment. At the go/no-go checkpoint, the decision is made whether to activate the emergency plan or proceed with the deployment. Adequate time should be allocated for this decision-making process, including a thorough business review to ensure rollback procedures are successful if necessary. It is crucial to maintain system backups to enable root cause analysis in case of issues during production. The team should agree on specific steps to be taken in case of problems.

This agreement ensures a coordinated response and swift resolution of any issues that arise.

Conclusion
In conclusion, data transformation projects come with their own set of challenges. However, they can be successfully executed using clear definitions, transparent communication, formal processes, and effective teamwork.