The Data Challenge - Jumping Hurdles in Data Transformation Projects

Introduction

Enterprises frequently need to cleanse, migrate, transform, and validate the data they have in their information technology systems. Even though this is a complex and expensive process, putting your own data under the lens is not always fun, much like going to the doctor for a regular medical examination and waiting for the results you already suspect or know.

Why embark on this?

Transforming or moving data may be needed, for example, due to an urgent need to replace old technology, address audit concerns or discontinued product support, cost considerations, or pending business requirements that can only be implemented with updated product features.

To add to the soup, compliance, and data security requirements can only be met by updating products and systems.

Time-boxed and restrictive requirements may result from companies selling a company or units of their businesses or acquiring business units from other companies. Divestitures are normally mandated by a Transition Services Agreement (TSA), tailored to rule the terms under which the selling company provides services to the buying company. This is meant to ensure a smooth transition and the continuity of normal business operations.

System splits or mergers may also result in transitioning infrastructure platforms or service providers.

Data anonymization may be mandatory, especially when “mixed” data needs to be maintained to ensure operational and financial integrity for both companies after a company unit split or merger.

In all cases, data is the crown jewel.

While transforming data leads to achieving business goals, gaining valuable insights, and improving data accuracy, it presents complex challenges that require careful consideration and strategic approaches for success.

Regardless of the software and technology being implemented, or the methodology and tools applied, the consistency and quality of the data must be guaranteed to ensure continued, seamless business operations.

Understanding these challenges upfront is crucial to a successful outcome and requires an adaptive approach.

This article addresses best practices to consider when embarking on a data transformation project.

Understanding the Challenge

Data transformation projects are unique and need to be understood as such. Here are some key messages:

The Key Ingredients

·?????? Data literacy and strategy alignment. Core teams must have a mutual understanding of the “AS-IS” of the data quality. With facts in hand, focused combined efforts are crucial in defining data migration strategies and transformation rules. It must be clear to all involved exactly what must be done, and these definitions must be formally signed off.

?·?????? Communication and cross-team collaboration. Any doubts?

The Grit

Inconsistent data quality in source systems always exposes inconsistencies, errors, and inaccurate or incomplete data. These issues must be addressed before the data can be relied upon, requiring thorough validation, and cleansing efforts.

Understanding complex data relationships, dependencies, and hierarchies is essential for accurate transformation.

Data security is a primary concern, given the increased vulnerability points during data movement. Proper safeguards must be implemented to protect sensitive data during transit and storage.

Data security rules are defined by applicable regulatory compliance rules, which impose severe and varying constraints on data handling and storage, such as regulatory compliance rules like GDPR, HIPAA, or CCPA. Compliance in data handling is vital to mitigate legal risks and potential penalties.

These restrictions also impact physical team location and system access.

Team member nationalities may impact security clearances.

Data transformation projects must be resilient to unexpected changes in the structure and format of data coming from evolving source systems.

A specialized technical toolset and skilled team with unique skills are required.

The scale of handling large volumes of data impacts the time required for data profiling, the definition of transformation logic, and the duration ultimately required for downtime during the go-live.

Data migrations necessitate temporary system downtime during deployment, potentially impacting business operations, mostly scheduled over a weekend for a 48-hour blackout period. During this downtime, users typically need to be logged out, and recurrent system jobs such as archiving should ideally be deactivated to prevent data manipulation during migration, optimize system resources, and ensure swift data processing.

These factors emphasize the importance of meticulous planning and alignment, to avoid budget overruns, project delays, and other disruptions.

Coordination among diverse stakeholders, including system integrators, technical teams, business users, and compliance experts, is crucial to achieving alignment and success.

Key Success Enablers

Setup and Scope

The early establishment of project management methodologies is not unique to transformation projects and is essential for laying a solid foundation and ensuring clarity and alignment.

Creating and signing off on an initial high-level charter is essential for aligning expectations and providing a foundation for the project. This charter should address key aspects such as scope, milestones, governance, meeting instances and cadence, roles and responsibilities, and reporting formats. By addressing these topics early in the project setup stage, potential issues arising from misalignment can be mitigated effectively.

Early System Access

Prompt access for team members is crucial but often time-consuming due to the sensitive nature of customer data. Plan for delays.

Again: Know your Data!

Performing data profiling early is crucial for the success of the project. The sooner your technical and functional team can analyze the system, the better.

Clean the Data

The principle "garbage in, garbage out" underscores the critical importance of data quality in any successful data transformation endeavor.

Therefore, once you know what you are dealing with, cleanse and standardize the data. This practice helps minimize errors during migration and ensures high data quality in the new system.

Data Migration Strategy

The detailed definition of data selection and acceptance criteria is paramount. It serves as the backbone of the project, guiding decision-making processes and ensuring that the right data is selected for transformation.

Appropriate Skillset

Data transformation projects encompass diverse types, each with its unique requirements and challenges. Common types of data transformation projects are Carve-outs, Deletions, Mergers and Acquisitions (M&A), and Data Anonymization.

Data transformation projects require a multidisciplinary team with a blend of functional and technical skills, aligned with clear project objectives and governance structures. Collaboration and communication among team members are key to overcoming challenges and achieving project success.

While logistics and financial expertise are often central to data transformation projects, the multidimensional nature of such endeavors frequently necessitates the integration of other specialized experts.

Navigating and planning for a learning curve is an inherent part of any data transformation project, even before it is fully formalized. This acknowledgment emphasizes the importance of anticipating challenges and acquiring the necessary knowledge and skills to address them effectively.

A symbiotic relationship between project management and technical architecture is central to the successful execution of data transformation projects. Project management ensures the alignment of project goals, schedules, and resources, while technical architecture provides the necessary expertise to design and implement robust data solutions. Together, they navigate complexities, mitigate risks, and drive the project towards its objectives.

Teamwork

Fostering collaboration among global teams is essential for achieving project success. Clear planning and disciplined execution are key components of this process.

Collaborating with teams spanning different countries, cultures, and time zones is common. The "Follow the Sun" methodology presents an appealing operational approach in such scenarios. While leveraging teams across various geographies and time zones can add significant value to projects, it necessitates precise coordination, clear planning, and disciplined execution.

By embracing the "Follow the Sun" methodology and addressing its associated challenges proactively, organizations can leverage the strengths of global teams to drive project success and deliver value.

Integration and Communication

Efficient collaboration with both customer and partner teams is essential for the success of any project. Effective communication plays a crucial role in ensuring that all stakeholders are aligned with project goals and requirements. To achieve this, it is important to establish clear communication governance and define a regular meeting cadence early in the project lifecycle.

By setting up communication governance, teams can establish guidelines for communication channels, frequency of updates, escalation procedures, and decision-making processes. This helps streamline communication and ensures that relevant information is shared promptly among team members.

Additionally, defining a regular meeting cadence enables teams to stay synchronized and address any issues or concerns on time. Whether conducted virtually or in person, regular meetings provide a platform for team members to discuss progress, address challenges, and make decisions collaboratively.

To ensure effective collaboration through regular status meetings, it is essential to establish a structured meeting schedule and agenda. This helps in maintaining consistency and ensuring that all necessary topics are covered during each meeting.

Collaborating effectively with various stakeholders is vital for the success of any project. Regular status meetings provide an opportunity for stakeholders to stay informed about project progress, discuss any challenges or obstacles, and make decisions collectively.

Overall, effective collaboration facilitated by clear communication governance and regular meetings fosters teamwork and enhances the likelihood of project success.

Planning

This also is not specific to data transformation projects. A highly detailed project plan must be in place, encompassing both project and go-live plans. Of particular importance in transformation endeavors is the meticulous consideration of timing and dependencies for the go-live phase, with thorough time measurement for all tasks involved. This plan serves as a foundational document, continuously refined, and utilized across all migration runs to ensure the development of a mature and predictable strategy for the final go-live event. By adhering to this structured approach, potential risks are mitigated, and the project's success is optimized.

During the initial planning stages, it is imperative to include an adequate number of transformation runs and validations to mitigate risks effectively. By doing so, potential issues and challenges can be identified and addressed proactively. This approach not only helps in minimizing the likelihood of errors but also ensures a smoother transition during the later stages of the project. Therefore, careful consideration of the transformation runs and validations at the outset can significantly lower the overall project risk and contribute to its success.

Project Tracking

Establish a central, actionable, and auditable tracking mechanism. This system must provide meticulous documentation of each of the project's activities, events, and decisions in a centralized location. By maintaining such a tracking mechanism, complete visibility into the project's progress is ensured, and it serves as a central source of project knowledge for all stakeholders involved. This approach facilitates efficient communication, collaboration, decision-making, and auditing throughout the project.

Data Tracking

Meticulously document the number of data records moved or deleted during the transformation process. This involves tracking changes at the table or object level and supplementing this information with other table and financial analyses to establish the "before and after" status of the data. This comprehensive documentation provides valuable insights into the scope and impact of the data transformation, allowing for thorough analysis and verification of the transformation's effectiveness and accuracy.

This information is also required for auditing purposes, as is commented below.

Testing and Acceptance

Defining acceptance criteria for testing and auditing, such as reports, should be prioritized early in the project lifecycle. This ensures clarity on what constitutes a successful test or audit and helps in setting the right expectations among stakeholders.

Rigorous functional and process regression testing may be necessary to ensure the reliability and stability of the data transformation process. Different migration cycles may require varying validation methods tailored to the specific requirements of each cycle. By conducting thorough testing at each stage, potential issues can be identified and addressed early, reducing the risk of errors or failures during the actual transformation process. This approach helps ensure that the outcome meets the desired quality standards.

During the initial transformation runs, technical-functional resources may be responsible for conducting testing to assess the functionality and performance of the data transformation process. However, once the data stabilizes and reaches a consistent state, it is essential to involve business users in formal testing. Business users can provide valuable insights into the accuracy and completeness of the transformed data from a practical perspective, ensuring that their specific requirements and expectations are met.

Ultimately, obtaining sign-off from business users on the data quality is crucial to validate its suitability for operational use and to ensure alignment with organizational objectives.

Formal Approvals

Securing formal approvals is essential to guarantee clear documentation and alignment throughout the project. This process ensures that all stakeholders are aware of and agree upon the project's objectives, scope, and requirements. Obtaining formal approvals also provides a mechanism for accountability and helps mitigate potential misunderstandings or disagreements down the line.

System Performance

Performance should be a top priority for everyone involved. Conduct performance measurements at the project's outset to assess sizing and validate system configuration. Collaborate with teams to monitor and optimize system usage throughout the project. Confirm the system configuration during the project and before going live to ensure consistency and reliability in performance standards.

Change Management

Establish and agree upon the change management process, delineating between zero-cost changes and those requiring funding. Documenting all changes, regardless of cost, ensures transparency and accountability within the project. This comprehensive approach helps streamline decision-making, ensures proper resource allocation, and minimizes the risk of overlooked or undocumented changes.

Escalation management

Indeed, encountering setbacks is inevitable in any project. Having a well-defined process in place to promptly address deviations and resolve issues is crucial. This initiative-taking approach ensures that the project stays on track despite unexpected challenges and enables the team to overcome obstacles efficiently.

The Proof is in the Pudding

The deployment (cutover) phase must be meticulously planned and executed, resembling a well-oiled machine.

Implementing a "follow the sun" approach with teams spanning various countries and time zones can be advantageous, but it demands extensive planning and coordination to ensure seamless operations.

Creating a comprehensive cutover plan is paramount and should commence early in the project timeline, refining it through multiple migration test runs. During the final rehearsal before the actual deployment (known as the Dress Rehearsal), every aspect including system configuration, team schedules, and task timelines must closely mirror what is planned for the actual go-live event.

If a “follow the sun” approach is used for teams in different time zones, clear handovers must be performed.

Be prepared for any potential failures. A fallback plan should be established and communicated well in advance of the cutover. At the Go-No/Go checkpoint, the decision to activate the fallback plan or proceed with the deployment is made. Allocate time for this and the decision-making process, including a thorough business check so the rollback procedure is well defined if needed.

It is imperative to maintain system backups to facilitate root cause analyses in the event of issues during production. The team should reach a consensus on the specific steps to be taken if something goes awry. This agreement ensures a coordinated response and swift resolution of any problems that may arise.

Closing Thoughts

While data transformation presents its fair share of challenges, addressing the key aspects defined earlier in this paper enables a successful transformation project.

Clear definitions, transparent communication, formal processes, and effective teamwork are the key ingredients to these (and any other) projects.

The overall compass is the customer’s continued, uninterrupted business operation.

Thank you for reading this paper! All feedback is very welcome.