Assess the Landscape

Start planning the data effort by assessing what you have today and where you are going tomorrow. While frequently glossed over, spending time assessing the landscape provides three prime benefits to the program. The first benefit is that it gets the business thinking about the possibilities of what can be achieved within their data, by outlining what data they have today and what data they would like to have in the future. Secondly, this exercise gets the organization engaged on the importance of data and instills an ongoing master data management and data governance mindset. The third benefit is that it enables the team to start to seamlessly address the major issues that programs encounter when assessment is glossed over.

  • Create legacy and future D365 data diagrams that capture data sources, business owners, functions, and high-level data lineages.
  • Outline datasets that don’t exist today that would be beneficial to have in D365.
  • Ensure the future D365 landscape aligns with data governance vision and roadmap.
  • Reconcile legacy and future state data landscapes to ensure there are no major unexpected gaps.
  • Perform initial evaluation of what can be left behind/archived by data area and legacy source.
  • Perform initial evaluation of what needs to be brought into the new D365 based landscape and reason for its inclusion.
  • Profile the legacy data landscape to identify values, gaps, duplicates, high level data quality issues.
  • Interview business and technical owners to uncover additional issues and unauthorized data sources.

Create the Communication Process

Communication is a critical component of every implementation. Breaking down the silos enables every team member to understand what the issues are, the impact of specification changes, and data readiness. (Read about the impact of siloed communication.)

  • Create migration readiness scorecard/status reporting templates that outline both overall and module specific data readiness.
  • Create data quality strategy that captures each data issue, criticality, owner, number of issues, clean up rate, and mechanism for cleanup.
  • Establish data quality meeting cadence.
  • Define detailed specification approval, change approval process, and management procedures.
  • Collect the data mapping templates that will be used to document the conversion rules from legacy to target values.
  • Define high-level data requirements of what should be loaded for each instance. We recommend getting as much data loaded as early as possible, even if bare bones at first.
  • Define status communication and issue escalation processes.
  • Define process for managing RAID log.
  • Define data/business continuity process to handle vacation, sick time, competing priorities, etc.
  • Host meeting that outlines the process, team roles, expectations, and schedule.

Capture the Detailed Requirements

Everyone realizes the importance of up-to-date and comprehensive documentation, but many hate maintaining it. Documentation can make or break a project. It heads off unnecessary rehash meetings and brings clarity to what should occur versus what is occurring.

  • Confirm the legal entity structure and the planned usage of data sharing functionality.  This will have greatest impact on how the data needs to be prepared.
  • Collect the full list of data entities that need to be populated for each legal entity in D365.  Ensure the correct version of each entity is carefully considered.
  • Customers V2 and Customers V3 are not the same.
  • Document detailed legacy to D365 data mapping specifications for each data entity and incorporate additional cleansing and enrichment areas into data quality strategy.
  • Incorporate when Microsoft quarterly releases will occur in each instance into the project schedule to ensure any updates to data entities are identified and are reflected in the mapping specs, conversion programs, etc.  At least two releases must be accepted every year.
  • Be cautious when scheduling test cycles or go-live around accepted quarterly releases.
  • Document system retirement/legacy data archival plan and historical data reporting requirements.

Build the Quality, Transformation and Validation Processes

With the initial version of the requirements in hand, it's time for the team to build the components that that will perform the transformation, automate cleansing, create quality analysis reporting, and validate and reconcile converted data. To reduce risk on these components, it's helpful to have a centralized team and dedicated data repository that all data and features can access.

  • Create data analysis reporting process that assists with the resolution of data quality issues.
  • Build data conversion programs that will put the data into the D365 data entity format.
  • If necessary, review D365 documentation for any unexpected findings when working with data management delivered imports.
  • Incorporate validation of the conversion against D365 configuration from within the transformation programs.
  • Enable pre-validation reporting process that captures and tracks issues outside of the data management imports.
  • Define data reconciliation and data validation requirements and corresponding reports.
  • Create exports in data management to extract migrated data out of D365 to streamline validation and reconciliation process.
  • Verify data quality issues are incorporated into the data quality strategy.
  • Confirm any delta or catch-up data requirements are accommodated within the transformation programs.

Execute the Transformation and Quality Strategy

While often treated separately, data quality and the transformation execution really go hand-in-hand. The transformation can't occur if the data quality is bad and additional quality issues are identified during the transformation.

  • Ensure that communication plans are being carried out.
  • Capture all data related activities to create your conversion runbook/cutover plan while processes are being built/executed.
  • Create and obtain approval on each D365 load file.
  • Run converted data through the D365 import process.

Validate and Reconcile the Data

In addition to validating D365 functionality and workflows, the business needs to spend a portion of time validating and reconciling the converted data to make sure that it is both technically correct and fit for purpose. The extra attention validating the data and confirming solution functionality could mean difference between successful go-live, implementation failure, or costly operational issues down the road.

  • Execute data validation and reconciliation process.
  • Execute specification change approval process per validation/testing results.
  • Obtain sign-off on each converted data set.

Retire the Legacy Data Sources

Depending on the industry/regulatory requirements system retirement could be of vital importance. Because it is last on this checklist, doesn't mean system retirement should be an afterthought or should be addressed at the end. Building on the high-level requirements captured during the assessment. The retirement plan should be fleshed out and implemented during throughout the course of the project.

  • Create the necessary system retirement processes and reports.
  • Execute the system retirement plan.

Following this checklist can minimize your chance of failure or rescue your at-risk D365 implementation. While this list seems daunting, rest assured that what you get out of your D365 implementation will mirror what you put into it. Time, effort, resources, and – most of all – quality data will enable your strategic investment in D365 to live up to its promises.