Data complexity and its impact on project success is underestimated on large ERP implementations like SAP, Oracle, Workday, Infor, etc. Over the past 35 years of working on complex data initiatives, we’ve seen our share of RFPs that do not include the bulk of the data activities needed drive project and long-term organization success. This high level list outlines data activities to precede the implementation, the data work to be performed during the implementation, and the establishment of ongoing data quality management practices to continue post implementation.

Including these deliverables in the proposal/scoping documents is the first step in transforming the  data landscape from its current state to what it needs to be to drive the business for years to come. If the plan is to handle some of these activities internally, they should still be included in the RFP to sanity check the internal resourcing. The overall scope of the project can always be adjusted.

If there are additional questions on data deliverables or execution, Premier can assist with defining the scope and/or take on the execution/management of the data components of your firm's data journey.

Pre-Implementation Assessment

  • Confirm the in-scope datasets
  • Identify data sources where data for those data sets reside
  • Identify which data source is the system of record for each data element
  • Identify and quantify major areas of concern and how they will be addressed
  • Create a data dictionary/profile for each data set that includes: each element name, type of data it contains, frequency distribution of value (if relevant), etc.
  • Generate data quality reporting that identifies high level data quality issues at a summary and detail level
  • Provide interim storage strategy including the method and location of where the cleaned data will live and how it will be maintained
  • Work with business to determine regulatory and non-regulatory archival/historical data requirements
  • Document data validation/reconciliation requirements across each data area
  • Recommend solutions for master data maintenance for data integrity in legacy systems during the duration of the project
  • Quantify the data effort for remaining portion of the implementation

Data Transformation

  • Create and execute a data quality strategy that includes: expected v actual burn down rate, responsibility, criticality level, resolution categorization of automated/legacy clean-up/hybrid automated/manual process
  • Creation and execution of data readiness reporting process
  • Perform the following automated cleansing: address standardization and accuracy, removal special characters, identification/resolution of duplicate records, survivor management
  • Harmonize the overlap of master data between multiple source systems
  • Extract data from each legacy data source
  • Define and maintain the detailed data mapping specifications
  • Utilize a pre-validation readiness process that checks data for majority of errors without touching the new environment
  • Transform the data into the target system load formats
  • Load data into the new application
  • Validation/reconciliation reporting process that includes: data validation plan, scorecards, and reports that support the effort

Post Go-Live/Ongoing Master Data Management

  • Manage the establishment of ongoing data governance council through the second meeting
  • Establish data quality scorecard metrics and processes
  • Document relevant MDM policies, procedures, and data models

Depending on business requirements there is more that can be added to the MDM activities and it could be a significant RFP in itself. These bullets set the groundwork for ensuring that the organization is keeping data on the right foot.