Is data quality achievable in an SAP S/4HANA project, given the multitude of strategic, architectural, operational and functional decisions and assumptions that must be made?
The complexity and number of considerations, touch-point impacts and sequence risks can put a strain on project milestones and post go-live success.
Is there even room to set the data quality stage gates as high as possible, given other time and budget constraints, or does some quality have to be sacrificed in order to ensure an effective solution and timeous project delivery?
The answer to both questions is: “Yes. Data quality is achievable, measurable and has been proven successful.”
Migrating into SAP S/4HANA is challenging and the key is to use a robust, proven S/4HANA data migration methodology. With the different migration options available (eg, greenfields/brownfields, etc), one non-negotiable challenge is your data. Data has to achieve the following objectives:
- Limit and mitigate risk to the business of cutting over to SAP.
- Provide quality data for testing and go-live.
- Collect and collate data rules that can be used for keeping the data clean post go-live.
These are some of the repeated key takeaways from current and previous S/4HANA migration projects, which can reduce the obstacles that increase the risk of the two questions becoming ‘no’.
|Scope and requirement management||Other major project dependencies (eg, integration, finance).||High||Joint governance and planning are driven by enterprise-wide change approval board. Review alternate plans (Plan B) to manage exceptional scenarios.|
|Scope and requirement management||There is a risk that priorities may dictate ERP work being de-prioritised which may result in a lack of business resource capacity to undertake allocated ERP tasks.||High||Resource levelling across the enterprise, fixed plans from partners, formal extension processes.|
|Executive||Peak business periods will severely limit the availability of business to participate in the design and build activities.||High||Plan smoothing of consulting resources and business resources, to allow for any delays. Stage gates will still need to be observed.|
|Executive||Delays in sign-off of design and blueprint will lead to knock-on delays on dependent deliverables. There will be a timeline risk when multiplied across the programme.||High||Consultants to be on-site where required, to clarify detail with business analysts and allow for quicker turnarounds. Early change management and business secondment into the programme. Programme kick-off to emphasise ways of working and urgency. Change management team to facilitate ways of working.|
|Project management||Overlap phases due to project timelines.||Medium||Stage and quality gates must be defined to mitigate this risk.|
Brett Schreuder, Managing Director of GlueData Master Data Solutions, highlights that “by optimising and reducing your data footprint, you will ensure SAP S/4HANA can support the business processes and increase quality reporting. Data is the glue that binds the configuration to the process, ensures effective system integration and allows SAP S/4HANA to provide the business with a clear and singular view of their customers, vendors and product offerings.”
The six steps for migration are: Analyse, purge, cleanse, validate, migrate and reconcile. The idea is to run your ERP system as lean as possible, and proper data assessments will clearly indicate how much data can be purged (archived or moved to a data warehouse for history keeping purposes). Once the data scope has been confirmed, data will be profiled, cleansed and validated prior to transformation and load into SAP. Data cleansing is a time-intensive activity, which requires active business and IT involvement and should start right after the migration blueprint has been defined.
There are three important benefits from cleansing your data:
- Reduced data footprint will also reduce your infrastructure, hardware and SAP HANA licensing costs.
- Reduced data size allows you to perform the technical migration with reduced business downtime and less technical complexity.
- By keeping only quality and necessary data in your system, SAP HANA performs even better after the technical migration.
“It is important to prioritise data-cleansing activities. GlueData has found that the 80/20 principle holds true in data quality. By fixing the 20% of items with high stock volumes or high item values first, 80% of critical-revenue touching data issues can be resolved,” continues Schreuder.
In summary, quality stage gates and a tiered approach will allow prioritisation of data objects per tier, with clear tracking and sign-off of data at every point in the migration. This approach has the ability to set and measure quality as defined by agreed business expectations and signed off by the business as valid targets for data migration stage gates. Pre-load migration reports will show if the data meets the expected standards within the error tolerance levels, and then a post-load report is run that reflects the state of the data that has been loaded into SAP. This report will be signed off by the business data owners before the system is handed over to the business.
Every S/4HANA Project must set data as the priority for the post go-live to be successful.