Data Quality

It's Time You Had the 'Data Talk'

Utopia Marketing | June 20, 2019 | 2 min read

 A major step on the road to system modernization is the migration to a unified platform, in this case... SAP S/4HANA. While enterprises are prepared to invest in SAP S/4HANA, it’s critical to realize that you need to first invest in your data. You can have the nicest garbage can on the block, but the inside is still trash. 

When migrating from a non-SAP legacy system, or from an SAP ERP system and implementing SAP S/4HANA, both master and transactional data must be migrated from the legacy system into a new environment. This means all the modern solutions in the world are for naught without a solid foundation of data on which to run, and simply moving to SAP S/4HANA won’t fix an enterprise’s data problems – in fact, it may create more.

Migrating bad data doesn’t automatically turn the data into something good; instead, bad data decreases the value of a powerful business platform like SAP S/4HANA, rendering the investment in the platform nearly useless. Reports and metrics are only as good as the data driving them, and the most powerful analytics engines in the world can still yield incorrect answers if they are basing their decisions on bad data.

Poor data quality leads to inefficiencies at every level as time and resources are spent managing inaccurate, incomplete data.  Data issues are often addressed by adding more resources and additional project time, costing the company in terms of both budget and employee hours. And then you have all the manual 'clean-up' work to try and gather insights from your analytics instead of using clean, trustworthy data to begin with. 

As an example of how bad data can affect real-world operations, think of a motor running a piece of machinery inside a manufacturing facility. A sensor in the motor can detect that the motor is failing, and run analytics to determine what part is failing and what’s wrong with it. The analytics engine sends a message to maintenance, requesting action, and a maintenance planner initiates a work order.

The problem is, the asset information in the maintenance system was incorrect, so the wrong part was ordered, and the maintenance worker arrives to fix the machine with an incorrect idea of what went wrong and a part that won’t fix the issue. The machine stays broken, reducing productivity and wasting time and money.

One tiny piece of inaccurate data – one incorrect parts number, for example – can impact system uptime and wrench time, and those shiny new analytics engines and business platforms to predict asset reliability might as well not even be there at all.

These risks become reality when data quality is not a critical track in a move to SAP S/4HANA. No migration will truly succeed if the underlying data is not right, and a failed migration can result in potential revenue loss or compliance/regulatory impact, as well as delay the entire system deployment.

It's time to have the 'data talk'.

Scary, right? Start asking these tough questions to anyone who is talking to you about migrating your data into SAP S/4HANA and those involved in the transformation. 

  • What is the plan for improving data quality before we load it?
  • Are we comfortable with just a 'lift & shift' approach to the SAP S/4HANA migration?
  • How can we ensure data records are complete and that information is being gathered and pushed out to all systems we're integrating?
  • How can we govern the data to ensure long-term data quality?
  • What processes need to be in place so that the right people have access to create, modify and approve master data changes. 

It seems daunting, but understanding these things up front can have a major impact on your business. 

If you want to 'talk data' with a team of SAP data experts who can help you wrap you head around the role data quality plays in an SAP S/4HANA migration, give us a shout

Contact us today for a 15-minute discussion with one of our Subject-Matter Experts