Making the Case for Data Quality During Data Migrations - Syniti

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Many organisations understand the value in making the switch now to SAP S/4HANA - saving a lot of frustration when SAP inevitably stops supporting older systems (even if that’s years away). Yet a recent study from this very group (UKISUG) found that a majority of SAP customers – 61% of those surveyed – expressed that data management challenges will slow, or have slowed the automation of their business process. Two-thirds cite data management as a key challenge when moving from SAP ECC 6.0 to SAP S/4HANA. Additionally, sixty-four percent of those surveyed cited concerns with data accuracy and consistency.

It’s not surprising that accuracy and consistency – two cornerstones of data quality and ongoing data governance – are cited as challenges. Historically, data quality has been a bit of an afterthought with data migrations, when in reality it is a critical component. As Paul Cooper, Chairman UKISUG, calls out, “Data quality is a very important consideration, as organizations will fail to realize maximum value from implementing S/4HANA if their existing data hasn’t been cleansed first.

Data quality is essential to a smooth, efficient and relatively fast migration. The shift to SAP S/4HANA – or any large data migration, frankly – is the right time to be thinking about data quality and integrating it into the migration efforts. Organizations who don’t think beyond simply moving data between platforms may incur challenges such as increased risk, excess strain on existing resources, and mounting costs with little returns.

Any new system is only ever as good as the data you put in – which sounds relatively simple but in practice can be much more difficult without proper planning. A data migration is hardly ever conducted in a vacuum, and in fact, is one of the best times to introduce the importance of quality, and then ongoing data governance; the reason for this is that a data migration is one of the few times when you have business and IT stakeholders in the same room, with the same end goal.

So how can you bring data quality to the forefront of your migration efforts?

  • Understand your data
  • Assess ongoing data issues
  • Optimize migrations through high-quality standards and analysis
  • Standardize data across multiple sources to reflect the new system
  • Simulate and validate your loading process

To kickstart a data migration effectively, organizations need to first get a handle on their data. If the eventual goal is to improve your business as a whole (operations, reporting, compliance, agility, you name it) – which is a key value driver for the move to S/4HANA - the first place to start is by assessing your current data landscape. Too often organizations take this step for granted or skip it entirely, but a proper audit of your data serves as a foundation for successful migrations.

Establishing a baseline of where your data stands today can reduce the time and resources required throughout the migration project. Pay close attention where time-consuming, manual tasks are draining resources and opening a gateway for downstream manual errors.

Ongoing data quality is essential to ensuring data accuracy, consistency, and continued success post migration go-live, but it also needs to be placed before any migration. To speed up the migration process and ensure only business-relevant data is migrated, data profiling can be used to gain insight into legacy data and generate the right outputs for migration to S/4HANA. In this way, data harmonization and cleansing needs only to be performed on the data that is needed.

Many data projects fail because scores of data is lost in disparate spreadsheets or systems. To keep migrations on track and reduce confusion during every wave of the project, data should be validated against existing business rules. A collaborative process between existing data and S/4HANA, these rules and mappings should be usable even post go-live to optimize resources.

This stage creates the quality data that the business needs for the future and utilizes all the outcomes set from the previous stages. At this point, data should be fully validated with the correct rules and configurations applied.

Using data quality standards such as data harmonization and standardization, teams can access real-time insights and identify new opportunities for improvements. Clean, ready-for-insights data enveloped in a sound strategy reduces the implementation timeline for migrations and reduces risks while optimizing your data verification and transformation efforts.Use this opportunity to fine-tune your overall enterprise data strategy, particularly in establishing clear data governance rules.

In reality, digital transformation initiatives, like migrating to S/4HANA, are always data transformation efforts. They’re integrating data science and technology into every aspect of a business – from HR to marketing to operations and more. It’s not about simply moving data from one place to the next; it’s time to move past the “lift and shift” approach.

Fixing bad data before your system goes live ensures that the benefits you based your investment on for the move will actually be realised. Addressing those problems downstream means you will invariably run into process failures or outages that are a direct result of that bad data. The reality is that the costs associated with fixing bad data after-the-fact could be as much as 10 times more than they would have been if they were fixed from the start!

Bio: Rex Ahlstrom is the CTO and EVP of Growth & Innovation at Syniti, a leader in enterprise data management. Rex has over 30 years of technology industry leadership experience and specializes in enterprise software within the data integration and information management space. Prior to joining Syniti, he successfully led multiple software development companies, including SOALogix, which was acquired by SAP in 2011. He is also a member of the Forbes Technology Council.

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