In this highly volatile economic climate, companies can’t afford to stand on the sidelines while their competitors jump on the digital business bandwagon. It’s the only way to stay relevant and withstand future challenges in this Digital Era. One crucial pre-requisite is to migrate to a digitalized, agile, and high-performance enterprise system that’s able to support the business of the future.
Through S/4HANA, SAP is committed to helping its customers transform their business and redefine the way they do things. The in-memory, digital enterprise platform offers numerous technological and business benefits that make a compelling case for you to plan for early migration.
While you may prioritize functional and business assessment, project planning, and deployment options for your S/4HANA migration, never underestimate your data situation.
It All Boils Down to Your Data
Formulating a data strategy is as important as assessing your functional and business processes before migration. A Forrester research in 2019 reported that data-driven companies are 58% more likely to beat revenue goals than those who are not focused on data.
While you may be aware that data activities like cleansing should be carried out within the migration timeline, the data strategy should encompass post-migration stage as well. Facing repercussions from deteriorating data quality after transitioning to a state-of-the-art, in-memory system is definitely something you want to avoid.
First, you need to perform a thorough data assessment. The aim of the exercise is to gauge data quality and identify data issues. You also need to revisit existing business and validation rules and determine whether they need to be modified or added with new ones.
Other important tasks are building a report inventory, establishing which reports can be replaced with S/4 reporting and analytics, and identifying areas where new analytics need to be built.
This stage is primarily focused on determining the plan and strategy to resolve the identified data issues. Usually, the issues are duplicate, corrupted, inconsistent, redundant, and incorrectly formatted data.
You need to decide what needs to be done to address these issues based on your business requirements and strategize data cleansing around them. It needs to work in tandem with your previously defined business rules.
Data cleansing is where the heart of the action is. It’s a process of removing or rectifying the above-mentioned data issues. The goal is to ensure that you have quality data based on these attributes:
- Validity — The degree to which your data conforms to the defined business rules and validations
- Accuracy — Closeness of data to the true values
- Completeness — The extent to which all required data is available
- Consistency — How consistent the data appears within data sets
- Uniformity — The degree to which the data is specified using the same units of measure
Migration can then proceed once the data cleansing activity is completed.
Data Quality and Governance
With successful migration, you might think that the buck stops with data cleansing. But once your S/4HANA goes live, it will start processing new transactions. If the data generated isn’t validated, garbage data gets accumulated, resulting in garbage analysis. Not an ideal situation to be in!
That’s why a data governance process leveraging rules and validations should be in place. Most organizations would also appoint data stewards to delineate responsibilities and ensure process adoption across their business. Essentially, these measures help to guarantee rapid value realization in moving from ECC to S/4HANA.
Where MDO DIW Fits In
Manually performing the data-related activities is simply unheard of. Companies usually opt for software or tool to automate and expedite the activities.
This is where MDO Data Intelligence Workbench (DIW) comes in to facilitate your S/4HANA transformation. It has the capabilities to assess, prepare, cleanse, and migrate data as well as providing a data quality assurance framework. It ensures overall data completeness, integrity, and compliance.
To top it off, DIW has these unique differentiators with the ultimate view towards establishing a data-driven culture:
Active and passive governance
Active governance ensures only cleansed and deduplicated data enters the system, while passive governance ensures data quality within the system using updated rules and validations.
Machine learning and AI-enabled
Its ML and AI capabilities allow for a more predictive and intelligent rule-based approach to cleanse and enrich data. You just need to train the ML model using your data sets.
The Golden Record functionality allows you to define intelligence to de-duplicate data and also propose, merge the unique records, and remove all obsolete data from your environment. Ultimately, you’ll get to save precious database space apart from the obvious benefit of obtaining a single version of truth.
Data enrichment can be done based on pre-defined standards or external content like UNSPSC taxonomy. This adds more context to your data to derive more useful insights. This also enables organizations to stay compliant and follow various industry standards, e.g., ISO 8000 and ISO 14224.
Various collaborators can be appointed to review or enrich data across the business. With dashboards and analytics to facilitate visualization, the progress of data cleansing and other activities can be tracked with defined SLAs and KPIs.
Towards a Data-driven Culture
DIW and other MDO solutions come with pre-built data models and integration with SAP S/4HANA to allow for a more seamless transition and ensure ongoing data quality and governance. If you’re looking to cultivate a data-driven culture as part of your digital transformation agenda, look no further than MDO to manage, govern, and automate your data activities.
Get in touch with our Data Stewards today!Written by: Shigim Yusof