With SAP S/4HANA transformation strategies being developed by so many organizations one of the favoured migration methodologies, Selective Data Transition (SDT) will inevitably be at the forefront of most digital transformers’ minds today. At Syniti, we’ve been brought in time and again to rescue failed migration projects that result in significant cost overruns and business disruption. The cause is almost always related to data. Business transformation leaders are gambling by not fully addressing the data question, potentially putting their entire project at risk.
To help business and IT leaders understand the importance of data on Selective Data Transition, in this blog, I’ll dig into:
- What is Selective Data Transition?
- Why data is such an important factor in the transition plan?
- What dangers lie for businesses that move to S/4HANA too quickly?
- Tactics to address data in a Selective Data Transition
To learn more about what Selective Data Transition is and what it can mean to your business, read "A Primer on Selective Data Transition," which will dive into SDT as an approach in greater detail.
Why Data REALLY Matters in Selective Data Transition
In the US economy alone, according to Harvard Business Review, bad data is estimated to cost organizations an incredible $3.1 trillion* (about 6% of the total economy). Translate that to your organization, and imagine if 6% of your organization’s turnover was lost because of bad data? What resources and investment would be applied to tackle a challenge of that size in your organization?
While bad data is a massive cost for businesses today, it’s not just a theoretical problem. In all digital transformations, including moving from SAP ECC to SAP S/4HANA utilizing the Selective Data Transition approach, data is the key determinant of project success or failure. Why in particular for SDT?
Well, Selective Data Transition as an approach thrives on the happy trade-off between speed and process redesign. While a straight copy (or System Conversion) from ECC to S/4 will always be the fastest way to S/4, it also carries a higher risk particularly in a heavily customized ECC, as data and processes haven’t been properly appropriately structured by default for a move to S/4HANA.
The relatively-high failure rate of common conversions is why SDT has gained popularity in the industry as it offers the ability to mitigate selected data issues while only minimally increasing the project timeline. It’s an evolution, not a revolution. And it’s why ultimately the success of your selective data transformation efforts determine the success of your entire program.
But getting the data transformation right is no easy feat. It requires effective planning, budget, resources, automated components, and a well-thought-out data strategy that’s aligned neatly to the business objectives.
What Goes Wrong When Data is NOT Addressed?
To help us answer this question, we’ll be leaning on expert, David Linthicum, who answers this in his recent Infoworld.com article, "How to Screw up Data Migration to the Cloud":
“Most enterprises just want their move to the cloud to be fast and cheap. That means they take a lift-and-shift approach to data migration. At first, this method may make budgetary sense. However, taking the long view, lift and shift means you’ll have to migrate your data twice: once, the wrong way, and second, the right way…. Those with a short-term view often find that migrating data to a public cloud provides no real gains in cost savings, agility, or productivity. Indeed, the problem that resided in their data center is now a problem that resides in the cloud.”
In David’s view, shortcuts don’t exist in data migration. The ‘lift and shift’ approach seems to guarantee that ultimately, you’ll still have to migrate data properly a second time anyway, and you won’t actually obtain any of the anticipated benefits of the transformation until you sort the data out. What’s more, missing go-live deadlines, because the data isn’t workable, can easily cost organizations hundreds of thousands of dollars a day in resourcing costs and missed capitalization on business opportunities.
What some organizations miss as well, is that taking care of data through a migration can actually create value as well as streamline costs, even if the amount of intended data work is seemingly inconsequential. According to a study conducted by IDC and Syniti, customers of SAP Advanced Data Migration, an SAP Solution of Choice for effective data migration, generated $3.45m average benefit per organization, delivered 46% faster data migration projects, and reduced downtime by 96%.
Data is a powerful asset. Treat it as such and businesses have an excellent opportunity to generate a competitive advantage.
Tactics to (Sensibly) Address Data During Your SDT
We understand that organizations going down the Selective Data Transition path, have different expectations in timeframe and budget than those conducting a Greenfield program. So, if budgets and timelines (say 12 months) are tight what activities could be done to start addressing data? Here’s some ideas to get you moving:
Prove Data's Value Through Data Assessment Express
At Syniti, we view data as the world’s most valuable asset, but we know it’s not recognized by executives with enough value. With Data Assessment Express, our business-relevant data quality assessment, we’re able to quickly highlight where data is impacting your bottom-line, and build your SDT business case for improved data quality and master data management. With more than 450 vetted, pre-built data quality reports and unique dashboards that identify cost-savings, cash flow, and P&L improvements, at Syniti, we get things rolling fast. This will help you build your business case for data improvement and also your SDT strategy by identifying which entities, departments, or processes require the most work, and which could be copied over with little pain. If you’d like to learn more about Syniti's Data Assessment Express, you can do so here.
Be Very Selective with Where you Spend your Resources
With millions, if not billions of records, flowing around your business, it’s not realistic to try and tackle every process inefficiency all at once. If speed of implementation is a business necessity, you’ll likely make gains from copying some processes over and focusing then on a few business areas that will drive the most positive value from transformation. This could be your procure-to-pay process or spare-part inventory, or it might be a subsidiary. Try to think about the areas where data is causing you the most frequent issues, or where the most mistakes have been made. That should give you an indication, but then a deeper dive via Data Assessment Express will be able to quantify this impact and help you prioritize.
Implement a Data Management Platform to Automate and Reduce Manual Intervention
One of the larger costs of bad data in an SDT transition, are employees, the manual remediators tasked with finding, fixing and manually reconstructing data. While it may seem an appropriate short-term fix, long-term, the cost to retain a team of individuals purely for fixing data is not always that effective in the fight against bad data quality. Implementing a Data Management Platform to orchestrate the automation of the program can be a significant step forward. According to IDC, Syniti, increases IT team efficiency by 31% and overall project efficiency by 46%, and frees employees to tackle more strategic initiatives. While introducing the cost of new software may sound counter-intuitive, installing a silo-free, cloud platform, like the Syniti Knowledge Platform, allows you to streamline your software license portfolio to save on infrastructure costs too.
Concentrate on Knowledge Re-use to Maximize Data Management Efficiency
The biggest wasted opportunity in any data strategy is a lack of re-use. Particularly in complex organizations, large data initiatives begin and end in different corners of the company with little communication or retention of rules, policies and metadata. This would mean that a new data project (migration, MDM, or governance) would have to effectively start from scratch and redo the same preparatory work all over again! This is where the Syniti Knowledge Platform really stands alone in the marketplace. Through, the cloud-based knowledge tier, every initiative’s metadata, rules and policies are retained by default in the solution and then are able to be shared out across the organization, simplifying and accelerating every future project by as much as 50%.
Where to go from here?
We hope you’re seeing more clearly the importance of data in the Selective Data Transition process, and now have some ideas about how to improve data in your enterprise.
If you’d like some specific guidance into how to drive the data agenda in your organization, we’re here to help.
To read more news and thought leadership from Syniti visit our blog at blog.syniti.com.