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Author: Marcel Egloff, Senior Account Executive, Data Migration International

For over twenty years, my career has been focused on SAP-related areas in a variety of different functions and positions. And although the main theme has stayed the same, there’s been an enormous amount of change during that time – and today more than ever. The technologies I’ve seen have spanned everything from mainframe and proprietary midrange systems to client-server architectures and hybrid infrastructures comprising cloud and on-premise environments; and from standalone functional modules running alongside each other through to Business Suite, industry-specific packages and the applications and cloud offerings acquired since 2007. No wonder the adage says that 10 years in IT is like an entire generation in normal life.

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But even with all these changes, which have solved many past problems, one challenge has remained: how to efficiently and effectively manage the lifecycle of legacy information against a backdrop of recurring changes to data structures and of redundant data sets. After all, times and solutions might change but information is always there.

Agile or stable?

Why? Because data and documents from completed tasks need stability. For legal and other regulatory reasons, there should be no further changes to their structure. In the financial area this is usually the case for ten years, while for liability questions it can continue for up to thirty years and in the medical sector up to a hundred years.

Still, it’s not like companies want to get rid of their unchanged legacy information as soon as the legal retention period has expired. Quite the opposite in fact. For example, knowledge – such as how to manufacture cars efficiently while maintaining a consistently high level of quality – is older than details of the design of an e-car and its control software. Machines and production systems have a lifecycle of several decades, and in that time they provide valuable insight – not just the construction blueprints, but also the maintenance reports. These can be very helpful for people developing future products, even if the proportion of software in them will be much higher going forward.

So historical information is valuable, there’s no doubt about that. But the fact is that the retention of, and access to, legacy data and documents is a challenge from both the technical and financial perspectives. Many companies continue to run their legacy systems so that the historical information stored in them remains accessible. That naturally costs time and money while increasing the IT infrastructure’s complexity – the sworn enemy of efficient and agile working methods.

One key question emerges from these challenges: how can companies resolve the conflicting needs for agility and stability?

Complex or simple?

From my own experience, I know that this has long been an open question since the transition from SAP R/2 to SAP R/3. But companies should really find an answer to it when they migrate to SAP S/4HANA. If they don’t, they’re risking the goal and success of their digital transformation initiatives. After all, how can they create data-based and data-driven business processes and models if they ignore the value of historical information? If the legacy databases can’t be cleaned up, enriched and optimized, companies won’t meet one of the key prerequisites for big-data scenarios. Also, the high operating costs and complexity of legacy databases eat into the resources companies need for innovation. And what happens if they have to keep reducing access to legacy systems and information for security reasons? Or if the executive team has all the hassle of fines and legal consequences? These are a risk if the company’s not able to manage the entire lifecycle of historical information – from storage through to deletion in a way that’s legally watertight and compliant with GDPR.

Dumb or intelligent?

SAP rightly describes the goal of digital information with the term “Intelligent Enterprise”.

However, can an enterprise really be intelligent if it doesn’t have a well-trained, long-term memory in perfect working order? The analogy with the human brain is striking. We all use our long-term memory every day. Once something is stored there, we can usually recall it with no problem – as though we had learned or experienced it very recently. It doesn’t slow us down or burden us – quite the opposite in fact. It keeps us agile and is the heart of our identity, right into old age. And the reason it can do all that is because it’s located in its own areas in the brain.

This principle of designating separate areas for different tasks can also be used for IT infrastructures. Historical data and documents belong to the company’s long-term memory and should be stored in separate environments – not in legacy systems. That’s the only way they can make a real contribution to the company’s agility.

Separated and automated!

A business can benefit in many ways by separating its historical from its operational information and managing it on a dedicated platform. First: it will quickly start seeing significant cost reductions, as the new platform means that the company can wind down and completely retire the legacy system. Compared to running the old system, this usually reduces operating costs by 80 percent.

Second: this approach delivers long-term benefits. Companies can clean and enrich old data before they move it to the new platform for its permanent, tamper-proof storage. Optimizing data quality is crucial to ensure that all the promise of data-driven business processes and models can really be fulfilled. Decisions based on data analysis are only as good as the quality of the data itself.

The platform for managing historical data requires a high degree of automation if it is to support decision-making and other agile scenarios like migration to SAP S/4HANA or mergers and acquisitions. Ideally, companies should be able to import data and documents from the legacy system at the touch of a button. And the platform can replicate any changes to business objects and data structures in the live systems. As far as possible, it should also automate the process of identifying the potential of information that doesn’t need to be imported to the operational environment. The platform also provides filter rules – used to migrate the selected information from the complete database – in a modern and neutral format to ensure that most transformation and migration tools will understand them automatically. Another benefit here is that analysis solutions can access historical information just as easily as operational data, perhaps via data hubs. In this way, companies can be sure their long-term memory is always available, complete and system-independent when they’re looking for sustainable solutions for the future.

Simply intelligent rather than complex!

Human intelligence depends largely on the connections between the different areas of the brain and on optimal interplay between them. Yet we’re not conscious of the complexity of these areas and their interconnections – everything just happens on its own, automatically.

Automation is also the key to reducing complexity in the Intelligent Enterprise. When companies automate the management of their historical information throughout its lifecycle, and implement a suitable platform providing automated connections between that information and the operational systems, they’re simplifying their entire IT environment. Why? Because simplicity is the key to the intelligence of an information management system.

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Matthias Edinger