The Fundamental Challenge Of Data and Management

Data challenges are nothing new. Indeed, some well-established methodologies and tools have evolved into rich solutions to support typical data implementations or upgrades. However, this has still not led to data being a simple, repetitive task within digital transformation projects. So why is addressing data still such a challenge?

I suspect the fundamental challenge for all programs is that data is not a single item or workstream. Instead, it’s a pervasive component of the whole solution, if not the company itself. While the transformation projects often have an overarching solution design authority, with representatives from the business, functional, technical, and project management areas who oversee the complete solution design and operating model, data is often not given the same attention.

While the fundamentals behind data strategy are probably well understood in most companies, additional considerations arise during digital transformation. These transformation initiatives drive toward a clean-core solution with separate layers (or platforms) that can realize bespoke differentiation and innovative solutions. This layered delivery must also cover heritage and cloud solutions, leading to a broader set of data design and delivery requirements. Here are some wider requirements pertaining to data:

Data Management

A key focus of data management activities is still the quality of technical and business data, yet you must ensure the relevant security, compliance, and data protection. This is a more complex undertaking when solutions span on-premise to cloud, as you potentially will have core SAP applications, open cloud-based apps, and APIs accessing the data. Data management also means addressing a complex company’s volumes, locations, and range of data entities. Hence, data management is now more complex than ever, and loss of data trust or security can have significant impacts.

Aligned Data

How business users manage, process, and use data in an aligned manner across a digitally transformed landscape is essential to ensure that information can be relied upon regardless of source or usage.While core systems such as SAP will have a single data model, there is always a need to align the data design and model across the corporate systems landscape and corporate-level informational reporting. In addition, data is often shared across customers, vendors, or trading partners. This can be done via transactional trading hubs and informational/data portals through older style integration or EDI data flows. Ensuring data is consistent and correct across all these areas is thus a fundamental requirement.?

Data Flexibility

The data design and delivery must be flexible to support the range of cloud solutions, operational processing, reporting, and business needs, including acquisitions, disposals, re-organizations, upgrades, implementations, or digital transformations. Even within one system or application, there must be flexibility to meet the changing needs (within days or hours!), yet also adhere to standard processes, balance tightly connected heritage systems, or complementary off-the-shelf systems (COTS) data models or processing solutions. Increasingly, the data solution must enable growth, innovation, and intelligent use in a more connected and real-time world, with high volumes and no latency – in short, it must be flexible enough to meet a wide range of needs.

Delivery Speed ?

In conjunction with data, flexibility is the ability to deliver change or improvement in an agile manner, often being able to support DevOps delivery beyond just the waterfall style of solutions. Additionally, speed can also be within the apps or applications themselves. Organizations must utilize the speed of in-memory processing, maximize the capacity to handle big data processing, or use embedded AI/ML to increase automated processing and insights.

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