The fundamental challenge of data
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.
So what is the solution?
One obvious way to address and at least partially resolve data challenges is to ensure the project’s data strategy fits into the broader data governance and operating model. Also, include data within the solution design authority as a fundamental area of consideration focusing on the whole solution, including the project delivery. While many companies do this already, I think we need to go even further to improve both data delivery and the business outcomes of digital transformation projects.
Improve data management and delivery, so that transformation projects can deliver outcomes the business expects.
This blog highlights some topics to help drive improvements in how data is delivered and managed, so that the transformation projects have the expected business outcomes and value.
The evolution of data delivery and management
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.
These are some of the areas where a data-driven digital transformation must be adaptive, flexible, and responsive to meet current business needs.
Understanding the potential (and limits) of new data solutions
While many aspects of data work have not changed, there is an ever-increasing set of options available - from general open and cloud-native approaches to solutions offered by large software and “software-as-a-service” (SaaS) providers.
Will these new tools and methods make your data problems go away? As ever, the answer is a mix of yes and no. Yes, there are certainly a range of items that can help, but as much as we wish, there is no magic solution that can make all data challenges disappear. For sure, new tools and methods will undoubtedly lead to savings in effort and time. However, the challenges associated with the underlying solution design and operation still exist, and any increase in the scope of data delivery will still require a well-structured approach, a clear roadmap, and a vision of a target state that needs to be met.
I will use SAP’s range of solutions to start the rest of this discussion, but the points made are true regardless of the software used. Additionally, I will also use Syniti as an example of how a complete platform, knowledge base, and intelligent data software solution can help address and de-risk the data-driven realization required for the corporate digital transformation journey.
The SAP landscape has an extensive and rich range of solutions from a data perspective, including the SAP S/4HANA Migration Cockpit, Data Hub, Master Data Governance (SAP MDG), or SAP Information Steward. The reality for companies using SAP applications is that data will probably be in a hybrid environment that includes heritage apps, other applications, or vendor solutions. That in itself is not an issue, yet the design and thinking around data must consider this wider hybrid footprint to maximize return on your SAP investment and understand what the product sets can provide.
Syniti’s solution is a good example of a broader digital transformation-enabling platform for data. It covers the Syniti Knowledge Platform, which SAP also sells under SAP’s Advanced Data Migration (ADM). The platform can cover much more than just the extract, transform and load (ETL) of the classical data tools. Syniti supports all stages of data delivery, speeds delivery across the entire data journey, and supports the design, realization, automation, and operation of solutions, hence it’s a ‘platform’. I won’t cover the details of all the components (that will be a separate blog), although I would note that it provides the structure, approach, and detailed delivery capabilities to address a much wider data delivery.
Taking the next step in your data journey
The first step toward realizing the benefits of this change in data delivery is to take a wider perspective when reviewing your data quality, data designs, data flows, processes, and the associated governance, tools, and finally, methodology with its target operating model.
Initially, it may seem that more steps/tasks are required within a tool such as this – after all, most of us are familiar with the more limited ETL approach. But ask yourself: were you just hiding all of these additional tasks before in off-line workshops, spreadsheets, informal communications, or relying on people’s knowledge? Solutions, such as Syniti, help provide that step change in data delivery, automation, intelligent data use that enables a wider data-driven digital transformation.
This blog aims to introduce the wider data delivery considerations that sit behind digital transformations. We would be keen to talk to organizations that have large on-premise SAP-focused systems and want to understand how they could transform their delivery capability and maximize cloud utilization. If you are specifically interested in how this affects wider technical delivery, please check out my other blogs and download my Cloud Native eBook.
There is a range of associated topics which complement this data blog, which I have covered in a series of 5-minute read blogs, all aimed at enterprises that have a large SAP landscape. The other blogs cover topics such as; realizing a clean digital core, managing extension and developments, building your DevOps capability with SAP, SAP Integration Suite, intelligent enterprise solutions, service improvement (such as AIOps), and lastly building a richer user and business experience.