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Technology-neutral strategy consultancy: Building a modern analytics landscape with SAP and Microsoft technology

Red rock formations and giant palm lilies, sandy beaches and turquoise waters, or dense forests and deep-green ferns – the Earth is rich in diverse landscapes. The IT and analytics landscapes within different organisations are similarly diverse. Some of these are already perfectly adapted to their circumstances, whilst others still offer great potential for cultivation, restructuring and renewal in order to remain competitive in the long term. And that is precisely our speciality.

We offer technology-agnostic analytics strategy consultancy that isn’t tied to a specific system such as SAP or Microsoft; instead, we look at the bigger picture – the entire landscape, so to speak. With all its distinctive features and peculiarities, but also its opportunities and possibilities. In doing so, we explore which measures and objectives are sensible and feasible and, if you wish, move straight on to implementation.

So, for example, if you’re planning a migration to SAP S/4HANA or wish to digitise your ERP system whilst also incorporating the crucial aspect of analytics, we’re your ideal partner. We scrutinise and analyse your existing analytics landscape to define the strategic path that’s right for you in this regard and to embark on it together.

In this blog post, we explain more about our method:

 

 

Three steps to success

Our approach is divided into three steps, which we explain in more detail below:
1
Analysis of the current analytics architecture
2
Design of the future analytics architecture
3
Analytics-Roadmap

1.) Current state analysis of the analytics architecture

Our technology-agnostic analytics strategy consultancy always begins with a current state analysis. We therefore analyse your existing landscape and map it out. The aim of this initial phase is to draw up a system map that takes interfaces into account, highlights pain points and enables us to assess and categorise the analytics landscape. In doing so, we draw on our nine-stage maturity model:

1
Level 1:
If a company is at Level 1, it does not yet use analytical reporting at all, but relies on a stand-alone system. This may be the case if the business is very small or the company is only just beginning to map processes within an operational system. Analysis is carried out using operational applications.
2
Level 9:
Companies that we classify as being at Level 9 already have a governed data platform or an enterprise-wide data strategy and are successfully implementing it. At this stage, it makes sense to discuss topics relating to ‘advanced analytics’ or, where possible, to further refine the data strategy in detail.
3
In between:
In between lie all manner of organisations, such as those using SAP as their primary database and working with individual PowerLists, those that already have their own data warehouse, or those that have already integrated an analytical reporting system but still use separate Excel spreadsheets alongside it. More advanced, in turn, are companies that already distinguish between strategic and operational reporting and have defined key performance indicators (KPIs) and clear responsibilities, or those that have also established the analytical requirements process for KPI management.
4
Advanced Analytics:
Most of our clients are somewhere in the middle and wish to improve, in line with their own timeframe, available resources and capabilities. From Level 8 onwards, we refer to this as Advanced Analytics. And it is only at Levels 8 and 9 that it makes sense to discuss the topic of AI or to actively address it. This is because the be-all and end-all of a meaningful AI strategy is a defined, clean, company-wide database that can be relied upon. The guiding principle here is therefore: without data management or a data strategy, there can be no meaningful or value-adding use of AI.

2.) Target design for the future analytics architecture

The next step in our technology-agnostic strategy consultancy – which takes both SAP and Microsoft tools into account – is to define the medium- to long-term direction for the analytics architecture. To this end, various objectives and strategic requirements – such as the integration of AI or increased speed – are aligned with timeframes, resources, budgets and other factors. This is followed by a more detailed analysis and consolidation, resulting in the target architecture.

3.) Analytics-Roadmap

Finally, the path to the goal is broken down in more detail – including measures, tasks, responsibilities and resources. This produces an analytics roadmap that already contains specific calls to action.

We´ll be right there with you

And once you’ve chosen us as your strategic partner, we’ll get started straight away. Setting course for a modern, digital future. We’d be delighted to support you on this journey with our technology-agnostic strategy consultancy.

One key factor in the success of a modern BI strategy – one that is often overlooked but should be a clear priority – is data culture and change management. It is therefore essential to embed data culture and analytical thinking within the organisation; otherwise, even the best BI or analytics strategy will be of no use. Only then can a holistic transition be achieved.
Clemens Stadler
LoB Manager Analytics & Planning

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