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Analytics has been a vital part of companies since people have been doing business. Since the start of modern analytics usage for decision support, we have seen a great evolution in the technology and the effect of analytics on business processes.

However, in the age of digitalization, terms like “data is the new oil” and “democratization of data” and the mass usage of machine learning /artificial intelligence (AI) Analytics becomes even more important to every company. With digitalization data landscapes becoming more fragmented, it gets harder to find the nuggets in the high-volume data. Data is the new oil, but most organization still use nineteenth century tools to harvest their oil.

Global studies reveal that companies that embrace and integrate analytics into their core functions are significantly more productive than companies that don’t. Unfortunately, only 33% of companies succeed on their own quest to become a data/insight driven company. Main challenges to succeed are internal (data) silos, missing organizational links, complex and scattered IT landscapes, and lack of process integration of analytics in process design.

To navigate the complex environment of analytics in the age of high volume, distributed, volatile and fast-moving data, successful companies need to focus on three areas to build their information value chain:

Technology and Architecture 

Digitalization needs a holistic approach to the analytics architecture, closing the gap between the different technology stacks in a grown enterprise landscape with hyperscaler data lakes (Azure, AWS etc.) and business driven operational and analytics systems like SAP S/4HANA or SAP BW/4HANA. Besides technology, the architecture needs to provide capabilities to fulfill model driven requirements from company leaders  and use-case driven requirements from data scientists.

For the model-driven requirements, a core capability is an enterprise data model defining a semantical model to drive the business providing governed data assets to steer the company. From our perspective this capability is delivered by the SAP Data Warehouse Cloud enabling cross technology stack connectivity and IT and business driven modelling capabilities.

For the use case driven requirements, core capabilities are flexibility and orchestration for the daily changing cases. A data scientist needs to access all data in the company at core level, use state of the art algorithms and seamlessly integrate their models into processes. SAP Data Intelligence provides the capabilities to acquire cross stack connectivity, centrally managed and governed data pipelines, a broad library of algorithms and (data science) model lifecycle management.

Organizational setup 

An information value chain is not a virtual topic living in the informal processes of a company. Like the Oil & Gas industry has a global integrated supply chain for their oil, an insight driven company needs one for their information - a “Data&Analytics (D&A)” organization.

A “Data&Analytics” organization means companies embracing analytics will need to set up an organization covering the:

  • exploration of data assets in the variety of data,

  • supply routes and build of data/information assets with general management and line of business (LOB) view

  • process optimization utilizing the information assets

This organization will utilize preexisting company roles like business key users, key performance indicators (KPI) owners or reporting architect, but will add new roles like data asset owner or semantic model engineer.

Providing an organizational root for analytics will make the information value chain transparent across the company and provide ownership. Additionally, the value added to the organization is measurable and moves information out of the cost corner into the value corner.

Process Integration

From a process point of view, the information value chain and integrated analytics provides the ability to refocus the work of every function in the company. Today, most companies have defined processes and look for optimization potential. With analytics as the leading question when discussing a process change - analytics will ask: "How do you identify anomalies in your payment process?" or "How does your sales employee identify a compelling event for a meeting". A holistic analytics architecture provides capabilities to automate standard process and in parallel scan/identify the anomalies to hand highlight them directly on the desktop of the responsible person.

Enhancing your process and reengineering your digitalization journey with the analytics view adds instant value as your company's resources work on value-add topics and not the standard cases.

Briefly looking into these three dimensions it gets clear; analytics is your key to drive value from your digitization journey and focus your people on the important items of their daily business. We as SAP have been through this journey as well setting up our “Data&Analytics” unit and ingesting analytics into every function. Not only does our journey impact our portfolio for managing the data (for instance Data Intelligence, Data Warehouse Cloud, SAP BW/4HANA) and providing seamless access to it (SAP Analytics Cloud), but we feel like an evangelist for the setup of strong D&A organizations.

We have the relevant experience, experienced people and the right services to make analytics a success – a CUSTOMER SUCCESS like we aim for at SAP. Get in touch to understand more how we can help you on this exciting journey.


Author: Nils Vieth, Chief Innovation Architect, Innovation Incubation Team, Services MEE, Customer Success, SAP Deutschland SE & Co.KG