We are all concerned with how to use the data we have in a meaningful way so that we can find the well-known needle in the haystack. By uncovering actionable insights, analytics is the key to open up the treasure that lies in our company.
Amazing examples from our customers
By leveraging our analytics capabilities, you can underpin every decision with a new level of intelligence. This provides tremendous benefits that goes far beyond reporting. The following customer examples demonstrate how analytics can unlock client potentials to find the needle in the haystack.
30% increased productivity, reduced breakdowns and lower overall maintenance costs* One of our customers wanted to improve the maintenance process. By establishing a new data driven maintenance process that is based on sensor machine data, spare part information from suppliers and production data from the customer it was possible to increase productivity, reduce breakdowns and lower overall maintenance costs.
25 million € additional revenue Another customer from the consumer electronic industry wanted to open up new business areas and wanted to create a new user experience for their customers. By linking social media data with operational data, it was possible to incorporate customer feedback directly into the product improvement process and also to build a bridge to the R&D department to create new products and offers that are discussed on social media. We have found 25 million € additional revenue in the haystack due to the identification of new products and services by leveraging social media capabilities.
But first: What actually is analytics?
Simply put, analytics delivers value by providing information that support business decisions.
In order to leverage this added value, it is important to understand that analytics is not a reporting tool, but a separate continuous business process across all other company processes, across all departments and across system boundaries. Data are the linkage between all these processes and are essential to work with new innovative technologies to bring additional value to life. This business process supports the organizational strategy alignment by eliminating the morass of stand-alone reporting spreadsheets or disconnected planning tools. Leveraging analytics is a tremendous advantage before, during and after the digital transformation.
Figure 1: Data Value Stream of Analytics
How does analytics fuels the digital transformation?
Leveraging analytics can boost the digital transformation in your company. Thus, for an optimal implementation of the ERP system SAP S/4HANA, analytics in combination with processual and structural knowledge is key. To make the transition to an integral company-wide process more effective it is important to consider it from three different components: Processes, technology, people & organization. All three components must work together, otherwise an enterprise-wide analytics approach cannot achieve long-term success. With our SAP Analytics Assessment we take these three components into considerations and support you on your journey of becoming an intelligent enterprise.
Figure 2: Dimensions of enterprise analytics journeys
Process: The available technologies, such as machine learning, enable a high degree of automation of routine activities. This results in changes in process flows and the possibility for employees to concentrate on critical business cases. The information needs of employees can thus be met with a high degree of flexibility. However, for analytics to be self-evident, processes need to be re-thought from an analytical point of view.
Technology: Technology is no longer a limiting factor. It's about providing the right information at the right time and in the right place. To do so, the technology needs to cope with hybrid system landscapes and different data structures by connecting and harmonizing the information into a consolidated model. For an integrated approach it is important to cover the following points:
Merge logical data sources
Map uniform semantics
The physical data storage is done in the systems where the data is generated. This gives the advantage of cost efficiency and speed.
People and organization: In the area of organization, the holistic cross-departmental responsibility of the department needs to be considered in terms of quality, authorization, and availability (e.g., for data aging strategies). Due to the heterogeneous system landscapes the semantic layer needs to handle the hybrid data worlds of SAP and non-SAP solutions. And at this point, it is absolutely necessary to firmly establish organizational governance structures, anchor analytics in every role and define data responsibilities. These aspects are supported by the described technical architecture and require the change of responsibilities and business processes.
How does analytics create additional value after the transformation?
Companies accumulate tons of data on every aspect of their business in data warehouses but often struggle to create value out of this valuable information. Imagine you could lower your costs and increase your revenue by making use of these data. Wouldn’t that be great? By using advanced analytics functionalities and the combination of internal and external data this is not a dream anymore, but the new normal. Our analytics solutions bring the unstructured data together in a semantic layer and make it readily available. Based on smart algorithms, analytics detects new ties in your data, uncovers insights previously concealed, automizes processes and fuels intelligent predictions. With the power of machine learning and artificial intelligence, analytics paves the way of becoming an intelligent enterprise. The additional value that can be gained from data enables you to achieve your goals faster and to secure competitive advantages in a challenging environment. All this is possible with our software and service portfolio in combination with the clear data and analytics strategy.
Success factors for getting more out of data
Essentially there are three success factors for getting more out of data.
The first one is the usage of technology to ensure a flexible data layer that can react fast to new requirements. The platform is scalable and works with all the data from the different platforms.
The second success factors are the organizational aspects that are often considered too late. Like, for example, the definition of data responsibilities and governance structures. By creating clear data responsibilities, you can break with silos and establish and end to end data value chain.
The third success factor is the consideration of the impact on business processes like, for example, the automation of processes and the associated additional capacities of employees who can then concentrate on more valuable tasks that have a greater benefit for the client.
I look forward to discuss this topics further with the community either via questions on the topic page, comments/questions on the blog or via direct message to me in the community. Pls. Find the relevant links below: