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Data-driven business models

Use the challenges of digitalisation as an opportunity

Digitalisation in the energy industry makes it possible to implement data-driven business and decision-making processes throughout the entire value chain. Everyone that does so encounters new challenges in the process. A lot of companies these days complain that there are too many manually controlled processes. They want their business processes to be digitalised. At the same time, the main question being asked is how the large volumes of customer data and data on production, distribution and transmission can be collected, processed and utilised in a structured and efficient way.

Data as a key foundation

Companies often work with several systems that are impossible to integrate due to insufficient interfaces or poor communication. The problem here is that only some data is stored redundantly. Companies can only create processes that are efficient, plausible and automated when they have a holistic view of the data – from collection, to storage, to evaluation. When developing software landscapes, the need for interfaces increases and the relevance of system interdependencies becomes more pronounced. Data is the basis for almost every new business model, as they often include data-based evaluations – as well as smart contracts or platforms.

We help your company create data-driven business models by applying our wide range of methods and tools to each of your respective situations.

The possibilities of artificial intelligence (AI) are limitless and enable you to implement large-scale tasks (for example, digitising archives) at low cost. Take a look at our use cases!

Find out more about our AI solutions

The topic of data and analytics is of particular relevance to many companies. However, many of them lack the right strategy and expertise to make the collected data usable. We already assist our customers in both of these areas using our own methods and highly developed expertise in a wide range of technologies. We design efficient data and analytics processes and support you as independent consultants.

Learn more about data and analytics

Data from an extremely wide range of areas accumulates within a company. The key to success is to prepare and evaluate this data at a high level of performance. As an end-to-end solution, the SAP Analytics Cloud makes it possible to view your business data from completely new perspectives and helps you make better strategic and operational decisions.

Learn more about the SAP Analytics Cloud

The topics of artificial intelligence (AI) and data science have been trending for the last few years: eighty-four per cent of decision-makers in IT and management state that the use of AI is a critical competitive factor. The right AI strategy not only enables you to optimise your most important processes with the help of AI-powered applications, but it also enables you to gain a better understanding of your data; all of which helps you make the right decisions for the long-term success of your company.

Find out more about our AI and data science solutions

Successful data management is essential for data-driven business models and artificial intelligence (AI). You can only exploit your competitive advantage if the corresponding data is available, the data quality is suitable and manual work is kept to a minimum. With our comprehensive data management services, we support you throughout the entire project lifecycle to keep you on track right from the start.

Learn more about data management

VBM: optimised maintenance measures reduce risk and economic downtime

Value-based maintenance (VBM) is about deciding which maintenance measures are to be carried out at the power plant and when the maintenance is to be performed. Factors such as the condition of the power plant’s components, the power plant’s operating state and the economic impact of maintenance measures and component failures are used as a basis for making these decisions. These factors are included in a risk assessment that factors in the economic impact of a given maintenance measure and keeping the power plant operational. The decisive factors for a VBM risk assessment are scattered throughout the various knowledge silos within the company. Forecasts on component conditions and economic developments are required to be able to plan maintenance work in advance. The exact maintenance costs are unknown and may vary. The exact amount of (economic) damage a power plant component failure entails is unknown.

adesso is building a central data platform in order to centralise the data from knowledge silos. adesso will use this data to build predictive models to do things such as forecast the service life of power plant components. In addition, we help digitalise engineering experience to better assess the damage potential of power plant components and the effort needed to maintain them.

Do you have any questions?

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