Data Science
As digitisation progresses, industries around the world are moving from what was once primarily a physical trade exchange to a closely connected digital ecosystem.
The cost of storage and performance has fallen over decades. Machine learning (ML) algorithms and tools are now widely available. This enables companies in all industries to derive ever greater added value from the data transmission speeds of their processes. Process automation, pattern recognition and predictions enable both efficiency gains and the development of ever more innovative products and services. Taken to its logical conclusion, this means that companies in all industries are becoming more and more like software companies - regardless of whether they produce physical products or services. The speed at which companies develop their digital core as the operating system for all business units and processes will be a key factor in their competitiveness in the coming years.
Newly founded companies, for example in the fintech or biotech divisions, have the opportunity to create a cross-company data lake on which use case-related machine learning is based and which defines and improves the company's products and services. They can set up their data pipeline from the outset in such a way that it contributes to the success of their application scenarios. This allows the company's end-to-end processes to be data-based, largely paperless and automated and geared towards innovation.
The established organisational structure of traditional companies and their legacy IT systems tend to make it somewhat more difficult to introduce a horizontal digital platform across divisions. It is not uncommon for a data lake to begin as a data puddle in one of the divisions with an affinity for data and then gradually spread to other divisions via lighthouse projects. Preparing the data treasures into a structure that can be used for specific application scenarios takes time. There are also cultural aspects: While data lakes, data pipelines and machine learning for the development of use cases are at the core of digital value creation from the outset for newly founded companies, for traditional companies this tends to mean changing what already exists.
Those traditional companies that have invested early in building their cross-company digital core will be better prepared for the increasing intensity of competition from new market entrants. This can be seen in divisions such as entertainment, retail, banking, pharmaceuticals, automotive, manufacturing and many other sectors.
Swisscom supports companies in seizing the opportunities that arise. It has entered into strategic alliances with leading cloud-based service providers that enable both large and small businesses to utilise artificial intelligence on the basis of data lakes.
Matthias Mohler, Head of Data & Analytics Consulting: "Swisscom has a wealth of experience in the design, development, integration and operation of data-driven solutions such as data lakes, data warehouses, ML and AI as well as dashboarding & reporting. Our consulting teams deal with different industries and their individual challenges and develop customised solutions in the cloud and / or on-premise based on the appropriate technology. In many cases, a well thought-out data strategy serves as the basis for creating clarity about the company's usable information, its quality and potential. After all, only data that is connected across the board creates added value and thus competitive advantages when used in AI and machine learning"
The world-leading 5G, IoT, Cloud and AI track solutions (Azure-based):
Swisscom wins the Microsoft Global IoT Award
with the digitisation of Rhomberg Sersa's track construction.
Innovation thanks to digital transformation: Datwyler's journey from cable manufacturer to IT solution provider (AWS-based):
Where innovation is rooted in tradition
Swisscom is building the data analytics platform of the future with Helvetia:
VP Alliance Management
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