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Emerging Technology Trends

SAP recently posted a new report titled: Trend Report 2018 - Emerging Technology Trends. This report takes a look at new technology trends that will impact all businesses over the next few years. Here are a few major hightlights from the report.

EMERGING - Technology not yet used at scale

  • Immersive Technologies

  • Conversational Systems

  • Robotic Process Automation

  • Machine Learning

  • Contextual Workspace

  • Digital Twin

  • Blockchain

  • Neuromorphic Hardware

  • Quantum Computing

ON THE HORIZON - Technology or concepts in prototyping or design phase

  • Autonomous Robots/Drones/Vehicles

  • Intelligent Assistants

  • Brain Computer Interfaces

  • Human-Machine Convergence

  • Advanced Robotics

  • Bio Computing

  • 4D Printing

Enhancing the Digital Experience

Two distinct but merging technologies are behindimmersive technologies: Virtual Reality (VR), com-puter-generated, digital environments that fully immerse users in a virtual world and AugmentedReality (AR), which overlays digital information onthe physical world and augmented reality operating environments.

Companies are already experimenting in pilot proj-ects and the technology has the potential to becomethe next computing platform. Mobile devices are currently at the center of the implementation of AR/VR and they could replace large parts of the PC landscape. We observe a coevolution with digital twins and gaming. Hardware is important, but the user experience such as easeand comfort of use and real-time performance as well as a structured con-tent are keys for success. High dataquality including realtime analytics is also a prerequisite.

The Algorithms to Make Everything More Intelligent

The success of Google AlphaGo in 2016 marks the rise of machine learning. It is based on the idea that machines can learn for themselves if they get access to large volumes of data and comprises deep learning, neural networks and natural-language processing algorithms. The technology has very strong connections to big data and real-time ana-ytics but needs high quality data and a clear pro-cess to be trained.

Machine learning and IT vendors will inbuild intelligent capabilities into business intelligence systems, analytics and into many devices, robots and machines. This is rising a number of ethical questions such as the future of our work, how machine learning will affect our behavior, how can we guard us against mistakes and unintended consequences, and how do we stay in control. Extracting the qualitative data needed for machine learning training is a challenge, as well as finding experts.

Machine learning can be used to solve a tre-mendous variety of problems and automate tasks that had to be handled by humans before, such as translations, face, speech and pattern recognition, text analytics, analyzing large data sets and more. The technology promises to save costs and improve the quality of work by automating 80% of the roughly 50% work activities that are suitable for automation. Most of todays’ knowledge work contains the following activities: pattern recognition (99%), generating and understanding natural language (75%), optimizing and planning (30%).

Blockchain – Decentralized Trust

Blockchain is based on distributed ledger technology, which records data (transactions, files, or information) across a peer-to-peer net-work. Participant can see the data and verify (or reject) it using consensus algorithms. Approved data is entered into the ledger as a collection of “blocks”, stored in achronological“chain”,and secured through cryptography.

The disruptive nature of Blockchain is its ability to move control over interactions from centralized systems to distributed users. For now, legal and institutional barriers restrict a shift away from central systems but blockchain has a high disruptive potential for all trust-bound activities.

Four types of blockchain are evolving: the consortium (controlled by a preselected group), the semi-private (a single company granting access to any user), and private and public blockchains like Bitcoin and Ethereum. Up to now the consortium model is the most accepted model for business although the technology is still unproven in a larger business context.

Blockchain might make systems more transparent, potentially more democratic and help inventing new trust models. It could improve cash flow, compliance and accountability, it could lower transaction cost sand reduce fraud. Furthermore, it offers a huge potential to unify flows of payments, physical goods and information in the rather chaotic relationships among untrusted parties, like in complex supply chains.

Blockchain will impact organizations and businesses firstly in a first non-disruptive, incremental change, by leaving processes unchanged and realizing cost savings and process improvements. A use case will start around verifications, smart contracting, transparency and accountability, sharing and leasing models, rights, and IP and government records. The second wave of block-chain will radically restructure existing industry sectors or business ecosystems into systems of trust

View the full report on the SAP website.

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