Skip to Content

Level 1 – Easy ; 15 minute read

Audience: Project managers, business analysts, subject matter experts

Author: Mark Muir l SAP BTS, S/4HANA RIG Americas

 

Preamble:

Welcome to a new blog series brought to you by the global SAP S/4HANA Regional Implementation Group, more commonly known as the S/4HANA RIG.  A key differentiator supporting early adoption customers the RIG has supported many S/4HANA on premise and cloud implementations.

Caught up in the minutiae helping customers successfully implement S/4HANA you lose sight of the awesomeness and tangible value S/4HANA delivers to our customer’s.  This blog series is an extension of lessons learned pulling from select content focused on management, business owners, analysts and subject matter experts.

Let us start with the history of Artificial Intelligence, SAP’s Intelligent Enterprise Strategy and Terminology then jump into Solving Unique Challenges in Your Business.

A Brief History in Time

Not wanting to recreate the wheel Alan Turing started the ball rolling with his Turing Test, “can machines think.  Continuing your journey read the Forbes publication A Short History of Machine Learning — Every Manager Should Read by Bernard Marr highlights milestone achievements and the startling pace of innovation.

SAP Intelligent Enterprise Strategy

The Journey to the Intelligent Enterprise starts here, understand the SAP Vision, Mission and Strategy from Value Discovery to Delivery.

The Intelligent Enterprise from SAP features 3 key components.

  1. Intelligent Suite to enable our customers to automate their day-to-day business processes and better interact with their customers, suppliers, and employees through applications that have intelligence embedded
    • Customer Experience
    • Manufacturing & Supply Chain
    • Digital Core
    • People Management
    • Network & Spend Management
  1. Digital Platform to facilitate the collection, connection, and orchestration of data as well as the integration and extension of processes in our integrated applications
    • Data Management
    • Cloud Platform
  1. Intelligent Technologies to enable our customers to leverage their data to detect patterns, predict outcomes, and suggest actions
    • Artificial Intelligence / Machine Learning
    • Internet of Things
    • Analytics

Terminology

The evolution of research areas and software comes with its challenges, marketing and branding is one of them.  Terms and definitions associated with AI can vary, if I were to pick an elevator pitch below are definitions you could use.

Term Definition
Artificial Intelligence (AI)

Artificial Intelligence is the intelligence exhibited by machines and broadly defined to include any simulation of human intelligence.  This includes robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs), …

Artificial Intelligence areas of research

  • Rule-based Reasoning
  • Machine Learning [branch to] Deep Learning
  • Natural Language Processing [branch to] Translation
  • Machine Vision
  • Speech [branch to] Text to Speech, Speech to Text
  • Robotics [branch to] Autonomous Vehicles
Machine Learning Machine Learning is enabling computers to do things without being explicit programed for. The key area to enable this is by leveraging the information that is already available and using some math to derive conclusions and rules that will describe a desired outcome. Machine learning uses sophisticated algorithms to “learn” from massive volumes of Big Data. The more data the algorithms can access, the more they can learn. In S/4 HANA Machine Learning capabilities and predictive analytics are embedded into core business processes to help organizations stay competitive in a rapidly changing business environment.

  • Includes robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs), …
Deep Learning Deep learning describes a revival of neural networks. Neural networks take inspiration from the human brain: they consist of small neuron-like computing units resembling the synapses of the brain. These networks can learn complex, non-linear problems from the input data. Deep learning networks derive their name from their “deep architectures” with several hidden layers. Deep learning networks have led to breakthroughs in several machine learning tasks and are currently the best bet in getting us closer to some of the goals of AI, for example making computers see and understanding language.
Data Science Also known as predictive analytics describe the widely-used analytics methods, where tools or users explicitly train exploratory models on given and well prepared data and features, in order to apply such models on new data to predict the respective pattern classification or values. Moreover, forecasting extends the concept by predicting a time series of values about the future. Many predictive analytics methods use machine learning to make their predictions.

  • Algorithmic and computational techniques and tools for handing large data sets
  • Increasingly focused on preparing and modeling data for ML & DL tasks
  • Encompasses statistical methods, data manipulation and streaming technologies (e.g. Spark, Hadoop)
Data Mining

Data mining is a multi-disciplinary field, the origins of which grew out of database technology, machine learning, artificial intelligence and statistics. It is a field included in the Data Science umbrella.

Data mining is the process of extracting hidden and previously unknown patterns from raw data and relationships between variables. Once you find these insights, you validate the findings by applying the detected patterns to new subsets of data.

The ultimate goal of data mining is prediction – and predictive data mining is the most common type of data mining and one that has the most direct business applications.

Big Data

Big Data is an umbrella term for technology that can process data with high volume, velocity, and variety, beyond what traditional databases can offer.

The availability of Big Data is one of the driving forces behind the progress in machine learning in recent years. But not every aspect of Big Data is about machine learning.

Analytics is concerned with the analysis and interpretation of patterns in data and is a term mostly used in industry.

Internet of Things The Internet of things (IoT) is the inter-networking of physical devices (“things”) to collect and exchange data. Thus, IoT generates massive volumes of data. This represents a great opportunity for machine learning to turn this data into value-creating assets.

  • For example, in predictive maintenance machine learning is used to predict machine failure before it happens.

Thank you for your interest

Mark

Further reading in this Machine Learning blog series

  1. An enablement guide for managers
  2. Solving unique challenges in your business
  3. Adoption in industry
  4. Where’s the beef?
  5. Introduction to Modelling
  6. Approaching your Project
  7. Machine learning and IoT
  8. Co-Innovation with SAP
To report this post you need to login first.

Be the first to leave a comment

You must be Logged on to comment or reply to a post.

Leave a Reply