This is the third part of our blog series: Enabling Automation: Fast Forward.
You can read the first part here.
You can read the second part here:
In this blog, we will be focusing on how organizations can access and utilize data that is not available to anyone within the organization (Quadrant 4).
Introduction
Artificial Intelligence (AI) and Machine Learning (ML) are two of the fastest-growing technologies, impacting business from operational to strategic levels. Although they are interchangeably used, they are fundamentally different with some common elements that are inextricably intertwined.
The integral and most common element of these 2 technologies is data, in particular, the exploration, and optimal utilization of it. Therefore, it is imperative that enterprises consider the right tools to address AI/ML requirements with pragmatic implementation timelines and a robust methodology. The first step for this is to identify use cases in AI/ML. While this can be a lengthy exercise, the following opportunity matrix serves as a simple way to identify use cases.
Looking at the matrix from the point of view of a department head, the quadrants of the matrix translates into the following:
How do Open-Source Technologies like Python enable AI/ML Requests?
Python is one of the most popular programming languages used by developers today owing to its rich set of libraries that can be exploited for different phases of AI/ML projects such as data exploration, engineering, model creation, and testing amongst others.
As depicted in the diagram below, Python is useful in extracting or consolidating data from multiple data sources or disparate data silos. At the same time, it can handle different kinds of data such as structured, unstructured, and semi-structured. In addition, Python provides the flexibility to apply the right algorithms to address requirements from areas like Regression, Classification, Sentiment Analysis, Natural Language Processing (NLP), and Image Analysis. These capabilities make Python a sought-after tool to handle requirements from AI/ML areas.
Integrating AI/ML features into SAP
Organizations fall short in seamlessly integrating their AI/ML use cases to the IT landscape. One of the reasons for this failure is the lack of compatibility with existing ERP & analytics solutions.
SAP bridges this gap with SAP Data Intelligence, a unified data management solution that supports AI operations and the data orchestration layer of the SAP business technology platform. SAP Data Intelligence facilitates access to structured, unstructured, and streaming data sources from the cloud, IoT, SAP applications, as well as third-party applications. SAP Data Intelligence also allows organizations to build high-quality production-grade AI / ML solutions and integrate them with other solutions. With Data Intelligence, organizations can consolidate data from disparate systems, apply AI/ML algorithms using open source technologies and output the data/findings to multiple downstream systems.
Conclusion
Enterprises embarking on their AI/ML initiative should begin by evaluating ML solutions with a business problem for there isn’t a solution yet. During the exploratory phase, enterprises tend to generate several descriptive insights which may require major change management initiatives and management support to make it a successful initiative. The most important thing is to integrate machine learning models into the decision-making processes of the organization. The descriptive analytics phase itself helps organizations to uncover hidden patterns, trends, and insights in their own data.
SAP provides a comprehensive ecosystem for building and deploying AI services in the enterprise. SAP’s data analytics platform has been designed to best suit mid and large-scale businesses. The intuitive UI and native language functionality allow citizen users to adapt to AI/ML technologies with pace.
SAP’s integrated analytics and AI solutions enable organizations to automate processes and augment efficiencies through AI/ML services and deploy them into any business process. Enterprises can manage diverse types of data at every stage of the AI lifecycle and process any type of data from any source with SAP’s robust and dynamic cloud integration. SAP’s suite of intelligent applications enhances customer experience while optimizing business processes and making artificial intelligence easily accessible throughout the enterprise.
Additionally, SAP assists organizations with monitoring, support, and deployment of ML models on ongoing operations which can also be tailored to specific organizational use cases. Backed by the SAP HANA database, enterprises can manage data at each stage of the AI lifecycle as well as store, process, and access data from multiple sources across the enterprise.