Knowledge Graph Benefit Case
What you'll learn
- Describe what is Knowledge Graph
- Articulate knowledge graph use cases
- Articulate benefit case for knowledge graph use cases
- Define relative complexity of knowledge graph solution
In the past, Organizations relied heavily on process reviews and sample reviews to drive their conclusion. Today, nearly 85% of the data they review is unstructured. Traditional IT function automates 15% but leaves the 85% to an army of humans. Digitization is seeking decision-making in real-time.
Regulators are seeking review of 100% transactions. Samples are no longer sufficient Outsourcing is involving external organizations to decision-making. Policies and procedures must be digitized. Customer engagements are aggressively looking for fast turn-around on quality decision making utilizing all available structured and non-structured data. They want support for proactive online decisions to improve customer service..
The concept of using Graph databases to map relationships digitally started seeing popular usage in business around 2015. With increased compute power, in-memory computing, multi-processing and agreed-upon standards moved the concept from academics to real-world uses in business and enterprise computing.
After completing this course, youu will be able to
* Describe what is Knowledge Graph
* Understand and articulate the use case for knowledge graph solution
* Define benefit case of knowledge graph solution
* Define relative complexity and benefits of knowledge graph solution and
Finally, You will learn how to strategize a Graph analytics experience
Who this course is for:
- Business Analysts
- Management Consultants
- MBA Students
- Analytics Students
- Data Scientists
Ms. Neena Sathi is a Principal at Applied AI Institute, specializing in developing hands-on interactive solution and training contents (videos and class lectures) for various AI and Analytics related topics. She is also a Lecturer at University of California Irvine, where she teaches many courses on Generative AI, Conversation AI and Business Analytics. She had worked as Director/Data Scientist at KPMG Lighthouse labs with specialization in developing / integrating AI solutions associated with enhancing customer experience, back office automation and risk and compliance. Neena is an experienced professional with 30+ years of experience in architecting, designing, and implementing AI and Analytics application for Healthcare, Telco, Media, Retail, Public Services and Accounting Services organizations. She drives AI solutions in prototype to production-level system development for internal and external use cases. She has been affiliated with many universities (Carnegie Mellon University, University Of Phoenix, MIT and University of California, Irvine) for advanced research, teaching and training/curriculam/content development related with many AI and Analytics related topics
She is Master certified integration architect from IBM and Open Group as well as certified Project management professional (PMP) from Project management institute. She is also certified in many Cloud and Cognitive technologies. She has widely presented and published many papers in AAAI, IEEE, WCF, ECF, IBM Information on Demand, IBM Insight, World of Watson, IBM Developer Works and many other journals.