Information management and machine learning are more closely connected that ever before. With today’s data sprawl and complex changes in the variety, volumes, and types of data, most companies need a dizzying array of tools to extract value from increasing volumes of multifaceted data. While machine learning and AI promise to deliver a wealth of new, meaningful business insights, the data requirements and complexity involved in deploying and operating AI is daunting.
So, the question is – you know you have lots of data, how do you mange the complex data integration and data management requirements for multifacted data? How can gain insight by somehow applying machine learning to that data? How to you begin to find the right business case to help you know where to start?
One of the best places to start is with the interactive Business Case Discovery Tool for SAP Data Intelligence.
In this interactive tool you explore the business cases, how SAP Data Intelligence works, and how it can work for you.
The tool is divided into multiple sections. The sections explain the challenges of extracting real value from data and the importance of making data work for you.
It also provides an introduction of SAP Data Intelligence and how you can get started with the solution.
Each business case includes the description of the business case, the challenges, the solution, benefits, and a dig deeper section where you can go to learn more. Each business case has been implemented by a SAP customer as either a proof-of-concept or live implementation.
We have multiple business cases across various industries and periodically we add more.
One example is customer churn. It provides a description, the challenges, solutions, benefits and dig deeper. Below you can see the challenges for the customer churn case where they had siloed data that was uncleansed and not connected.
With SAP Data Intelligence they used data pipelines to orchestrate that data so that machine learning could be applied to understand what causes churn and to predict churn.
Other use case examples include:
Predictive Quality – Improve manufacturing quality through sensors, cameras, using machine learning
Late-Payment Risk Assessment – Reduce the number of customers late payments and predict if customers are likely to pay late.
360-Degree Customer View – Leverage consumer transaction history to predict future sales
Customer Risk – Assess credit worthiness and credit risk scoring for customers
Product and Parts Identification and Classification– Use image classification to determine how to fix jewelry
Predictive Simulation – Help municipalities become green cities by predicting the results of investments in different types of energy
Time Sheet Analytics– Orchestrate data to make better decisions on contingent workforce spending
Serial Returns Detection – Predict likelihood of customer returns
Service Ticket Intelligence – Automate and streamline service ticket processes using machine learning
Product Quality Improvement -Understand how home appliance is being used using IoT data about user behavior
Customer Churn Prediction – Leverage data in multiple, disconnected systems in order to predict customer churn
IoT Data Analysis and Management – Cleanse and understand IoT data, integrate it with enterprise data to predict future performance and optimize efficiency of wind turbines
Commodity Code Prediction – Use machine learning to predict commodity code required for new products
Predictive Pricing – Leverage machine learning to predict best price for used cars
E-Commerce Product Placement Optimization – Ensure products sold on various digital stores have correct placement according to product guidelines
We continue to add additional use cases, so check back from time to time.