Spotfire Apps: Industrial Data Science in Operations

The Spotfire® Data Science team has created a compilation of innovations to provide guidance, examples, accelerators, and starter apps for running AI systems at scale. The program shows real-time AI in action, including case studies in recommender systems, anomaly detection, risk management, dynamic pricing, and customer engagement. All of these offerings are based on real-world customer opportunities. The goal is to provide a brief yet rich set of capabilities that showcase how Spotfire envisions an AI-enabled world.

Spotfire Apps

Spotfire Apps use capabilities across the Spotfire platform to address deep and complex industry challenges. View the gallery below with links to demo videos, blogs, and templates to help you get started.

Hospital Management

|Healthcare|

Hospitals can be some of the most complex organizations to manage due to the large amount of data about patients and diverse management processes and assets. From recent work with our customer the National University Hospital System (NUHS), we have learned the importance of applying data science to improve the hospital's daily operation and patient care. Therefore, we have used Spotfire® Data Science to develop this Hospital Management AI Application that provides data analytics for historical patients' data requiring privacy protection and advanced analytics for predicting the Length of Stay and Readmission Risk for patients using machine learning models.

• Watch a short demo of this application
• Try it out yourself in the Spotfire Demo Gallery
• Learn more about it on this Community page

Well Completion Surveillance

| Energy | Manufacturing |

Click on the image above to watch a demo.

This solution, developed along with our partner Rivitt, provides an end-to-end solution for well crews and engineers to examine and monitor real-time completion data (streamed live from the well pad) and also to perform other data analysis tasks. Rivitt performs the data collection from the well pad sensors, aggregating it and connecting it to the data streams, where Spotfire picks them up. Additionally, Spotfire® Data Virtualization runs as a data source in order to write data back from the Spotfire dashboard.

• Watch a short demo of this application
• Try it out yourself in the Spotfire Demo Gallery
• Learn more about it on this Community page

Machine Learning for Pattern Recognition

| Manufacturing | Image Analysis |

Endless streams of data should be our savior, but too often they drown us. Identifying patterns of interest in big data is complicated but remains a critical part of any manufacturing scenario. Explore our novel approach to pattern recognition as applied to semiconductor wafer maps. Using a combination of machine learning techniques, we have enabled models to sift through massive datasets and present the manufacturing experts with a digestible and visual representation of patterns. The users can then apply their own expertise and power of the human brain to improve the model iteratively. The resulting model is deployed in production environments to act on incoming manufacturing data. While Spotfire serves as the powerful visual and interactive user interface, Spotfire® Data Science - Team Studio unleashes the scale of Spark clusters. Spotfire® Data Virtualization serves as the data abstraction layer to surface various data sources in a manageable and user-friendly way. This is a great example of how Spotfire envisions a smart manufacturing architecture. 

• Watch a demo of this application
• Try it out yourself in the Spotfire Demo Gallery
• Download it from the Community Exchange
• Learn more about it on this Community page 

Anomaly Detection and Root Cause Analysis

| Manufacturing | Energy | Transportation | Cloud |

The Spotfire anomaly detection solution leverages cutting-edge Tensorflow Autoencoders to identify anomalous behavior in multivariate environments. This methodology maps many variables into a lower dimension and compares the predicted value to the actual readings; if the predicted value is far from the actual reading it is labeled as an anomaly. This approach also offers the user visual analyses to find causes of anomalous behavior.

• Watch a demo of this Spotfire App
• Try the Spotfire App for yourself
• Watch a webinar about the Spotfire App
• Watch a webinar and see how SPC techniques are applied to time series signals for anomaly detection, clustering of anomalies, and root cause analysis
• Download this Spotfire App from the Community Exchange
• Learn more about anomaly detection through this comprehensive article

People Analytics

People Analytics | Human Resources

Trying to find the best way to manage employees and turn data into insights is such a critical need, especially now. This demo provides a brief example of the capabilities Spotfire provides for analyzing HR content coming out of Workday. People Analytics, Diversity Equity, Inclusion, and Environmental, Social, and Governance (ESG). Other capabilities that are built together with this rich demo include hires and growth, attrition, exit interviews, structure, and MBO.

• Watch a demo of this application
• Try it out yourself in the Spotfire Demo Gallery
• Learn more about it by visiting the People Analytics page on the community

Statistical Process Control

| Manufacturing | Energy | Healthcare | Telco | Cloud |

Control Charts have played a vital role in the evolution and success of the global manufacturing industry. Today, SPC is widely used across the globe and is considered an essential tool in the mass production of manufactured goods. Its usage is growing in many other sectors, as well, such as Energy, Healthcare, and Telco.
 
The SPC Spotfire App shows a solution to monitor a large number of parameters leveraging Statistical Process Control methods. The main summary page highlights recently alerted problems requiring attention. Engineers are able to select a parameter, re-calculate actual quality control charts, and interactively investigate the situation.

• Watch a demo of this application
• Try it out yourself in the Spotfire Demo Gallery
• Download it from the Community Exchange
• Learn more about it by visiting the Process Control Monitoring and Alerting Solution page. It provides a more detailed explanation of SPC methodology and this Spotfire App

Dynamic Pricing

| Insurance | Finance | Retail |

Insurance quoting systems are becoming increasingly online and self-service, generating new data rapidly. Insurance brokers sell policies based on premiums priced by insurers. If brokers are able to utilize new data effectively, they may offer more competitive premiums by using their commissions to provide discounts to select customers. Our solutions show how Spotfire can not only monitor and visualize new quote data in real-time but also continuously "rebase" machine learning models with new data and improve the model automatically over time. 

• Watch Detailed demo video
• Accelerators - find more information on how to use these solutions here:  Dynamic Pricing Accelerator
• Technical Case study available from the AAI customer reference page or content library: Closed Loop Continuous Learning with Spotfire: Dynamic Online Pricing

Customer Engagement and Recommendation

| Retail | Telecommunications |

This demo guides retail sales and marketing decision makers through a step-by-step advanced analytics application. Underpinned by data science workflows for customer segmentation techniques such as RFM and Customer Lifetime Value combined with Market Basket Analysis for Next Best Action product recommendations, this analysis produces actionable consumer insights using intuitive, interactive sliders, filters, and visualizations. While the demo uses specific types of products as examples, the same methodology can be applied across various customer metrics and recommendation use cases.

• Learn from this short demo video how Spotfire can help your telecommunications company deliver real-time contextual offers to customers. 
• View the full demo on this YouTube playlist or learn from this e-book how companies can create smart apps with Spotfire Data Science. See how "Telco X" improves sales of the newest smartphone and what can be done when predictive analytics is infused into critical business processes.
• Download the Spotfire template for Customer Analytics from the Spotfire Community Exchange and try it out for yourself!

Digital Twins to Improve Yield

| Manufacturing | Energy |

This is a manufacturing demo about digital twins for yield. It's about real-time, continuous analysis of manufacturing equipment sensors and process data at very large scales - up to millions of predictor columns - to understand the causes of semiconductor product yield loss. Digital twins are virtual representations of physical systems. The recent intense interest in them is fueled by the convergence of IoT, machine learning, and big data technology directed at the growing volumes of data available from sensors on process equipment. As the process complexity increases, these digital twins are becoming key to efficient operations and high product yields. Part 1 of this demo features Spotfire and shows how the data is visualized to easily view the results. Part 2 features Spotfire Data Science and shows the big data science workflow used to generate the data visualized in Part 1.

• View the demo, webinar, whitepaper & blogs for the Digital Twin use case on the Manufacturing Solutions page.

Production Surveillance & Condition-Based Maintenance

| Energy | Manufacturing | Transportation |

In this example, we take historical data, determine conditions that help us predict failure, deploy those as rules into a real-time data feed, and monitor for potential new failures with streaming data. This "closed-loop" analytics process helps you tighten your operations and increase uptime.

• Want to try yourself? See our Github repo with a step-by-step walkthrough here
• Or, just interact with a Live streaming version directly on our Demo Gallery here

Fraud and Risk Management

| Finance | Insurance | 

Fraud detection is an enormously important challenge in the financial and insurance industries. On one hand, they are very costly and often difficult to catch. On the other hand, flagging legitimate transactions as fraudulent leads to poor user experience and opportunity costs. This demo shows how Spotfire technology helps analysts and financial professionals explore large volumes of their transactions to build hypotheses. From there we see how labeled data can be used to build a supervised model that labels new transactions as legitimate or fraudulent. We also define a measure of oddity where each transaction is measured based on how familiar it looks compared with prior transactions in an unsupervised manner. Last but not least we see how our models can be shared and operationalized in an enterprise environment to label incoming transactions in real-time.

• Accelerators - find more information on how to use these solutions here! Specifically the Risk Management Accelerator