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White Papers

Effective BI Visualization for AI Prediction – Part 1, Continuous Prediction

By BI & AI Committee

Date: December 2023
Author: Cupid Chan, Deepak Karuppiah, Dalton Ruer, Sachin Sinha, Stu Sztukowski

In an era dominated by the buzz around Large Language Models, we take a distinctive turn to shed light on a less-discussed yet critical aspect of AI: visualizing predictions, especially in the binary domain. Our journey begins with Sally Sue Somebody, a burgeoning Citizen Data Scientist, who quickly discovers that the realm of AI predictions is far more nuanced than mere binary outputs. Through her exploration, we uncover the intricate dance between prediction scores and binary results, revealing the profound implications for decision-making in business intelligence. This first installment of our four-part white paper series invites you to delve deeper into the art and science of effectively visualizing AI predictions, moving beyond the simplistic “Yes” or “No” to a more informed, decision-centric approach. Join us as we embark on this enlightening journey, transforming the way we perceive and utilize AI predictions in the ever-evolving landscape of business intelligence.

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Effective BI Visualization for AI Prediction Part 2, Forecasting

By BI & AI Committee

Date: December 2023
Author: Cupid Chan, Deepak Karuppiah, Dalton Ruer, Sachin Sinha, Stu Sztukowski

In Part 2 of our series, we follow Sally Sue as she ventures into forecasting, where time becomes a crucial dimension in visualizing AI predictions. Transitioning from static predictions to dynamic forecasts, Sally confronts the challenge of presenting complex AI-generated forecasts in an accessible manner. This segment focuses on the art of crafting clear, actionable forecast dashboards, highlighted by six best practices Sally employs using the transformed Box & Jenkins airline data, now depicted as “Sales.” Dive into the nuances of effective forecasting visualization, essential for informed decision-making in the business realm.

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Effective BI Visualization for AI Prediction Part 3, Text Analysis

By BI & AI Committee

Date: December 2023
Author: Cupid Chan, Deepak Karuppiah, Dalton Ruer, Sachin Sinha, Stu Sztukowski

As our journey progresses, Part 3 sees Sally Sue expanding her expertise into the realm of unstructured data. Moving beyond structured predictions and forecasts, she now delves into the complex world of text analysis, where the nuances of language pose new challenges. Sally explores the intricacies of text topic, sentiment, and emotion analysis, applying artificial intelligence to uncover insights hidden within unstructured data. This segment reveals how Sally adapts her visualization skills to represent these qualitative analyses, offering a fresh perspective on AI’s capabilities beyond numbers and charts. Join us to discover the strategies Sally employs to make sense of the unstructured, transforming raw text into actionable intelligence.

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Effective BI Visualization for AI Prediction Part 4, AI Prediction

By BI & AI Committee

Date: January 2024
Author: Cupid Chan, Deepak Karuppiah, Dalton Ruer, Sachin Sinha, Stu Sztukowski

In the concluding part of our series, we turn the spotlight on Sally Sue Somebody’s prowess in ‘what if’ analysis. Here, Sally experiments with scenarios, tweaking data variables to forecast their impact on business metrics. This segment explores her method of comparing alternative models against a baseline to evaluate potential outcomes. The challenge lies in effectively communicating these complex analyses, highlighting the significance of understanding the underlying drivers and the inherent uncertainty within AI predictions. Part 4 offers insights into Sally’s strategic use of AI-powered BI dashboards to navigate this uncertainty, ensuring that decision-making is both informed and adaptable. Join us to uncover the art and science behind ‘what if’ analysis, a pivotal tool in Sally Sue’s analytical arsenal.

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Artificial Intelligence and Data in Open Source

Foundational Paper By Our Executive Director

Date: March 2022
Author: Ibrahim Haddad, Ph.D.

Artificial intelligence (AI) is no different from any other technology domain where OSS dominates. As with other industries, OSS adoption in the AI field has increased the use of open source in products and services, contributions to existing projects, the creation of projects fostering collaboration, and the development of new technologies.

Artificial Intelligence and Data in Open Source reviews critical challenges in the open source AI ecosystem, discusses common characteristics across AI and data projects, and presents the role of the LF AI & Data Foundation in empowering innovators and accelerating open source development.

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Convergence of AI, BI and Data – Analytics Future with OBAIC

Date: Nov 10, 2022
Authors: Cupid Chan, Deepak Karuppiah, Joe Madden, Dalton Ruer,Yi Shao, Sachin Sinha, Stu Sztukowski

At the dawn of the century, it was stated that technology will play such a significant role in enterprises and organizations that they will have to become a technology company. Every single company will be a technology company was the prediction and it has come true. We are at another inflection point now. Every company will have to become an AI, ML and Data Science company.

This report looks at the future of analytics at the convergence of AI, BI, and data.

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Human-Centered AI for BI Industry

Date: Dec 11, 2020
Authors: Cupid Chan, Xiangxiang Meng, Scott Rigney, Dalton Ruer, Sachin Sinha, Gerard Valerio

In 2019, our group explored how BI is being impacted by and should respond to the AI phenomenon. This year, BI & AI Committee takes a step further to investigate this influential topic of Human-Centered AI. A group of BI and Analytics leaders dissect this subject into six different areas to see how BI industry should adopt to this important theme.

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BI Endgame – When BI Meets AI

Date: Nov 17, 2019
Authors: Cupid Chan, David Freriks, David Harsh, Kim Kaluba, Monica McEwen, Scott Rigney, Gerard Valerio

Like it or not, the BI Endgame has come. We can either fight back or wait to be killed. In this whitepaper, six BI leaders will share their strategy for this battle. In particular, we explore how they leverage AI in the legacy BI platform to make it more competitive.

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“BI”g Data: How Business Intelligence and Big Data Work Together

Date: Dec 13, 2018
Authors: Jeff Bailey, Cupid Chan, David Freriks, Benjamin Reyes, Jason Tavoularis, Gerard Valerio

Exploring an optimal BI approach to help users consuming data effectively is one of the critical step in analytics, which aligns perfectly with ODPi mission of simplification and standardization of the big data ecosystem. Hence, this project is chartered to bridge the gap so that BI Tools can sit harmoniously on top of Big Data and RDBMS, yet provide the same, or even more, business insight to the BI user who also has Big Data in the backend. From a BI vendor perspective, this project aims to find an effective way for connecting and querying Big Data without reinventing the wheel. From a BI user perspective, this project should help to provide an objective guideline for evaluating the effectiveness of a BI solution, and/or other related “middleman” technologies such as Apache Hadoop, Apache Hive, Apache Drill, Presto, Apache Impala, Apache Phoenix, etc. After socializing this idea with 5 of the most prominent BI vendors in the industry, we decided to join forces within this project hosted by ODPi to publish a white paper and share with the industry what can we learn from each other in the aspect of putting BI on top of Big Data.

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Data Science Notebook Guideline

Date: Nov 16, 2017
Authors: Cupid Chan, Moon Soo Lee, Frank McQuillan, Tanping Wang

As Data Science becomes more popular, interactive notebooks are becoming more and more prevalent in both the business intelligence and data science communities. They offer a combination of data analysis, presentation and collaboration capabilities that act like an electronic equivalent to notebooks kept by Galileo while observing Jupiter’s moons. Or perhaps more personally, the composition notebooks kept during chemistry and physics classes.

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