Name: Analyst Series: Should AI Bots Build Your Data Pipelines? by Secrets of Data & Analytics Leaders in Data Analytics | Chuck Muizers
Duration: 10 min 21 s
Description: Kevin Petrie, the Vice President of Research at Eckerson Group, and Dan O’Brien, research analyst, discussed large language models (LLMs), which are neural networks that analyze text to predict the next word or phrase. These models use training data, often from the internet, to understand word relationships and provide accurate answers to natural language questions.

Data Analytics | Chuck Muizers

COVID, inflation, broken supply chains, and not-so-distant war make this a turbulent time for the modern consumer. During times like these, families tend to their nests, which leads to lots of home-improvement projects…which means lots of painting.

Today we explore the case study of a Fortune 500 producer of the paints and stains that coat many households, consumer products, and even mechanical vehicles. While business expands, this company needs to carefully align the records that track hundreds of suppliers, thousands of storefronts, and millions of customers.

Business expansion and complex supply chains make it particularly important—and challenging—for enterprises such as this paint producer, which we’ll call Bright Colors, to accurately describe the entities that make up their business. They need to be governed, validated data to describe entities such as their products, locations, and customers. Master data management, also known as MDM, streamlines operations and assists data governance by reconciling disparate data records into golden records and ideally a single source of truth.

We’re excited to share our conversation with an industry expert that helps Bright Colors and other Fortune 2000 enterprises navigate turbulent times with effective strategies for MDM and data governance.

Dave Wilkinson is chief technology officer with D3Clarity, a global strategy and implementation services firm that seeks to ensure digital certainty, security, and trust. D3Clarity is a partner of Semarchy, whose Intelligent Data Hub software helps enterprises govern and manage master data, reference data, data quality, enrichment, and workflows. Semarchy sponsored this podcast.

Dave Wilkinson: Enterprise Data Governance and MDM Case Study

We in the West have watched Russia's invasion of Ukraine with disbelief and horror. How could this happen to a European country in the 21st century? Is there any justifiable rationale for the wanton destruction of people and property there? As we ponder these questions, our data colleagues in Ukraine have experienced the war firsthand. 

To help us get a handle on Ukraine's role in the data economy and how teams based there are coping with Russia's military onslaught, Wayne interviews two software executives today who share how the war has affected their companies and how they are adapting to the evolving situation. 

Dragos Georgescu is vice president and chief technology officer of DataClarity, an innovative data analytics vendor with a development shop in Lviv, Ukraine. 

Bogdan Steblyanko is CEO of CHI Software, a  software development company based in Ukraine with more than 500 employees spread across four development centers, including hard-hit Kharkiv in the east, which is the company's headquarters.

The Impact of the War in Ukraine on Data Teams

Join us this week on the New Zealand Initiative podcast! Our host, Dr Eric Crampton chats with Professor Rhema Vaithianathan from Auckland University of Technology. Rhema gives us a peek into her pioneering work on data-tools, designed with a human touch to better serve communities.

In this fascinating chat, they delve into Rhema's trailblazing project, the Allegheny Family Screening Tool. This unique decision-aid tool is a global first, introduced in 2016 to support child welfare hotline screening. The duo also touch on the equally impressive Allegheny Housing Assessment tool, another innovative machine-learning tool used since 2020 for prioritising housing within the homelessness system.

Rhema reveals the power of predictive risk modelling in the US child protection system, where her team used administrative data to help improve outcomes for children. Listen to how this model uses past case data to guide operators in their decisions regarding potential abuse cases.

In the US, this approach worked to substantially reduce child hospitalisation. It could be done in New Zealand as well. In fact, the work started here. But New Zealand’s politics and public service has a very difficult time with new approaches. American localism means that one innovative county can try it out, and demonstrate the benefits to others.

All this and more in our enlightening podcast. Stay tuned for Part Two, where the conversation on AI and predictive models in child protection systems, and on encouraging public sector innovation more broadly, continues!

Podcast: Data analytics for social impact (part one)

ข้อมูลของเรา เวลามีขนาดไม่เยอะ ใช้เครื่องมือธรรมดาอย่าง spreadsheet ก็น่าจะเพียงพอแล้ว แต่เวลาข้อมูลมีขนาดใหญ่ มันก็จะไม่ง่ายอย่างนั้น วันนี้คุณไอซ์ วรุฒ กฤษรุ่งเรือง Senior Data Engineer จากบริษัท Data Wow จะมาเล่าให้ฟัง ตั้งแต่ต้นทาง ไปจนถึงปลายทาง Data Pipeline ทำอย่างไร ยากไหม ติดตามได้ในตอนนี้
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คำถาม
1. Data Pipeline คืออะไร แล้วมันมีประโยชน์อย่างไรครับ
2. อะไรเป็นองค์ประกอบของ Data Pipeline แล้วแต่ละส่วนมันเชื่อมโยงกันอย่างไร
3. อะไรเป็นความท้าทายเวลาเราต้องสร้าง หรือ Maintain Data Pipeline ครับ
4. ระหว่าง Batch Processing กับ Real-Time/ Streaming Data Pipeline เราเลือกใช้อย่างไร
5. คำว่า Data Pipeline คือการเอาข้อมูลมายัดใส่พวกตระกูล BigQuery หรือ RedShift หรือเปล่า
6. มีเคสที่ Data Pipeline มีบทบาทสำคัญกับธุรกิจมาเล่าให้ฟังไหม
7. อะไรเป็น Technology ที่น่าสนใจและต้องจับตามองสำหรับการทำ Data Pipeline
8. ถ้าวันนี้เราจัดการกับข้อมูลจำนวนหนึ่ง แล้วในอนาคตข้อมูลเยอะขึ้นมากๆ เราจะจัดการหรือเตรียมความพร้อมกับ Data Pipeline ได้อย่างไร
9. เราสามารถทดสอบได้ไหม ว่า Data Pipeline เราทำงานได้อย่างถูกต้อง
10. Data Pipeline มีผลกับการทำ Machine Learning หรือ Data Analytics ไหม
11. มีข้อแนะนำให้คนฟัง เวลาที่คิดจะสร้าง Data Pipeline ควรจะต้องคิดถึงเรื่องอะไรบ้างไหม

Data Pipeline ศิลปะของการจัดการข้อมูล กับคุณวรุฒ กฤษรุ่งเรือง | Tech Monday EP.139

Kevin Petrie, the Vice President of Research at Eckerson Group, and Dan O’Brien, research analyst, discussed large language models (LLMs), which are neural networks that analyze text to predict the next word or phrase. These models use training data, often from the internet, to understand word relationships and provide accurate answers to natural language questions.

Analyst Series: Should AI Bots Build Your Data Pipelines?

Only You - gin$eng

In April 2019, the Harvard Business School Digital Initiative brought together academics and practitioners for an idea exchange around technology in a multidisciplinary world. This Future Assembly flash talk features Ayelet Israeli from Harvard Business School discussing "The added value of data analytics: evidence from online retailers." To learn more, check out digital.hbs.edu.

Transcript: https://hbs.me/3bQ28EG

Ayelet Israeli on the added value of data analytics | Future Assembly 2019

This EG blog is about data modernization steps and guiding principles that can help maintain balance within schools, stores, and other face to face businesses affected by COVID-19. 

Originally published at: https://www.eckerson.com/articles/covid-19-and-higher-education-a-case-study-in-data-modernization

COVID-19 and Higher Education: A Case Study in Data Modernization - Audio Blog

This blog is about the challenge of data sprawl and how the combination of AI-based entity matching, schema matching, and enhanced knowledge graphing is moving us ever closer to the vision of self-driving data.

Originally published at: https://www.eckerson.com/articles/using-data-knowledge-to-conquer-data-sprawl

Using Data Knowledge to Conquer Data Sprawl - Audio Blog

This blog is about Graphics Processing Units (GPU) and how much of the focus and industry growth in the use of GPUs has come from their suitability for machine learning, especially neural networks, more commonly known as deep learning. 

Originally published at: https://www.eckerson.com/articles/gpu-databases-getting-more-value-from-your-machine-learning-infrastructure

GPU Databases: Getting more Value from your Machine Learning Infrastructure - Audio Blog

This blog is about Continuous Intelligence (CI) and how it integrates historical and real-time analytics to operate, monitor and tune systems of all types. Our next blogs will explore architectural approaches to CI, and how to navigate the trade offs it introduces to your organization.

Originally published at: https://www.eckerson.com/articles/continuous-intelligence-the-nexus-of-data-integration-analytics-and-operations

Continuous Intelligence: the Nexus of Data Integration, Analytics and Operations - Audio Blog

This blog compares Predictive vs Prognostic analytics and gives a quick view into systems dynamics and causal modeling. If it sparks your interest, watch for an upcoming series of articles connecting the practices of systems thinking, causal analysis, and analytics.

Originally published at: https://www.eckerson.com/articles/looking-at-the-future-through-analytics-predictive-vs-prognostic

Looking at the Future through Analytics: Predictive vs. Prognostic - Audio Blog

Chuck Muizers is a Chief Operating Officer based in The Woodlands, Texas. Chuck has almost thirty years of experience in the National Security sector. This includes fifteen years of experience spent at the executive level in public and privately held companies. Currently, Mr. Muizers is occupied with several business ventures, including the COO of Skylla Engineering, CEO of ND2 Inc, and founding member of ExpFx LLC.

Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time global thought leader interviews guests who run data and analytics programs at Fortune 2000 organizations around the world. Tune in to stay abreast of the latest technologies, techniques, and trends in our fast-paced industry.

Analyst Series: Should AI Bots Build Your Data Pipelines?

Secrets of Data & Analytics Leaders

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