Radio DaTa

Chris Tynan lives in London (the UK) and works as a Director of Data at DistroKid. Before joining DistroKid, Chris had been working at intersection of music, data and tech at Utopia Music and Spotify, as well as as a Lead Data Scientist in the UK Government Administration.

DistroKid is is the world’s largest music distributor to Spotify, Apple, Amazon, Tidal, TikTok, YouTube and all major streaming services. Most new music today is released through DistroKid and every day, millions of musicians rely on its products.

Topics that we talk about include:

• What DistroKid is, who uses it and how it works
• Data collected and analysed by DistroKid
• ML and AI in music distribution e.g. detecting bad actors
• Generative AI in the music industry e.g. deepfake voice
• Democratisation of music creation with Generative AI, mobile devices, social media
• Tech and ML/AI stack used and evaluated by DistroKid e.g. AWS, Redshift, dbt, Redash, Whisper, Amazon Rekognition
• Very interesting tools in the ML/AI landscape e.g. Hugging Face, Modal
• What makes working at DistroKid unique

The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, ML/AI, cloud, and MLOps projects and services at getindata.com.

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What's data management? How does it relate to data governance, data observability, and other similar terms? In this episode, we'll try to lay out what you should know about modern data management and how you and your organization could get involved in this topic.

Topics that the podcast includes:

• What Data Management is? How does it relate to data governance, data observability, and other similar terms?
• Why do we care about Data Management?
• What does it take to introduce data management in the organization?
• How do we get from an AS-IS situation to a desired well-managed data environment?

This podcast episode was recorded by Michał Rudko, Data Architect (GetInData | Part of Xebia).

Do you want to read more about the topics? Check "Data Democratization Through Data Management" White Paper written by Michał Rudko

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Agnieszka Bomersbach lives in Uppsala (Sweden) and works as a Staff Data Engineer at Pleo. Before joining Pleo, Agnieszka had been working at Acast and Skyscanner and she has graduated from the University of Edinburgh with Masters in Artificial Intelligence.

Pleo is a fintech scaleup from Denmark that builds is a cloud-based solution for managing company spending and automating expense reporting, using virtual and physical company cards. Today, Pleo is used and trusted by 25,000+ customers across Europe.

Topics that we talk about include:

• What Pleo is, who uses it and how it works
• How data is collected and utilized by Pleo
• Data analytics use-cases implemented at Pleo
• Pleo's data tech stack, Including GCP, BigQuery, Kafka, Metabase, and Looker
• Pleo's approach to Generative AI
• Focus for the upcoming months
• Evaluation criteria for deciding between open-source and proprietary technology
• Differences in working with data at Pleo, Acast, and Skyscanner

The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.

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Kacper Łodzikowski lives in Poznań (Poland) and works as the Vice President of AI Learning Capabilities at Pearson. Kacper is also a researcher & lecturer in Artificial Intelligence at Adam Mickiewicz University in Poznań.

Pearson the world's leading learning company, serving customers in nearly 200 countries with digital content, assessments, qualifications, and data. The group's remit involves designing & building AI systems as well as providing technical & ethical leadership in application of AI for learning.

Topics that we talk about include:

• Data & AI functionalities provided by Pearson in their products
• Learning new (human) languages with Pearson and/or AI
• How AI changes the access to education and opens new opportunities worldwide
• The most important skills that one should focus on in the future
• What skills we should be teaching at schools & universities
• Disadvantages, negative consequences, risks of using AI in education
• Tech & data stack at Pearson
• Interesting future AI projects/challenges at Pearson

Links to topics that Kacper is referring to

• Skills Outlook report #1: the most in-demand skills from an employer’s perspective
• Skills Outlook report #2: how employees are preparing for a tech-focused world by building human skills
• Pearson’s leading AI textbooks
• About Pearson’s pioneering automated assessment and example data processing pipeline
• Pearson’s upcoming generative AI products
• Pearson 2023 Interim Results Presentation

The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.

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Dainius Kniuksta lives in Copenhagen (Denmark) and works as a Artificial Intelligence Product Lead at Forecast. Dainius is a tech enthusiast with more than 15 years of digital product and platform management, strategy, development, team leadership, and PSA / log-tech / ad-tech focused Artificial Intelligence and Machine Learning experience in global companies like Maersk, Adform, and Sermo.

Forecast is the work intelligence company that is delivering an AI-native platform for profitable project & resource management. The platform automates busywork, surfaces best practices, predicts outcomes, guides projects to success, and most importantly empowers every team member to do their best work.

Topics that we talk about include:

• Introduction to Forecast and its integrated intelligence
• Data that is collected and analysed at Forecast e.g. projects, budgets, scope, time, people
• Data & AI-driven use-cases at Forecast e.g. reporting, warnings, similarity of tasks, work anomaly, burnout, 
• Insights that can be taken from using Forecast related to people management, suitability (80% of accuracy currently), passion, career paths
• Tech, data & MLOps stack used to develop ML/AI models at Forecast e.g. NLP libraries, Google Bard, AWS, SageMaker, TensorFlow. Amplitude, home-grown solutions.
• Trends in people management, power of AI, AI vs. humans
• Using Forecast measure the adoption of AI tools in the tasks and projects
• Definition of the AI Product Lead role and his/her daily work at Forecast

The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com or contact us using [email protected].

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Ola Sars lives in Stockholm in Sweden and he is a successful serial music tech entrepreneur. He is the founder, CEO & chairman of Soundtrack Your Brand. Previously he started a number of successful business is the music/audio industry including e.g. Beats Music Beats by Dr. Dre.

Soundtrack Your Brand is a company that offers a cloud-based music streaming platform designed specifically for businesses (B2B). It provides licensed commercial music and analytics insights, plus curated playlists, customisable scheduling, and offline playback to help businesses create a unique and cohesive brand identity through music.

Topics that we talk about include:

• What Soundtrack Your Brand is, what the product is about, it’s value, who uses it, why and how
• The importance of data at Soundtrack Your Brand
• Data-driven use-cases implemented at Soundtrack Your Brand
• Complexity of building digital music streaming products
• Differences between B2B music streaming (e.g. Soundtrack Your Brand) vs. B2C music streaming (e.g. Apple, Spotify)
• Update of their road to profitability
• If and how data & AI helps in achieving profitability at Soundtrack Your Brand
• Current plans for investing in data & AI at Soundtrack Your Brand
• Generative AI in the B2B music streaming industry
• Interesting future trends for the next few years in the B2B music streaming industry
• Artists, music creators and their compensation in the B2B and B2C music industry
• Key metrics & dashboards that Ola looks at every day as the CEO of the company

The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com or contact us using [email protected].

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Jakub Janicki lives in Frankfurt (Germany) and works as Vice president | Big Data & Advanced Analytics at Commerzbank. He has over 15 years of experience in the financial industry, especially in using data & analytics at banks. Before joining Commerzbank in 2019, he had been working at mBank and Alior Bank. Topics that we talk about:

• How banks use data & analytics
• Types of data that banks analyze e.g. payments, clickstream, chatbot conversations
• Importance of personalized approach to every customer and its real-world examples
• Use of AI in banking industry e.g. Doc AI, Personalized AI assistants & advisors
• Leveraging AI to improve financial literacy, educate customers, help to build their financial portfolio
• Cutting-edge technologies at a banking industry e.g. cloud, metaverse
• Comparison between German and Polish banking sectors in the context of data, analytics, regulations and customer profiles.
• Why banking sector is so competitive and being a pioneer might not give you competitive advantages

The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com or contact us using [email protected].

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Varun Bhatnagar works as a Lead Designer in DevOps and MLOps area in the Group Business Intelligence value stream at Swedbank. Before joining Swedbank, Varun had been working with designing effective solutions for cloud deployment and possesses in-depth knowledge of implementing core DevOps concepts such as containerization, virtualization, version control, cloud computing, database management & administration, load balancing, etc. by using a wide variety of technologies.

Swedbank is the largest bank in Sweden and the third-largest in the Nordic countries. Five years ago, the bank started looking at how to integrate and analyze data and use insights to improve decision-making. To assure a stronger position in the market, Swedbank migrated existing capabilities and application services within the Azure cloud. The advanced analytics platform i.e. Enterprise Analytics Platform (EAP) has made AI and ML programming languages available in one click. The platform is not just limited to one set of audience but instead provides capabilities that can cater to the needs of the whole organization when it comes to AI & Advanced Analytics. The implementation of machine learning operations (MLOps) using the platform has enabled shorter development cycles, which has resulted in shorter time-to-market.

Topics that we talked about:

• An overview of the solution - What is an Enterprise Analytics Platform (EAP)?
• Evolution of MLOps at Swedbank - How it all started and how has the solution evolved over time?
• Iterative development for ML models - How can one improve the iterative development process for ML models?
• The road ahead (after migration) and what's next?
• The secret of success - What has led to this successful migration?
• Key take-away points and the lessons learned from our ML cloud transformation journey and how can one start or improve in this area?

The podcast was recorded by Adam Kawa (CEO at GetInData).

Subscribe to Radio Data on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.

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Yetunde Data and Ivan Danov both live in London, work at QuantumBlack, and develop Kedro which is an open-source MLOps project. Yetunde works as a Director of Product Management, while Ivan works as a Senior Principal Machine Learning Engineer and the Engineering Director of Kedro.

QuantumBlack, a McKinsey company is a data science and advanced analytics company that works with customers from various industries. QuantumBlack was founded in 2009 and has its headquarters in London, United Kingdom. The company became a part of McKinsey & Company, a global management consulting firm, in 2015, and now operates as part of McKinsey's global analytics practice.

Topics that we talk about:

• What Kedro is and what problems it solves
• Reasons why different groups of data practitioners use Kedro
• Differences between companies that use and don't use Kedro
• Companies that use Kedro in production e.g. Telksomsel (Indonesia's largest telecom) - blog post
• Data and statistics that describe the adoption of Kedro
• When to use Kedro vs. Jupyter Notebooks
• Running Kedro everywhere, on all clouds and on-premise using various Kedro plugins e.g. VertexAI, Azure ML, SageMaker - blog post
• Using data, analytics, and community-driven insights in the product development of Kedro e.g. Kedro-Telemetry, Github, online training
• Current challenges, milestones, and focus areas for Kedro e.g. simpler configuration
• Trends in the MLOps landscape e.g. so many MLOps tools, LLMOPs
• Integration between Iguazio & QuantumBlack, and its plans for Kedro

The podcast was recorded by Adam Kawa (GetInData).

Subscribe to Radio Data on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.

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Ludwig Holmstrom works as a Product Analytics Director at Mentimeter. Before joining Mentimeter, he had been working with data & analytics for more than a decade at various companies such as Kry, Spotify, and Google.

Mentimeter is a Swedish company that builds an interactive audience engagement platform. The platform allows users to create and conduct real-time polls, quizzes, and surveys. It is often used in educational and business settings to engage audiences and gather feedback. With Mentimeter, presenters can create various types of interactive presentations, including multiple-choice questions, open-ended questions, and rating scales. Audience members can respond to these presentations in real-time using their smartphones or other internet-enabled devices. Mentimeter provides a wide range of customization options and analytics to help presenters make the most out of their presentations. The company's mission is to empower people to share their ideas, opinions, and knowledge in a more effective and engaging way.

Topics that we talk about:

• What audience engagement platform
• Analytics use-cases at Mentimeter e.g. real-time visualization, customer journey
• Autonomous teams at Mentimeter
• Analytics stack at Mentimeter e.g. AWS, Redshift, Looker
• KPIs and dashboards e.g. Pirate Metrics (AARRR), Viral loop, LTV (Customer lifetime value)
• Unique aspects of working with data at Mentimeter

The podcast was recorded by Adam Kawa (GetInData).

Subscribe to Radio Data on Spotify, YouTube, and Google Podcasts to get notifications about future podcast episodes.

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