On today’s episode, we’re joined by Vlad Eidelman. Vlad is CTO and Chief Scientist at FiscalNote — a leading technology provider of global policy and market intelligence uniquely combining AI technology, actionable data, and expert and peer insights to give customers mission-critical insights.

We talk about:

• Vlad’s story and what FiscalNote does.
• How AI changes software.
• The importance of adding extra value to software.
• What to do with user data?
• How Vlad makes internal decisions at FiscalNote.
• The impact of remote work.
• The importance of building the right data analytics stack to acquire data.

Vlad Eidelman - https://www.linkedin.com/in/veidelman/ FiscalNote - https://www.linkedin.com/company/fiscalnote/

This episode is brought to you by Qrvey

The tools you need to take action with your data, on a platform built for maximum scalability, security, and cost efficiencies. If you’re ready to reduce complexity and dramatically lower costs, contact us today at qrvey.com.

Qrvey, the modern no-code analytics solution for SaaS companies on AWS.

saas #analytics #AWS #BI

You just learned SQL or Python, or Tableau. But you don’t know how to build your data science project? In this episode, Avery shares a 3-step guide to building your first data science project.

🌟 Join the data project club!

“25OFF” to get 25% off (first 50 members).

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(1:28) - Art is theft, and so is the data science project

(4:02) - Find ideas on Towards Data Science Medium

(5:32) - Read a few articles to get inspiration

(6:05) - Avery’s strategy is doing 30 projects in 30 days

(9:08) - How academia finds inspiration to write

(11:01) - Take Avery’s project, replicate and do it

Mentioned Links:

Building 30 Data Science Projects in 30 days: https://youtu.be/kKmA9ihIg20

30 Data Science Projects Resources: https://www.datacareerjumpstart.com/30projectsresourcesignup

I Used Data Science to UNCOVER McDonald’s Healthiest Meal: https://youtu.be/3bbFc1225-4

Connect with Avery:

📺 Subscribe on YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos 🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535 👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://www.instagram.com/datacareerjumpstart/ 🎵 TikTok: [https://www.tiktok.com/@verydata?]

Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Do you need to be active on Linkedin to land a data job? What should you post or even do on the platform?

In this episode, Avery sits down with teacher turned Data Analyst Chris French to discuss how he landed his first data job leveraging Linkedin.

🌟 Join the data project club!

“25OFF” to get 25% off (first 50 members).

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Chris’s Links:

Connect on LinkedIn

Timestamps:

(9:07) - How Chris grow Linkedin from 20 followers to 20,000 followers

(13:19) - His content types that gain attention

(18:02) - What Chris would do differently to do the job search

(23:29) - Technical skills vs soft skills

(26:53) - Before land data job, analyze your process first

Connect with Avery:

📺 Subscribe on YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos 🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535 👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://www.instagram.com/datacareerjumpstart/ 🎵 TikTok: https://www.tiktok.com/@verydata?

Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Kris Ewald will give you an overview of Innovations in Data Science you should be aware of. If data driven insights are key to competitiveness, you need to keep innovating on how you Collect, Manage and Challenge data. With plenty of other talks about very specific tools and data analytics frameworks, this talk will instead aim to inspire you to apply new approaches to your data science - it'll give you a list of topics you should care about and pay attention to. Expect to hear about Zero-knowledge proofs, Homomorphic encryption, DAGs, and Blockchain and data as value objects.

You just got the data job offer. Should you accept it or not? In this episode, Avery discusses what pros and cons are there and deciding on accepting the job offer.

🌟 Join the data project club!

“25OFF” to get 25% off (first 50 members).

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Timestamps:

Timestamps:

(0:58) - Jay got a data job offer but was in a dilemma

(2:22) - Your past experience is ALWAYS relevant in the data world

(2:59) - Job offer pros to look out

(4:03) - Job offer cons to be aware of

(5:57) - Avery takes on deciding job offer

(7:53) - Job offer is beyond salary but perks & benefits

Connect with Avery:

📺 Subscribe on YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos 🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535 👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://www.instagram.com/datacareerjumpstart/ 🎵 TikTok: https://www.tiktok.com/@verydata?

Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

CompTIA® Data+ DA0-001 Exam Cram is an all-inclusive study guide designed to help you pass the CompTIA Data+ DA0-001 exam. Prepare for test day success with complete coverage of exam objectives and topics, plus hundreds of realistic practice questions. Extensive prep tools include quizzes, Exam Alerts, and our essential last-minute review CramSheet. The powerful Pearson Test Prep practice software provides real-time assessment and feedback with two complete exams. Covers the critical information needed to score higher on your Data+ DA0-001 exam! Understand data concepts, environments, mining, analysis, visualization, governance, quality, and controls Work with databases, data warehouses, database schemas, dimensions, data types, structures, and file formats Acquire data and understand how it can be monetized Clean and profile data so it;s more accurate, consistent, and useful Review essential techniques for manipulating and querying data Explore essential tools and techniques of modern data analytics Understand both descriptive and inferential statistical methods Get started with data visualization, reporting, and dashboards Leverage charts, graphs, and reports for data-driven decision-making Learn important data governance concepts ...

Can cold messaging really land you your first data job? Even without experience? In this episode, Avery sits down with logistics Data Analyst Asa Howard to discuss how he landed his first data job with a simple cold messaging strategy.

🌟 Join the data project club!

“25OFF” to get 25% off (first 50 members).

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Asa’s Links:

Connect on LinkedIn Waitlist for Google Sheets course

Timestamps:

(3:22) - Asa realizes he needs a career pivot

(5:21) - What a Solutions Engineer does (Logistics Analyst)

(10:21) - System he used to land his job

(14:24) - Cold message template you can steal

(18:01) - What tools he uses on day to day

(24:37) - Google Sheets vs Excel

Connect with Avery:

📺 Subscribe on YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos 🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535 👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://www.instagram.com/datacareerjumpstart/ 🎵 TikTok: https://www.tiktok.com/@verydata?

Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Summary

Encryption and security are critical elements in data analytics and machine learning applications. We have well developed protocols and practices around data that is at rest and in motion, but security around data in use is still severely lacking. Recognizing this shortcoming and the capabilities that could be unlocked by a robust solution Rishabh Poddar helped to create Opaque Systems as an outgrowth of his PhD studies. In this episode he shares the work that he and his team have done to simplify integration of secure enclaves and trusted computing environments into analytical workflows and how you can start using it without re-engineering your existing systems.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you're ready to build your next pipeline, or want to test out the projects you hear about on the show, you'll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don't forget to thank them for their continued support of this show! Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Build Data Pipelines. Not DAGs. That’s the spirit behind Upsolver SQLake, a new self-service data pipeline platform that lets you build batch and streaming pipelines without falling into the black hole of DAG-based orchestration. All you do is write a query in SQL to declare your transformation, and SQLake will turn it into a continuous pipeline that scales to petabytes and delivers up to the minute fresh data. SQLake supports a broad set of transformations, including high-cardinality joins, aggregations, upserts and window operations. Output data can be streamed into a data lake for query engines like Presto, Trino or Spark SQL, a data warehouse like Snowflake or Redshift., or any other destination you choose. Pricing for SQLake is simple. You pay $99 per terabyte ingested into your data lake using SQLake, and run unlimited transformation pipelines for free. That way data engineers and data users can process to their heart’s content without worrying about their cloud bill. For data engineering podcast listeners, we’re offering a 30 day trial with unlimited data, so go to dataengineeringpodcast.com/upsolver today an

The "CompTIA Data+: DAO-001 Certification Guide" is your complete resource to approaching and passing the CompTIA Data+ certification exam. This book offers clear explanations, step-by-step exercises, and practical examples designed to help you master the domain concepts essential for the DAO-001 exam. Prepare confidently and expand your career opportunities in data analytics. What this Book will help me do Understand and apply the five domains covered in the DAO-001 certification exam. Learn data preparation techniques such as collection, cleaning, and wrangling. Master descriptive statistical methods and hypothesis testing to analyze data. Create insightful visualizations and professional reports for stakeholders. Grasp the fundamentals of data governance, including data quality standards. Author(s) Cameron Dodd is an experienced data analyst and educator passionate about breaking down complex concepts. With years of teaching and hands-on analytics expertise, he has developed a student-centric approach to helping professionals achieve certification and career advancement. His structured yet relatable writing style makes learning intuitive. Who is it for? The ideal readers of this book are data professionals aiming to achieve CompTIA Data+ certification (DAO-001 exam), individuals entering the growing field of data analytics, and professionals looking to validate or expand their skills. Whether you're starting from scratch or solidifying your knowledge, this book is designed for all levels.

We talked about:

Irina’s background Irina as a mentor Designing curriculum and program management at AI Guild Other things Irina taught at AI Guild Why Irina likes teaching Students’ reluctance to learn cloud Irina as a manager Cohort analysis in a nutshell How Irina started teaching formally Irina’s diversity project in the works How DataTalks.Club can attract more female students to the Zoomcamps How to get technical feedback at work Antipatterns and overrated/overhyped topics in data analytics Advice for young women who want to get into data science/engineering Finding Irina online Fundamentals for data analysts Suggestions for DataTalks.club collaborations Conclusions

Links:

LinkedIn Account: https://www.linkedin.com/in/irinabrudaru/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

FUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.

"Data Literacy in Practice" teaches readers to unlock the power of data for making smarter decisions. You'll learn how to understand and work with data, gain the ability to derive actionable insights, and develop the skills required for data-informed decision-making. What this Book will help me do Understand the basics of data literacy and the importance of data in decision-making. Learn to visualize data effectively using charts and graphs tailored to your audience. Master the application of the four-pillar model for organizational data literacy advancement. Develop proficiency in managing data environments and assessing data quality. Become competent in deriving actionable insights and critical questioning for better analysis. Author(s) Angelika Klidas and Kevin Hanegan are pioneers in the field of data literacy with extensive experience in data analytics. Both are seasoned educators at top universities and bring their expertise to this book to help readers understand and leverage the power of data. Who is it for? "Data Literacy in Practice" is ideal for data analysts, professionals, and teams looking to enhance their data literacy skills. Readers should have a desire to utilize data effectively in their roles, regardless of prior experience. The book is designed to guide both beginners starting out and those who aim to deepen their knowledge.

Data Analytics has played a major role in Chelsea’s journey to becoming the seventh most valuable football club in the world, Chelsea has won six league titles, eight FA Cups, five League Cups, and two Champions League titles.

Today, we are going behind the scenes at Chelsea FC to see how they use data analytics to analyze matches, inform tactical decision-making, and drive matchday success in one of the world’s top football leagues, just in time for the 2022 FIFA World Cup in Qatar!

Federico Bettuzzi is a Data Scientist at Chelsea FC. As a specialist in match analytics, Federico works with Chelsea’s first team to inform tactical decision making during matches. Federico joins the show to break down how he gathers and synthesizes data, how they develop match analyses for tactical reviews, how managers prioritize data analytics differently, how to balance long-term and short-term projects, and much more.

Summary One of the most impactful technologies for data analytics in recent years has been dbt. It’s hard to have a conversation about data engineering or analysis without mentioning it. Despite its widespread adoption there are still rough edges in its workflow that cause friction for data analysts. To help simplify the adoption and management of dbt projects Nandam Karthik helped create Optimus. In this episode he shares his experiences working with organizations to adopt analytics engineering patterns and the ways that Optimus and dbt were combined to let data analysts deliver insights without the roadblocks of complex pipeline management.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing Nand

Data Storytelling with Google Looker Studio is your definitive guide to creating compelling dashboards using Looker Studio. In this book, you'll journey through the principles of effective data visualization and learn how to harness Looker Studio to convey impactful data stories. Step by step, you'll acquire the skills to design, build, and refine dashboards using real-world data. What this Book will help me do Understand and apply data visualization principles to enhance data analysis and storytelling. Master the features and capabilities of Google Looker Studio for dashboard building. Learn to use a structured 3D approach - determine, design, and develop - for creating dashboards. Explore practical examples to apply your knowledge effectively in real projects. Gain insights into monitoring and measuring the impact of Looker Studio dashboards. Author(s) Sireesha Pulipati is an accomplished data analytics professional with extensive experience in business intelligence tools and data visualization. Leveraging her years of expertise, she has crafted this book to empower readers to effectively use Looker Studio. Sireesha's approachable teaching style and practical insights make complex concepts accessible to learners. Who is it for? This book is perfect for aspiring data analysts eager to master data visualization and dashboard design. It caters to beginners and requires no prior experience, making it a great starting point. Intermediate and seasoned professionals in analytics and business intelligence who are keen on using Looker Studio effectively will find immense value as well. If you aim to create insightful dashboards and refine your data storytelling skills, this book is for you.

Today I’m chatting with Iván Herrero Bartolomé, Chief Data Officer at Grupo Intercorp. Iván describes how he was prompted to write his new article in CDO Magazine, “CDOs, Let’s Get Out of Our Comfort Zone” as he recognized the importance of driving cultural change within organizations in order to optimize the use of data. Listen in to find out how Iván is leveraging the role of the analytics translator to drive this cultural shift, as well as the challenges and benefits he sees data leaders encounter as they move from tactical to strategic objectives. Iván also reveals the number one piece of advice he’d give CDOs who are struggling with adoption. 

Highlights / Skip to:

Iván explains what prompted him to write his new article, “CDOs, Let’s Get Out of Our Comfort Zone” (01:08) What Iván feels is necessary for data leaders to close the gap between data and the rest of the business and why (03:44) Iván dives into who he feels really owns delivery of value when taking on new data science and analytics projects (09:50) How Iván’s team went from managing technical projects that often didn’t make it to production to working on strategic projects that almost always make it to production (13:06) The framework Iván has developed to upskill technical and business roles to be effective data / analytics translators (16:32) The challenge Iván sees data leaders face as they move from setting and measuring tactical goals to moving towards strategic goals and initiatives (24:12) Iván explains how the C-Suite’s attitude impacts the cross-functional role of data & analytics leadership (28:55) The number one piece of advice Iván would give new CDO’s struggling with low adoption of their data products and solutions (31:45)

Quotes from Today’s Episode “We’re going to do all our best to ensure that [...] everything that is expected from us is done in the best possible way. But that’s not going to be enough. We need a sponsorship and we need someone accountable for the project and someone who will be pushing and enabling the use of the solution once we are gone. Because we cannot stay forever in every company.” – Iván Herrero Bartolomé (10:52)

“We are trying to upskill people from the business to become data translators, but that’s going to take time. Especially what we try to do is to take product owners and give them a high-level immersion on the state-of-the-art and the possibilities that data analytics bring to the table. But as we can’t rely on our companies having this kind of talent and these data translators, they are one of the profiles that we bring in for every project that we work on.” – Iván Herrero Bartolomé (13:51)

“There’s a lot to do, not just between data and analytics and the other areas of the company, but aligning the incentives of all the organization towards the same goals in a way that there’s no friction between the goals of the different areas, the people, [...]  and the final goals of the organization. – Iván Herrero Bartolomé (23:13) “Deciding which goals are you going to be co-responsible for, I think that is a sophisticated process that it’s not mastered by many companies nowadays. That probably is one of the main blockers keeping data analytics areas working far from their business counterparts” – Iván Herrero Bartolomé (26:05)

“When the C-suite looks at data and analytics, if they think these are just technical skills, then the data analytics team are just going to behave as technical people. And many, many data analytics teams are set up as part of the IT organization. So, I think it all begins somehow with how the C-suite of our companies look at us.” – Iván Herrero Bartolomé (28:55) “For me, [digital] means much more than the technical development of solutions; it should also be part of the transformation of the company, both in how companies develop relationships with their customers, but also inside how every process in the companies becomes more nimble and can react faster to the changes in the market.” – Iván Herrero Bartolomé (30:49) “When you feel that everyone else not doing what you think they should be doing, think twice about whether it is they who are not doing what they should be doing or if it’s something that you are not doing properly.” – Iván Herrero Bartolomé (31:45)

Links “CDOs, Let’s Get Out of Our Comfort Zone”: https://www.cdomagazine.tech/cdo_magazine/topics/opinion/cdos-lets-get-out-of-our-comfort-zone/article_dce87fce-2479-11ed-a0f4-03b95765b4dc.html LinkedIn: https://www.linkedin.com/in/ivan-herrero-bartolome/

Data plays a vital role in helping companies develop a competitive advantage, but it's the data evangelist who gathers and leverages those insights to help organizations understand the story their data is telling them. Today, on Analytics on Fire, we discuss how to become a data evangelist with data storyteller, leader, and lifelong learner, Santosh Kanthethy. At the time of recording this episode, Santosh was the IT Technology Manager for NextEra Energy Resources. Now, he is Head of Data Analytics and the leader of a growing internal data visualization community at EverBright, a solar financing solutions company and a subsidiary of NextEra. Tuning in, you'll gain step-by-step instructions for becoming a rockstar data evangelist , including three things to consider before you get started. We also take a look at the top functions of an internal data visualization community, how to get your executive team on board, and how to overcome some of the challenges that data evangelists are likely to encounter along the way. For actionable insights into how to build a thriving community, transform data culture from the inside out, and more, make sure not to miss this episode!   In this episode, you'll learn: [06:16] More about NextEra, one of America's largest capital investors in infrastructure. [07:10] Defining what a data evangelist is and how the internal data visualization community at NextEra was born. [08:48] Why Santosh decided to nurture and grow this community and switch from IT to data. [09:55] What the game of cricket taught Santosh about being a team leader. [13:55] Three things to consider before becoming a data evangelist: the maturity of your organization, your curiosity, and your ability to create content. [19:16] How often the data community meets and some of the topics that come up. [20:50] The three core selling points of a data community for your company: consistency better decision making, and relevance. [24:19] Tips for obtaining essential executive buy-in and support. [26:52] Becoming tool-agnostic: how to evangelize the benefits of the practice, not the tool. [29:34] A look at membership and how to determine who joins your data community. [31:40] KPIs, WIGs, and OKRs to measure the success of your community. [34:13] How data evangelists can overcome resistance while building a community. [36:20] What percentage of technology budgets should be allocated to community, change management, and upskilling. [38:50] How Santosh is inspired by the people he interacts with on a daily basis. [0:43:21] How Santosh can help you visualize your fitness data from Garmin or Strava! For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/89   Enjoyed the Show?  Please leave us a review on iTunes.

Master the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations. SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain. This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly. Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business. What You'll Learn Create and measure the success of their analytics project Start your business analytics consulting career Use solutions taught in the book in practical uses cases and problems Who This Book Is For Business analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analysts

Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own. This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "zero to hero" with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool. You'll learn how to: Import, manipulate, visualize, and investigate data in Power BI Approach solutions for both self-service and enterprise BI Use Power BI in your organization's business intelligence strategy Produce effective reports and dashboards Create environments for sharing reports and managing data access with your team Determine the right solution for using Power BI offerings based on size, security, and computational needs

Discover the power of business intelligence through Databricks SQL. This comprehensive guide explores the features and tools of the Databricks Lakehouse Platform, emphasizing how it leverages data lakes and warehouses for scalable analytics. You'll gain hands-on experience with Databricks SQL, enabling you to manage data efficiently and implement cutting-edge analytical solutions. What this Book will help me do Comprehend the core features of Databricks SQL and its role in the Lakehouse architecture. Master the use of Databricks SQL for conducting scalable and efficient data queries. Implement data management techniques, including security and cataloging, with Databricks. Optimize data performance using Delta Lake and Photon technologies with Databricks SQL. Compose advanced SQL scripts for robust data ingestion and analytics workflows. Author(s) Vihag Gupta, acclaimed data engineer and BI expert, brings a wealth of experience in large-scale data analytics to this work. With a career deeply rooted in cutting-edge data warehousing technologies, Vihag combines expertise with an approachable teaching style. This book reflects his commitment to empowering data professionals with tools for next-gen analytics. Who is it for? Ideal for data engineers, business intelligence analysts, and warehouse administrators aiming to enhance their practice with Databricks SQL. This book suits those with fundamental knowledge of SQL and data platforms seeking to adopt Lakehouse methodologies. Whether a novice to Databricks or looking to master advanced features, this guide will support professional growth.