talk-data.com talk-data.com

Topic

Analytics

data_analysis insights metrics

4552

tagged

Activity Trend

398 peak/qtr
2020-Q1 2026-Q1

Activities

4552 activities · Newest first

In today’s episode, I’m going to perhaps work myself out of some consulting engagements, but hey, that’s ok! True consulting is about service—not PPT decks with strategies and tiers of people attached to rate cards. Specifically today, I decided to reframe a topic and approach it from the opposite/negative side. So, instead of telling you when the right time is to get UX design help for your enterprise SAAS analytics or AI product(s), today I’m going to tell you when you should NOT get help! 

Reframing this was really fun and made me think a lot as I recorded the episode. Some of these reasons aren’t necessarily representative of what I believe, but rather what I’ve heard from clients and prospects over 25 years—what they believe. For each of these, I’m also giving a counterargument, so hopefully, you get both sides of the coin. 

Finally, analytical thinkers, especially data product managers it seems, often want to quantify all forms of value they produce in hard monetary units—and so in this episode, I’m also going to talk about other forms of value that products can create that are worth paying for—and how mushy things like “feelings” might just come into play ;-)  Ready?

Highlights/ Skip to:

(1:52) Going for short, easy wins (4:29) When you think you have good design sense/taste  (7:09) The impending changes coming with GenAI (11:27) Concerns about "dumbing down" or oversimplifying technical analytics solutions that need to be powerful and flexible (15:36) Agile and process FTW? (18:59) UX design for and with platform products (21:14) The risk of involving designers who don’t understand data, analytics, AI, or your complex domain considerations  (30:09) Designing after the ML models have been trained—and it’s too late to go back  (34:59) Not tapping professional design help when your user base is small , and you have routine access and exposure to them   (40:01) Explaining the value of UX design investments to your stakeholders when you don’t 100% control the budget or decisions 

Quotes from Today’s Episode “It is true that most impactful design often creates more product and engineering work because humans are messy. While there sometimes are these magic, small GUI-type changes that have big impact downstream, the big picture value of UX can be lost if you’re simply assigning low-level GUI improvement tasks and hoping to see a big product win. It always comes back to the game you’re playing inside your team: are you working to produce UX and business outcomes or shipping outputs on time? ” (3:18) “If you’re building something that needs to generate revenue, there has to be a sense of trust and belief in the solution. We’ve all seen the challenges of this with LLMs. [when] you’re unable to get it to respond in a way that makes you feel confident that it understood the query to begin with. And then you start to have all these questions about, ‘Is the answer not in there,’ or ‘Am I not prompting it correctly?’ If you think that most of this is just an technical data science problem, then don’t bother to invest in UX design work… ” (9:52) “Design is about, at a minimum, making it useful and usable, if not delightful. In order to do that, we need to understand the people that are going to use it. What would an improvement to this person’s life look like? Simplifying and dumbing things down is not always the answer. There are tools and solutions that need to be complex, flexible, and/or provide a lot of power – especially in an enterprise context. Working with a designer who solely insists on simplifying everything at all costs regardless of your stated business outcome goals is a red flag—and a reason not to invest in UX design—at least with them!“ (12:28)“I think what an analytics product manager [or] an AI product manager needs to accept is there are other ways to measure the value of UX design’s contribution to your product and to your organization. Let’s say that you have a mission-critical internal data product, it’s used by the most senior executives in the organization, and you and your team made their day, or their month, or their quarter. You saved their job. You made them feel like a hero. What is the value  of giving them that experience and making them feel like those things… What is that worth when a key customer or colleague feels like you have their back with this solution you created? Ideas that spread, win, and if these people are spreading your idea, your product, or your solution… there’s a lot of value in that.” (43:33)

“Let’s think about value in non-financial terms. Terms like feelings. We buy insurance all the time. We’re spending money on something that most likely will have zero economic value this year because we’re actually trying not to have to file claims. Yet this industry does very well because the feeling of security matters. That feeling is worth something to a lot of people. The value of feeling secure is something greater than whatever the cost of the insurance plan. If your solution can build feelings of confidence and security, what is that worth? Does “hard to measure precisely” necessarily mean “low value?”  (47:26)

In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technologies make healthcare more efficient and affordable for everyone involved?  Travis Dalton is the President and CEO at Multiplan overseeing the execution of the company's mission and growth strategy. He has 20 years of leadership experience, with a focus on reducing the cost of healthcare, and enabling better outcomes for patients and healthcare providers. Previously, he was a General Manager and Executive VP at Oracle Health. Jocelyn Jiang is the Vice President of Data & Decision Science at MultiPlan, a role she has held since 2023. In her position, she is responsible for leading the data and analytics initiatives that drive the company’s strategic growth and enhance its service offerings in the healthcare sector. Jocelyn brings extensive experience from her previous roles in healthcare and data science, including her time at EPIC Insurance Brokers & Consultants and Aon, where she worked in various capacities focusing on health and welfare consulting and actuarial analysis. In the episode, Richie, Travis and Jocelyn explore the US healthcare system and the industry-specific challenges professionals face, the role of data in healthcare, ML and data science in healthcare, the future potential of healthcare tech, the global application of healthcare data solutions and much more.  Links Mentioned in the Show: MultiplanPlanOptix: Providing Innovative Healthcare Price Transparency   Using a Data Mining Service on Claims Data Can Reveal Significant OverpaymentsConnect with Travis and JocelynCourse: Intro to Data PrivacyRelated Episode: Data & AI for Improving Patient Outcomes with Terry Myerson, CEO at TruvetaRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

podcast_episode
by Matt Colyar (Moody's Analytics) , Cris deRitis , Mark Zandi (Moody's Analytics) , Marisa DiNatale (Moody's Analytics)

The Inside Economics team breaks down the latest inflation data -- August’s consumer price index. They unpack the underlying components, focusing most of their attention on the confounding acceleration in shelter inflation. “Eggflation” makes a return to the podcast as well. Nevertheless, U.S. inflation has cooled considerably, and the Fed is set to start lowering their policy rate at next week’s meeting. What will that mean for U.S. consumers and businesses? Finally, Marisa takes some listener questions and Matt reads some (mostly positive) reviews of the Inside Economics podcast.    Guest: Matt Colyar - Assistant Director, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Analytics Engineering is one of the hottest career paths in data today, but many struggle to understand how to break in and where they should focus. In this episode, we demystify Analytics Engineering. Madison Schott talks about the career path of an Analytics Engineer, what they do for companies, and the tools they use. She also shares some of the best strategies and actionable advice for those looking to break into Analytics Engineering or take their career to the next level.   What You'll Learn: The day-to-day responsibilities and potential career paths of an Analytics Engineer The skills you should focus on if you want to break into Analytics Engineering Tips for networking, finding jobs, and acing the Analytics Interview   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Madison Schott is the Senior Analytics Engineer at ConvertKit and Author of the Learn Analytics Engineering newsletter Sign up for Madison's newsletter The ABCs of Analytics Engineering E-Book Follow Madison on LinkedIn

Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away!  There's one thing that will kill a data career analyst job hunt faster than anything else...and it's really easy to do. Please don't do it! 💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com//interviewsimulator ⌚ TIMESTAMPS (01:39) Solution #1 (06:42) Solution #2 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website


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

Today, we’re joined by David Abramson, Chief Technology Officer (CTO) at Qrvey. We talk about:  The evolution of embedded analytics and the latest trendsRising complexity in how companies deliver content to usersRewards of embedding analytics + risks and how to reduce themOften overlooked aspects of building analytics in-houseChallenges of licensing analytics for internal vs. external use

Mergulhamos no universo da Inteligência Artificial e da liderança de dados no Brasil. Para isso, convidamos os principais lideres do conselho do evento AI & Data Leaders — o maior encontro entre CDAOs, tomadores de decisão em AI e Data Analytics do Brasil-, para discutir os desafios, oportunidades dessa área em constante evolução e como as empresas estão utilizando a IA para impulsionar seus negócios.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Carina Ameijeiras — Executiva de Data & Analytics e atuante no conselho AI Data Leaders; Daniel Sérman — Diretor Executivo da TIM e membro do Conselho AI Data Leaders.

[Embedar_Episódio]

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.

Monique Femme — Head of Community Management na Data Hackers

Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Dr. Eirini Kalliamvakou is a senior researcher at GitHub Next. Eirini has built a career on studying software engineers, how to measure their productivity, how developer experience impacts productivity, and more. Recently, Eirini has been working on quantifying the impacts of GitHub Copilot. Does it actually help software engineers be more productive? Tristan and Eirini explore how to quantify developer productivity in the first place, and finally, arriving at whether or not Copilot‌ makes a difference. In the search for real business value, this research is a real bellwether of things to come. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs. Join data practitioners and data leaders this October in Las Vegas at Coalesce, the analytics engineering conference hosted by dbt Labs. Register now at coalesece.getdbt.com. Listeners of this show can use the code podcast20 for a 20% discount.

podcast_episode
by Dante DeAntonio (Moody's Analytics) , Cris deRitis , Mark Zandi (Moody's Analytics) , Marisa DiNatale (Moody's Analytics)

The Inside Economics crew gathers in Southern California for an early morning reaction to the August jobs report, which they all concur is “pretty good”. They discuss the implications of slowing job growth for the Fed’s upcoming meetings as well as the presidential election. Finally, they all give their odds for a recession occurring in the next year—Cris remains the bear of the group.   Guest: Dante DeAntonio - Senior Director, Economic Research Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

In this episode you'll hear best practices for building and leading analytics teams, the impact AI is already making on the industry, and how data teams should be thinking about the future.   Director of Analytics & Data Science Joe Squire will share practical tips for data leaders looking to build effective teams and navigate through today's rapidly changing environment, and give his thoughts on where things are headed.   What You'll Learn: Best practices for building and leading strong analytics teams The ways AI is changing analytics and where the data industry is headed How leaders need to be thinking AI and the long-term impact on their teams   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Joe Squire is a Director of Analytics & Data Science in the healthcare industry. Joe helps companies like UPMC manage and use their data in a meaningful way to improve their healthcare outcomes. Follow Joe on LinkedIn

Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

In this episode, host Jason Foster sits down with Anthony Deighton, CEO at Tamr, to delve into the complexities of data quality and analytics. They explore the challenges organisations face in managing and improving data quality, the pivotal role of AI in addressing these challenges, and strategies for aligning data quality initiatives with business objectives. They also explore the evolving role of central data teams, led by Chief Data Officers, in spearheading enterprise-wide data quality initiatives and how businesses can effectively tackle key challenges.


Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. They work with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and change management and leadership. The company was named one of The Sunday Times' fastest-growing private companies in 2022 and 2023 and named the Best Place to Work in Data by DataIQ in 2023.

Statistics for Data Science and Analytics

Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.

In this episode, Rachael Finch shares her incredible journey of transitioning from a night shift quality assurance analyst at an alcohol manufacturing company to a fully remote business intelligence analyst at Optum Healthcare within just 95 days. Rachael, a biology major, leveraged the SPN Method from The Data Analytics Accelerator to break into the data industry. Tune in to hear her inspiring story and practical advice for those looking to make a similar career shift. 06:25 Networking and the SPN Method 13:40 Interview Process and Challenges 19:34 Landing the Job and Celebrating Success 23:32 Reflections and Future Plans 29:12 Final Thoughts and Advice 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

Due to a technical glitch that ended up unpublishing this episode right after it originally was released, Episode 151 is a replay of my conversation with Zalak Trivdei from this past March . Please enjoy our chat if you missed it the first time around!

Thanks,

Brian

Links Original Episode: https://designingforanalytics.com/resources/episodes/139-monetizing-saas-analytics-and-the-challenges-of-designing-a-successful-embedded-bi-product-promoted-episode/ 

Sigma Computing: https://sigmacomputing.com

Email: [email protected] 

LinkedIn: https://www.linkedin.com/in/trivedizalak/

Sigma Computing Embedded: https://sigmacomputing.com/embedded

About Promoted Episodes on Experiencing Data: https://designingforanalytics.com/promoted