Every team wants to improve, optimize, reach goals, to grow. Every team also has a massive pile of ideas on what to work on next. Prioritization is the key to success, ensuring they work on stuff with a high chance of making an impact. As an analyst, you should deliver the insights that feed the prioritization. Did you know that analytics is just a tiny part of those insights? In this talk, I'll show you the best sources to analyze to develop insights that optimize your prioritization.

Marketing teams need analytics. Product teams need analytics. There are marketing analytics vendors like Google Analytics and Adobe Analytics. There are product analytics vendors like Amplitude and Mixpanel. But what are the real differences between marketing and product analytics? Do organizations need different teams for each? Should organizations use both types of tools? Will one replace the other or will these disciplines converge?

With the current economic challenges, layoffs and budget cuts proofing the value of analytics and making the case of how it could be used to bring value to organizations is of an increasing importance. In this talk Ibrahim will cover this topic and will suggest a framework and ways of working to proof and communicate that value.

Join us for a deep dive into the analytics and marketing tech stack of the future! Drawing inspiration from the lovable robot Johnny 5 in the classic film Short Circuit, we'll explore the critical role that data (aka input) plays in driving marketing and analytics success. Discover how innovative tools like customer data platforms and reverse ETL platforms are revolutionizing the way we build and improve our analytics infrastructure and tech stacks. Learn how to stay ahead of the curve in a much more data-driven world and discover how to make use of the power of more input for your own organization!

In the age of GDPR, CCPA, and HIPAA along with the widespread proliferation of ad blockers and Apple’s ITP, it’s never been harder to measure the effectiveness of your digital marketing efforts while protecting your customers' data.

In August 2020 Matteo and Roberto met for the first time a fascinating creature with a cumbersome name: App + Web. It was not love at first sight. After all, they were already engaged in a longtime romance with Universal Analytics.

When most Marketers think of Analytics, they think of clickstream data… the clicks, visits, views and hopefully the conversions. But Analytics is so much more than that. Analytics is applying your brain for discovery, interpretation, and communication of meaningful patterns in data.

In the past, digital analytics experienced a boom with the emergence of tag management systems. These tools allowed marketers to quickly and easily implement tracking and marketing tags without relying on web developers, improving speed and agility for those eager to take advantage of new opportunities. And we all know how eager marketers are.

As analysts we are well aware of the legal and moral boundaries that come with handling delicate data. But imagine for a moment that behind a desk at your competitor's office was an analyst unbothered by those boundaries. His only goal is to get a competitive advantage by exploiting the weak points in your analytics pipeline. He wants you to waste as much time, money and credibility while you deal with polluted source data, spam, data breaches and legal DDoS attacks.

Digital analytics data is almost exclusively data that exists over time. That is such an obvious fact that it's easy to blow right past some important considerations and techniques that are unique or (or, at least, present in) time-series data. Stationarity (and, often, the lack thereof) and seasonality (annual...and intra-week!) alone are important and intriguing considerations when it comes to time-series data. In this session, Tim will do a high-level review of some key concepts and some of his favorite techniques for responsibly working with time-series data.

A lot of information has been shared about "normal" website user interface testing using Cypress, an end-to-end testing tool, but not so much about testing analytics implementations. You can set up tests of your website features, but also tests of how your tag management system, and added tags, are running on your web pages.

Every analyst has their favorite analytics toolset. They may use many, or may only know one platform inside and out. In this panel we'll dive into the pros and cons of the biggest analytics platforms out there and have a healthy debate over where the future of the analytics industry will go.

GA4 brought an insane amount of work to anyone using Universal Analytics currently. HelloFresh decided to clean up its implementation and do things "out of the box".

Mark and Cris welcome Matthew Kahn, Provost Professor of Economics at the University of Southern California, and colleague Gaurav Ganguly, to discuss climate change and Matthew's optimistic view on adaptation to global temperature rise. Full Episode Transcript For more from Matthew Kahn follow him on twitter: @mattkahn1966 Recommended Reads: Adapting to Climate Change Climate Change Adaptation Climatopolis What will Climate Change Cost You Climatopolis Revisited Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight  

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.

Summary

Business intelligence has gone through many generational shifts, but each generation has largely maintained the same workflow. Data analysts create reports that are used by the business to understand and direct the business, but the process is very labor and time intensive. The team at Omni have taken a new approach by automatically building models based on the queries that are executed. In this episode Chris Merrick shares how they manage integration and automation around the modeling layer and how it improves the organizational experience of business intelligence.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Your host is Tobias Macey and today I'm interviewing Chris Merrick about the Omni Analytics platform and how they are adding automatic data modeling to your business intelligence

Interview

Introduction How did you get involved in the area of data management? Can you describe what Omni Analytics is and the story behind it?

What are the core goals that you are trying to achieve with building Omni?

Business intelligence has gone through many evolutions. What are the unique capabilities that Omni Analytics offers over other players in the market?

What are the technical and organizational anti-patterns that typically grow up around BI systems?

What are the elements that contribute to BI being such a difficult product to use effectively in an organization?

Can you describe how you have implemented the Omni platform?

How have the design/scope/goals of the product changed since you first started working on it?

What does the workflow for a team using Omni look like?

What are some of the developments in the broader ecosystem that have made your work possible?

What are some of the positive and negative inspirations that you have drawn from the experience that you and your team-mates have gained in previous businesses?

What are the most interesting, innovative, or unexpected ways that you have seen Omni used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Omni?

When is Omni the wrong choice?

What do you have planned for the future of Omni?

Contact Info

LinkedIn @cmerrick on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Omni Analytics Stitch RJ Metrics Looker

Podcast Episode

Singer dbt

Podcast Episode

Teradata Fivetran Apache Arrow

Podcast Episode

DuckDB

Podcast Episode

BigQuery Snowflake

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Materialize:

Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use.

Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features.

Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses.

Go to materialize.comSupport Data Engineering Podcast

The Q4 GDP Report has been released and Mark, Cris, and Marisa analyze the weirdness surrounding the data. Colleague, Gaurav Ganguly, joins the podcast to give a European perspective and Mark continues his domination in the statistics game. Full episode transcript To learn more about Moody's Analytics Summit 2023 & register, click here. Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight

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.

Mike Stonebraker is a veritable database pioneer and a Turing Award recipient. In addition to teaching at MIT, he is a serial entrepreneur and co-creator of Postgres. Andy Palmer is a veteran business leader who serves as the CEO of Tamr, a company he co-founded with Mike. Through his seed fund Koa Labs, Andy has helped found and/or fund numerous innovative companies in diverse sectors, including health care, technology, and the life sciences.  In this conversation with Tristan and Julia, Mike and Andy take us through the evolution of database technology over 5+ decades. They share unique insights into relational databases, the switch from row-based to columnar databases, and some of the patterns of database adoption they see repeated over time. For full show notes and to read 7+ 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.

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

On today’s episode, we’re talking to Si Dhanak. Si is the VP of Product at Latch, a company aimed at making spaces better places to live, work and visit. Here’s some of what we talk about:

• Si’s background, how Latch works and the problems it solves.
• Hardware companies that move into software and vice versa.
• Do you need to choose between selling to businesses and selling to consumers?
• Capitalizing on the transition from analog to digital.
• Creating trust in digital innovations around physical security.
• When is the right time to start a software business?
• The challenges around valuing SaaS businesses.
• Advice for people who want to start a SaaS company.

Si Dhanak - https://www.linkedin.com/in/saayuj/ Latch - https://www.linkedin.com/company/latch/

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

Inflation is rising, interest rates are up across the globe and cash is king again. How will this impact the flow of venture investments in start-ups and emerging technologies? While traditional investments may suffer during a recession, the venture capital industry has historically been able to weather the storm and even thrive. One reason for this is that venture capital firms typically invest in early-stage companies that are not yet generating significant revenue. In fact, some of the most successful companies in recent history, such as Uber, Airbnb and Snapchat, were founded during economic downturns. The downturns created opportunities for entrepreneurs to innovate and create new solutions to problems caused by the economic conditions. Mendoza Ventures is one such investor, but with a unique approach. Mendoza’s investment strategy is focused on the verticals of AI, fintech and cybersecurity and 80% of their investments go to founders from diverse and minority groups. I recently caught up with Scott Heyes, CFO at Mendoza Ventures to understand how a venture capital firm works in practice and how he and his colleagues think about investing in the current economic climate and beyond. In this episode of Leaders of Analytics, we discuss: How Scott became the CFO at Mendoza Ventures and what a week in venture investing looks likeHow the firm decides which companies to invest inWhy Mendoza Ventures specifically back founders from diverse and minority backgrounds.Which segments within AI, fintech and cybersecurity will win or lose during a period of uncertainty, inflation, reduced access to funding and higher borrowing costs.The trends in AI, cybersecurity and fintech worth watching in the next 2-5 years, and much more.Scott on LinkedIn: https://www.linkedin.com/in/scottheyes/ Mendoza Ventures: https://mendoza-ventures.com Learn more about Annual Recurring Revenue in this episode.