Josh Crowhurst joins as a guest to discuss reflections on the analytics and data science industry. Topics include AI, Google Analytics 4, marketing mix modeling, generative AI, evolving user tracking in marketing, and the impact of technology complexity on those who get paid.
Generative AI is gaining attention and organizations need to focus on skill-building and reskilling to effectively utilize this technology.
Marketing mix modeling is becoming more accessible and organizations should connect it with experimentation while balancing simplicity and complexity.
The demand for analytics engineering talent is rising, highlighting the importance of developing data engineering and infrastructure skills.
Deep dives
Generative AI and its Impact in 2024
Generative AI is expected to continue its upward trajectory in 2024, with businesses and organizations increasingly exploring its potential applications. Although still in the early stages, generative AI's ability to create content and its impact on industries such as marketing and elections is gaining attention. As companies dive deeper into generative AI, skill-building and reskilling will become vital in order to effectively utilize this technology. It will be interesting to see how organizations balance the need for AI expertise with reskilling their existing workforce. The key is to stay focused on the business problems and opportunities and ensure that generative AI is aligned with those goals.
Marketing Mix Modeling (MMM) Gains Traction
Marketing mix modeling has gained momentum as organizations look to uncover insights and drive strategic decisions. The availability of technology and open-source solutions has made MMM more accessible. Additionally, there is a rise in vendors catering to MMM, offering dedicated platforms and services. As organizations embrace MMM, it is crucial to connect it with experimentation and ensure a balance between the simplicity of asking the right questions and the complexity of data analysis.
The Push for Analytics Engineering
With the increasing focus on data-driven decision-making, the demand for analytics engineering talent is on the rise. Organizations, including those new to the field, are realizing the importance of establishing and managing marketing data warehouses. This has created a need for analytics engineers who possess the skills to structure and leverage data effectively. The development of data engineering and infrastructure talent will play a critical role in managing the complexity and reducing dependence on external tools and consultants.
Simplifying the Analytics Landscape
Amidst the growing complexity of tools, technologies, and data sources, it is important to recognize the need to simplify analytics. Chasing after the latest tools and collecting vast amounts of data can lead to inefficiencies and unnecessary complexities. Focusing on the business problems and identifying the right questions can help organizations avoid getting lost in the data maze. Building soft skills, including communication and problem-solving, is also crucial in order to effectively navigate the evolving analytics landscape.
Learning from Mistakes and Adapting
In the ever-changing analytics industry, learning from mistakes and adapting is crucial for growth and success. Rather than being discouraged by failures, embracing them as learning opportunities can lead to valuable insights and improvements. It is important to stay curious, open-minded, and willing to learn from both successes and failures. Approaching challenges with a growth mindset will enable professionals to evolve and stay ahead in the rapidly changing analytics landscape.
For those who celebrate or acknowledge it, Christmas is now in the rearview mirror. Father Time has a beard that reaches down to his toes, and he’s ready to hand over the clock to an absolutely adorable little Baby Time when 2024 rolls in. That means it’s time for our annual set of reflections on the analytics and data science industry. Somehow, the authoring of this description of the show was completely unaided by an LLM, although the show did include quite a bit of discussion around generative AI. It also included the announcement of a local LLM based on all of our podcast episodes to date (updated with each new episode going forward!), which you can try out here! The discussion was wide-ranging beyond AI: Google Analytics 4, Marketing Mix Modelling (MMM), the technical/engineering side of analytics versus the softer skills of creative analytical thought and engaging with stakeholders, and more, as well as a look ahead to 2024! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode