Are you ready to discover data analytics trends in 2024?
There are many reasons to be concerned about the current state of measurement. AI, GA4, and the death of cookies are just a few issues we had to deal with for the past year.
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In this guide, we will have a look at the future of analytics and marketing tracking for 2024.
Let’s unravel these top five data analytics trends so you can make an informed decision if measurement is still something you should pay attention to in 2024:
- AI Tools and Advancements
- Privacy Regulations
- Growing Uncertainty and Trust
- Unbundling of Tools
- Increased Usage of First-Party Data
Let’s dive into the data analytics trends of 2024!
1. AI Tools and Advancements
Let’s start with the elephant in the room. AI is here.
While I was hesitant to call anything AI a year ago, we now know the capabilities of AI. Along with the rapid growth of language models like ChatGPT, it has shown that AI is here to stay.
Despite AI being all the rage in image recognition and prompt engineering, it has only partially entered the analytics field.
You can already use these tools to do more advanced data analysis work. However, its presence is not as resounding to call it as fully integrated into marketing tracking.
We haven’t seen AI help us with everyday tasks that a web analyst would do, like setting up tracking, debugging, and helping us set up a data pipeline. It may be because it is not yet integrated into our current tools.
Google has yet to unleash the power of AI in Google Analytics 4. While it is nice to see predictive metrics, it is far from the vibe that machines have taken over.
In 2024, we still need to wait a bit to see the AI use cases integrated into our everyday work as measurement folks. Part of the problem here is that we probably need to give up a bit of data to get the output from our AI models.
Herein lies the next trend we must watch in 2024, which is privacy.
2. Privacy Regulations
The topic of privacy is still the same that we have seen in the past few years. However, it is increasingly becoming something that tracking specialists worldwide have to pay attention to, even outside of Europe.
There are more and more privacy regulations in different other countries modeled after the General Data Protection Regulation (GDPR) in the European Union and the European Economic Area, so companies need to take this more seriously.
At the same time, consumers want to see their data protected. Private companies also have chimed in. For example, Apple introduced its privacy protections that will impact various other technologies.
💡 Top Tip: If you sell your products or offer your services in multiple countries, it becomes harder to check if your website/app complies with the myriad of privacy regulations. Check out these GA4 Alternatives built for privacy and inherently compliant.
Now, with the age of AI, we have a new player in town.
What happens to the private information that is sucked up by one of these AI models? How is the data handled? How is it protected once it leaves your jurisdiction and is in the hands of OpenAI, for example?
Are you allowed to use ChatGPT to analyze client data on their systems?
These are challenges that are something that every legislative body needs to figure out. They are already on it, but it won’t be easy. The problem for those of us who would like to utilize these tools is that we have massive uncertainty about the extent to which we can use these tools.
3. Growing Uncertainty and Trust
Data nowadays is often sucked up by some machine learning algorithm or AI model, and we don’t always know how the output of these models processes that data in the end. The question now is, can you trust that data?
It was more manageable in the old days to describe a tracking deployment, how that data ended up there, and what it counts. But now, it gets even more uncertain.
With server-side tagging, where you don’t see all the network requests going through, the different enrichments in your CDP, and the modeling on top of that data, you have many new variables in the results you see later.
How will you know if something went wrong in the data collection process?
Debugging is getting more complex as more variables are in the mix of our tracking deployments. It is not always possible to explain to clients what the not set value means or why the data differs from other tools.
💡 Top Tip: If a significant chunk of your data has the (not set) value, it is essential to understand what this value means and if there is a way to fix it. Check out our guide on How to Fix Not Set in GA4.
At some point, we probably need to make the jump to trust the data these tools pull out. Maybe it won’t be possible to control all the different variables.
Trust is the currency we need to pay when we utilize these tools, trusting that the machine is giving us the most accurate data possible and that we can decide on that data.
The big question comes: which tool, company, or algorithm do you trust the most? Google, Adobe, Microsoft, or other smaller companies or tools?
With the change to GA4, we have seen even larger companies willing to abandon long-term products like Google Analytics for a more transparent or, from a marketing perspective, more trustworthy product.
We are eager to see how this trust war will play out in 2024. Maybe it will continue, and we will see more tools out there. There will probably be more diversification in terms of tracking tools and not everything being concentrated on one.
4. Unbundling of Tools
GA4 is not Universal Analytics, as we all know. It is a different data model, but GA4 doesn’t try to be everything at once.
Google unbundled multiple parts of the all-in-one analytics solution it used to be and has built standalone tools separate from GA4.
You can take your raw data into their data warehousing solution, BigQuery.
Then, you can connect it to a lot of machine learning within the Google Cloud. Once done with the data processing, you can send this to Looker Studio, their data visualization tool for the whole analytics suite.
GA4 doesn’t want to be everything to everyone. It is a tiny part of the whole analytics stack.
It is a notable deployment because the cloud has become so much cheaper. For example, you can set up a GTM server to measure your data.
Maybe it is data from Matomo that you deploy through Google Tag Manager, you send that data onto your data warehousing solution, maybe on AWS, and finally, you visualize your data in your data visualization tool of choice, like Tableau.
🚨 Note: Matamo is an open-source analytics solution that lets you install their tool on your server. It is one of the tools we recommend if you’re looking for alternatives to GA4. Check out our list of the Top 5 GA4 Alternatives.
It is now possible to use these different tools because the individual pieces are interchangeable. You can rent them on a time-to-time or a usage basis, and they fit together because the data is now more compatible.
That is a trend that I see more companies are doing because they don’t want to trust just one solution. They are unbundling and using the best technology they can find for their use case, building their data pipelines, and running their analytics stack.
We think this trend will continue in 2024.
5. Increased Usage of First-Party Data
As we all know, Google is phasing out third-party cookies for tracking, and they have yet to convince me, at least, that the privacy sandbox will work or that when everyone adapts it, they will be happy with it.
This point adds to one of the data analytics trends. Data quality is being degraded.
Now, there are many probable reasons for this that we have already talked about. Maybe it is consent, model data, or third-party cookies going away.
However, we can still tap into a new wealth of data – the data you own with first-party data.
We have already seen moves from Google, Meta, and other ad vendors asking us to send them our first-party information so they can match it up with their database of known users (which I am not a big fan of).
Why aren’t you using that data when you have it for your benefit? That is what the most clever companies are already doing.
They are building up their CDPs, combining the data sources they have, and making more informed decisions with their data, utilizing it to take action and make changes to their ad vendor’s platform.
For 2024, find ways to increase first-party data on your platforms and analyze it accordingly.
It could be a login-based platform, so you can see every step of the user, or loyalty programs where you identify users based on where they are on the user journey. Whatever it is, you can increase the first-party data and get more from your existing users from the start.
Summary
These are the data analytics trends that I am seeing for 2024. To recap, AI is a big one. We also have privacy, growing uncertainty and trust of data, unbundling of your toolset, and increased first-party data.
That said, the world of measurement will be exciting this year. I can’t wait to see all the changes that are coming up. It won’t be an easy one, but interesting nonetheless.
Kickstart your year by learning all about ChatGPT and how to use it for digital marketing. Check out our guides for using ChatGPT with Google Tag Manager and BigQuery.
Do you agree with our list? What are the data analytics trends you see for 2024? Let us know your thoughts in the comments below!
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