What’s happening in Digital Analytics in 2023
When you go online or to conferences you can find a ton of resources about Digital Analytics. The vast majority of content seems to be related to marketing technology. The transition from Google Universal Analytics to Google Analytics 4 is an instigator for martech providers and consultants to focus on technology in digital analytics.
I appreciate the value of collecting quality data in a technical solution for analysis and configuring tools to get the most out of them, but don’t be fooled, there’s more going on in the digital analytics space. And it deserves your attention. If I’ve learned anything in 15 years in digital analytics, it’s that digital data is useless, unless it is applied in a smart and effective way to provide value to the business. So let’s explore some other themes in digital analytics. I’ll share some resources for you to dive into topics a bit deeper.
The topics I will discuss are:
- Create value from data
- Data products
- Data contracts
- Data Privacy and the impact on society
- Sustainable Analytics
- Women in Analytics
Create value from data
Traditionally, digital analytics has been in a supporting role. Help to support digital marketing teams deliver great ROI on marketing investments or optimize functionalities or features on a website. It’s also known for providing insights and dashboards to help senior management monitor performance and make data informed decisions. It has always been difficult to prove the value of digital analytics. As analysts we basically create value through the work of others.
When you are working in (digital) data, there are many opportunities to create value beyond this supporting role. The value of digital analytics can go beyond maintaining a quality data pipeline and technology stack for others to use. Tim Wilson wrote an interesting article about all digital analytics work and how it can be split in two categories. Data collection and data usage. It should make you think about how you split your time and resources between these two categories.
Recent research among Chief Data Officers has resulted in some interesting recommendations to create more value from data. It provides some helpful ways to focus more on data usage.
Among these recommendations are:
- At lower levels of maturity, focus on a few key projects of value to stakeholders.
- Focus on data products
- Measure and document results
- Build relationships with peers and business leaders who get it
These and other strategies are discussed in the article 8 Strategies for Chief Data Officers to Create - and Demonstrate - Value in Harvard Business Review.
I want to look at the second recommendation from the last section in a bit more detail, Data Products.
Companies are collecting enormous amounts of data. This has been the main focus of many data efforts in companies. Collect as much data as you can and afterwards, figure out what you can do with it. Data teams are often managing these data flows like plumbers by fixing data collection or data technology issues. Digital analysts are usually not included in activating the data and creating value from it.
Data Products are a way for data teams to deliver value from data. You can think of a data product as a self-managed data “container” that directly solves a business problem or monetizes the data. They are built for internal or external users. Data products can come in all shapes and sizes. Anything from a dashboard and application or a ML model that can be used for predicting churn. None of this is new, but the approach to data products is. In the past, the job of data professionals ended by delivering the technical parts of the process, with data products there is a shift of focus from technical delivery to business outcomes. It is a shift toward product thinking in data.
Forbes shares the 5 characteristics of a data product and McKinsey discusses how you can manage data as a product. If you’re interested in using data as a product, I can recommend the “Experiencing Data” podcast by Brian O’Neill. He discusses how to implement and monetize data products with several seasoned practitioners.
Data is created by many departments and teams inside the company. Consider marketing teams generating data about user’s interest and product affinities. Or your product team, who collect data about how users interact with products and services. All of this data that is produced by one team is used by others in the organisation. For instance, data collected by marketing teams about user interest can be leveraged by product teams to personalize their product experiences.
Other use of data by other teams can be helpful to optimize experiences and drive the business forward.
Working together with Data Contracts
This system of data exchange causes issues related to dependencies. What if the data producing team is not delivering the data that is required by the data consuming team or the team doesn’t value some of the data they are producing? This is where Data Contracts come into play. Data producers commit to producing data that adheres to a specific contract. This will create an understanding between teams to work together and drive results for the company as a whole.
Yali Sassoon (Snowplow Analytics) has written an excellent post about Data Contracts. He outlines some of the pros and cons of data contracts and discusses the concept in more dept. For a nice introduction to data contracts, you can also listen to the Analytics Power Hour episode discussing this topic.
Data Privacy and the impact on society
One of the most important developments in data is the impact data use has on privacy and the implications on ethics that are impacted by our work. We collect enormous amounts of data to drive our business, but often forget the impact this has on society and our customers. There are many resources about technical ways to respect privacy and the way we can manage data sources and technical solutions to respect the privacy of our users.
I would like to pivot this conversation to learn more about this topic from a different perspective, the one that reviews the impact on the lives of everyday people in society. My favorite book about this topic is “Privacy is Power” by Carissa Véliz. It gives a short and practical perspective on the politics of privacy.
I first heard the term Sustainable Analytics when listening to the 33 Tangents Podcast from the agency 33 Sticks. Sustainable Analytics is a result of two different considerations.
Data hoarding - how much of our data do we really use?
Sustainable Analytics asks you to reconsider this approach and only collect data and use tools that drives value for your business. You should also consider the effort and time it takes to maintain the tool and dataset after implementation has finished. This approach will free up resources and help you focus on what matters.
Collecting useless data takes up natural resources
The other consideration for Sustainable Analytics is that we are also part of the real world, where data is collected on servers. Collecting data is not “free”. It impacts the environment and the world we live in. According to Statista, there are over 8 million data centers in the world. They consume an enormous amount of energy. About 2% of all carbon emissions are coming from these data centers. Let’s take our responsibility and stop hoarding useless data.
Update (30-3-2023): Yesterday, I watched a great presentation by Dumky de Wilde, data engineer from Xebia at their Data Engineering Meetup. He shared how much each query, or other data resource has an impact on CO2 emissions. Check out this cool dashboard he created to pick the right server for the lowest impact on CO2.
Women in Analytics
I’ve been in Digital Analytics long enough to remember this was a boys club. Seeing women work in digital analytics was an exception. Over the past 10 years this has slowly changed. By now, most digital analytics teams still consist mostly of men, but women are becoming a growing share of the workforce.
Unfortunately, women and men are not equally represented in all parts of the digital analytics community yet. Keynotes at conferences are still mostly men. Just like leadership roles in data teams are often still filled by men. And then there’s also the significant pay gap between men and women which is still there. I’m not going to pretend I have the solution for these issues, but I will encourage everyone to support initiatives to change this.
Here are some ways to support a more inclusive digital analytics community:
- Women in Analytics Amsterdam Meetup - become a speaker (women only), recommend a speaker or simply join the meetup to support and learn from the women on stage.
- Measurecamp - A global unconference that encourages new speakers to take the stage
- Measure Summit - A virtual conference, currently looking for speakers for their 2023 event
- Digital Analytics Summit - A conference, currently on the lookout for (female) speakers to join the line-up (contact me if you like to be a part of this event)
- Join the conversation on Diversity in Analytics on #Measure Slack, a place where people share and discuss topics related to diversity in analytics