Magic Numbers: Data Storytelling with Zackary Collevechio
Zackary Collevechio is a data scientist for WildFig, a data science and analytics consultancy with Pavone Group. In his role as data scientist, Collevechio develops advanced analytical solutions to assist clients in making data-driven business and marketing decisions.
LBB> What’s the number one question that clients are coming to you with when it comes to how they can better use data to enhance the creativity of their content and experiences?
Zackary> One of the most common questions is where to start. Today’s world is so data-rich that most clients have more data than they know what to do with, so guiding them in how to effectively use their data to boost creativity and performance is something we do frequently. Taking the first step in what we refer to as the “data journey” is a big milestone for many clients.
LBB> How can you make sure that data is elevating creative rather than forming a windtunnel effect and knocking all the interesting or unique edges off that make something distinctive?
Zackary> To us, elevating creative typically means increasing its performance. In that regard, we are believers in using data-driven feedback loops to gain iterative improvement. Creative is constantly monitored and updated based on the data to ensure it is connecting with our customers. If the creative lacks the uniqueness required to be effective, it will be quickly flagged for review.
LBB> Can you share with us any examples of projects you’ve worked on where the data really helped boost the creative output in a really exciting way?
Zackary> WildFig recently helped a client create customer personas using their own first-party data. Using a variety of data points collected on their customers, we were able to divide their customer base into a handful of distinct segments. This had a couple of downstream applications, but one of the uses was to create a more targeted communication framework with their existing customers.
This information allowed them to produce personalised creative for each persona and increase the chances of their communication being successfully received.
LBB> More brands are working to create their own first party data practice - how can a brand figure out whether that’s something that is relevant or important for their business?
Zackary> There is no figuring out needed - it’s relevant and important for all businesses! The first-party data you collect is a differentiator for your business, similar to your talent, culture, and brand strength. With better data you are able to operate more efficiently, both internally and externally. Formalising how your brand collects, manages, and uses first-party data is a great way to gain an edge over competitors.
LBB> We talk about data driving creativity, but what are your thoughts about approaching the use of data in a creative way?
Zackary> Using data creatively is an underrated skill for analysts to have. Data storytelling, or combining well-crafted narratives to communicate findings uncovered using data, is an effective way to convey your data-driven results to non-data folks.
This isn’t as straightforward as it seems because it requires both analytical skills and enough knowledge of the industry to communicate in language familiar to your audience. Sometimes a collaborative effort is needed to pull this off (i.e - a data-minded person completes the analysis, then an industry-minded person communicates the findings), so being able to handle both aspects of that process is a valuable skill set that requires both technical expertise and creativity.
LBB> "Lies, damned lies, and statistics" - how can brands and creative make sure that they’re really seeing what they think they’re seeing (or want to see) in the data, or that they’re not misusing data?
Zackary> The number one pitfall to avoid is using the data to support a preconceived belief. An example of that is conducting an analysis to “prove” something is effective. Doing this is an easy way to fall victim to wrangling, twisting, and manipulating your data to make it tell you what you want it to say.
And it might not even be malicious! If you begin an analysis with a certain presumption, your subconscious bias may result in your analysis being biased towards that outcome. Instead, it’s best to have an open mind and be willing to pivot if that’s what the data says. Communicating less-than-optimal results to clients also gives them the assurance that they can trust you when you’re delivering optimal results!
LBB> What are your thoughts about trust in data - to what extent is uncertainty and a lack of trust in data (or data sources) an issue and what are your thoughts on that?
Zackary> First, it’s important to make sure everyone using the data is on the same page regarding how much trust to place in it. If the person compiling the data has hesitancy in it, that needs to be communicated if it’s going to be used by others who are less familiar with the data quality.
Second, the importance of data certainty/trust scales with how important the decisions being made with the data are. Low-stakes decisions will typically have more tolerance for inaccuracy as long as it provides directionally correct information. As the impact of decisions increases, the scrutiny and trust of the underlying data should increase as well.
LBB> With so many different regulatory systems in different markets regarding data and privacy around the world - as well as different cultural views about privacy - what’s the key to creating a joined up data strategy at a global level that’s also adaptable to local nuances?
Zackary> For this question, I’m going to tap into the expertise of Kevin Purcell Ph.D, chief data scientist of WildFig:
"The scope of this issue is incredibly diverse. For instance, medical research in countries like the UK, where medical care is governmentally mediated, has excelled at centralising data for the purposes of large data-integrated studies. However, all such projects are plagued by privacy concerns, regulatory approval, and legal oversight. It creates friction for innovation and stifles entrepreneurial development. The key to a more integrated and globally minded future is a data ecosystem that recognises data ownership and rewards it.
None of the most exciting innovations in technology today would be possible without massive amounts of data, all of which has a provenance. If we recognise that ownership, it flips the discussion from massive investments in governmental oversight and regulation to a grassroots system of support where consumers invest (their data) into technologies that they most support.
As those technologies advance, the flow of capital and reward can reverse directions. It is like retail investing, but instead of capital, consumers invest data with the hopes of downstream dividends. While this framework may seem daunting, there are already outlines for how a system like this works.
For instance, Jaron Lanier, in his book “Who Owns the Future?” outlines how this was originally discussed in the early days of the internet and how a tracking mechanism for such things was deliberately avoided. A future where stakeholders support technology development with their feet (or their data in this case) is possible and could fix much of the dysfunction and litigation that exists today."
LBB> What does a responsible data practice look like?
Zackary> Transparency and security are two of the most important characteristics. Responsible data practices are transparent in the way they collect and utilise data to inform end-users where the data came from. This is also important during the collection phase - if the data is being collected from users, there should be a conveniently accessible option to opt out of the collection process.
Security is also paramount. Data can be highly sensitive, such as when it contains personally identifiable information (PII). There could be severely negative consequences if this data got into the wrong hands which is why data security is such a top-of-mind issue for many companies today.
LBB> In your view, what’s the biggest misconception people have around the use of data in marketing?
Zackary> One misconception we frequently encounter is that people believe using data to inform decision-making is a once-and-done exercise. The results you see in your data today may not be the same results you see next month, next week, or even the next day. Because of this, we educate and consult with our clients on the proper frequency they should be measuring. It’s not a one-size-fits-all approach and is shaped by the nature of your business and the robustness of your data.
LBB> In terms of live issues in the field, what are the debates or developments that we should be paying attention to right now?
Zackary> It would be hard to say anything other than artificial intelligence, particularly generative AI such as ChatGPT and others. In the consultancy space, I don’t believe we need to worry about being replaced by AI (yet!), but rather how we can best take advantage of its capabilities to complement our work. If it hasn’t happened yet, every role within the agency structure will be impacted by AI. It will be interesting to see how the landscape changes over the next couple of years.
