Take a second and picture yourself in the following situation: Imagine that one morning you are in a Walmart store, in the cereal aisle, and you have to choose which cereal is supposedly “best for your health.” After all, you’ve been focusing more and more on your health lately, and you’re determined to lose weight by eating better.
You then go picking item after item, brand after brand, reading ingredient by ingredient, examining nutritional factors one after another, and making your decision based on the data you’ve gathered. Would that be efficient?
Definitely not, as you certainly won’t make it home in time for dinner, not to mention breakfast.
But why is this process so inefficient? Because it simply includes analyzing too much data, which, in addition to taking a lot of time, can harm us in the decision-making processes.
After all, like everything else, data is a double-edged sword: just as making decisions without data is terrible, because it does not make the decision-making process assertive, but rather based on guesswork and intuition (a bit like how it was in the analog world). Too much can also hold us back in the decision process because it confuses us, takes us away from what is important, a bit like the “choice paradox” teaches us (it is commonly said that when we have too many options available, we tend to make the worst decisions). Taking a look back to the cereal example, instead of making a better decision, you will find yourself confused, anxious and stuck in the face of so much data and options… a bit like myself when in front of the beer shelves at Walmart.
And to realize that we already live in a world with a lot of data, where more than 90% of the data generated since the beginning of humanity was generated in the last decade, and where today we are witnessing a number of 97 Zettabytes of data predicted by the end of 2022 according to Statista. A Brazilian data scientist and great friend Ricardo Cappra, created the term “Infoxication” to define this data intoxication that we have, and the difficulty in managing them.
But calm down, this volume of data is nothing compared to what awaits us!
In a Metaverse world where you can metrify everything from the movement of your customer’s hand picking up a product package, to the speed at which an item drops off your highest shelf, or even the intensity of a vibration of a refrigerator that contains expensive wines, you will have exactly the same problem as you will with the cereal example. What problem? Too much data.
Do you realize that in the world of the Metaverse, the great challenge for leaders will no longer be capturing data (this will become a commodity, in a world where everything is measurable, but rather choosing the metrics to be monitored and prioritized?
To give you a better idea of how this works, I’m going to tell you something that happened to me when I was still the Director of Tinder in Latin America.
In my first days running the app, I created a dashboard that showed the main metrics (number of downloads, number of active users, number of swipes, number of matches and so on), in a clear and up-to-date way, updating the numbers each time and day. I was sure it would have helped me make better decisions, but what I realized was that it didn’t necessarily do this: knowing the absolute number of downloads helped me make what kind of innovative and assertive decision? None.
I started looking at percentages, increase and decrease, for example, of these metrics over time: I noticed, for example, a decrease in the number of downloads throughout the week, and I used this as a starting point for my decisions on how much to invest in marketing. Okay, but even if that were better than just looking at the absolute numbers, would it be that much different than speculating on Bitcoin or a stock on the Stock Exchange, just because it’s increasing in value? Obviously this does not guarantee that it will continue to happen, nor does it make clear what are the reasons why this trend is happening.
So, determined to solve this problem and this “blindness”, even though I had given a lot to the disposal, I started to play with correlations between the most obvious metrics, and I came to create a new metric that I called for myself “degree of customer satisfaction”, which fundamentally was the percentage of matches for each like. “What do you mean, Andrea? Where did that come from?”. Well, I started to notice that users who had, for example, a match for every two likes they gave, felt empowered, coveted, more “beautiful”, which would have given this user much more chance to spend more time in the app and be more satisfied. On the other hand, if a user had one match out of ten likes, they would feel frustrated, unwanted, and likely have little success with the app: that was the user I was most at risk of losing. And based on the analysis of this metric by city, by demography, by persona and so on, I was able to assertively define my marketing and investment strategy.
Do you see it now? The competitive advantage for leaders and organizations when it comes to innovating and generating more customer value in the Metaverse world will come not essentially from accessing data, but from choosing which metrics to monitor and prioritize.
The Metaverse will exponentially boost data generation: according to a report by Credit Suisse, the transition to the Metaverse will accelerate data usage by 20 times worldwide by 2032.
At the same time, a new Bright Data research conducted by leading research firm Vanson Bourne, generated insights from 400 IT and technology industry leaders in the US and UK and showed that more than half of respondents (54% ) believes that data will be vital to sustaining Metaverse and that, to support their Metaverse strategy, more than three-quarters of leaders (84%) in the IT, telecom and technology industries say they plan to look for data intelligence solutions to be deployed in virtual worlds over the next two years.
Or Lenchner, CEO of Bright Data, stated that “We now know that organizations rely on public web data to support more strategic decision making. Metaverse will add a new layer to this – revealing millions of additional public data points. As such, it is clear that data solutions will play a key role in connecting organizations with their customers or employees in the Metaverse, helping to uncover hidden insights.”
In an exclusive interview for this article, Or added that: “Looking at the internet today, the biggest database we’ve ever known, it’s easy to estimate that the amount of data and public data on the web will multiply many times over online or e-commerce, a sector that has seen strong growth in the last two years. According to recent estimates, by 2025, we expect this sector to represent almost a quarter of all retail sales on some continents – and that, without a doubt, will count for many layers of data – mostly public Web data”.
Today, when you look at the metrics that are measured within a company, you always find the same ones: sales results – which are compared month to month, year to year -, inventory, customer history, personas, acquisition cost, LifeTimeValue , and so on. In fact when you look at different organizations, they tend to have the same metrics! What can change between them could be the depth of the data, its quality, how up-to-date it is, and so on, but Control Dashboards are almost all the same, let’s face it.
Think about the following analogy: if companies were airplanes, pilots (ie leaders) look at more or less the same panels in the cockpit. This gives you the opportunity to differentiate yourself when it comes to “flying your plane” to a certain extent, as it will be difficult to get such unique and differentiated insights if we are looking at the same “dashboard”. But to the extent that my control panel is totally different, looking at totally different indicators because the “pilot” believes that this is what most anticipates future customer expectations or external trends, you open up the range of possibilities for differentiation and advantages. competitive you can achieve.
Basically, my thesis is that in a world that, in my view, is no longer Big Data but Huge Data, the great competitive advantage will no longer be in access to data, but in the ability to choose the most indicative metrics of future behaviors or customer demands – allowing us to make decisions that are much more predictive than reactive.
This is a bit like what Jorn Lyssegen, founder of Meltwater, a social media monitoring platform, said when in his book Outside Insight he used the metaphor of driving a car looking in the rearview mirror: today, as leaders, we manage our companies as if we were driving a car but instead of looking through the front window (from the visions of the future that correlations between external metrics and real-time data allow us), but looking through the rearview mirror (that is, looking at internal metrics from the past). Inevitably we will go slower, the zig zag, and soon we will crash the car.
All this makes it very clear that we are talking about a question of leadership and professional skills: the Bright Data survey above indicates that 89% of companies understand that even before the introduction of the metaverse happens, there is a need to improve and hire new qualified employees. on data to prepare for reality. “I think it’s clear that companies are fully aware that data will become the key tool in their arsenal when dealing with the metaverse. However, what has also become clear is that this imminent new reality, which is about to change all our business models with consumers owning digital assets and more, the road from concept to reality is complex and long, and most are not ready for it.” Or commented for this article. He added that: “We are in the early stages, and we still don’t fully understand how the metaverse will translate into reality in its future stages, however it is important to start recognizing now the challenges of bringing the metaverse to life, especially when it comes to data. Think about whether your data department is skilled enough to handle this new ocean of infinite data. Do you need to look for data partners and start talking about possible scenarios that are likely to come into play? From my own experience as CEO of a data platform web data, the world of data is exploding and a new data revolution is coming on our doorstep. While I don’t think it’s necessary to stress too much yet, I think starting to explore and better understand this new stage is what we need do it now”.
So like good pilots, the leaders of the Web 3.0 era and the metaverse will be the ones who get involved in detail in identifying the control metrics, and constantly redesigning with their teams the cockpits in which they will make their most predictive and innovative decisions – taking advantage of the enormous granularity of data coming from the Metaverse, which, while being an excellent input for better decisions, can become an obstacle as we do not know how to process and prioritize it in the right way.