In an era in which data has become the most valuable commodity, companies that have become the most proficient in data collection, data storage and data analytics are the ones that are responsible for the most value creation in the current market.
From the company’s very inception, the original Google gained a first mover advantage in the data economy by developing proprietary data sets, creating new methods of organizing data and developing sophisticated algorithms to analyze data.
This first mover data advantage has never really been relinquished.
BY:
Salvatore Nicci
Technology Analyst / Reporter
PROJECT COUNSEL MEDIA
3 March 2022 (Barcelona, Spain) – Over the past 2+ years we have noted one major trend that government regulators have unknowingly advanced, either through the European Union’s General Data Protection Regulation, California’s Consumer Privacy Act plus a myriad of other legislation: advertising and data is retreating/has retreated inside silos owned and controlled by Amazon, Apple, Facebook, Google, TikTok, etc. where nothing is passed around or shared. Taken to its logical conclusion, eventually most of these privacy regulations will simply not apply.
Owning a monopoly on data or information is not a new concept. Owning secret knowledge is as old as humanity. Think about humans learning the secret of fire, gunpowder, medicine, and nuclear bombs. The concept seems more modern, because of computer technology. This technology only means there is more data to control in a monopoly. Seeking Alpha discusses data monopolies in the article “Google Stock Is A Buy On Future Growth, Alphabet Data Moat” which is behind a pay wall but which we have put up on our Slideshare account. It is a long, very detailed piece with some good charts and graphics and well worth your read. Alphabet (a.k.a. Google) started as a search engine that sold advertising space, but now the company is a leader in augmented reality, blockchain, and AI. It continues to grow and its endeavors yield more data moats to control. The article notes:
“The reason why Alphabet has built several powerful data moats is that the company has become very proficient in identifying high value data, collecting that data, analyzing that data for insights and producing high value products based upon the insights gleaned from the data. The very first high value data that the founders of the company, Sergey Brin and Larry Page discovered was search data. Google established a first mover advantage in search data that grew into such a powerful data moat that even Microsoft is unable to catch up to Google in search. Google still maintains 92.47 percent market share in search as of June 2021. Over time, Alphabet has discovered and built up other high value data sets with some of the most well-known products built upon those data sets being Maps, YouTube, Google Assistant and self-driving cars (Waymo).”
Alphabet continues to grow its data moat through constant research and development, especially in the realms of AI. The company will continue to expand and it could grow a data moat monopoly. If Alphabet does get a data moat the government is supposed to step in and break it up. But we sincerely doubt this will happen the world’s current geopolitical dilemmas and how corporations have politicians in their back pockets.
A data moat requires two essential things, and missing either one of them means your moat is not really a moat. A data moat needs:
1) an accurate collection of relevant data
2) the culture to effectively use it
Having years of experience working with and consulting at enterprise e-commerce companies, I am consistently surprised by how often the latter is missing and how this turns a would-be moat into a puddle.
There is a point made by detractors of data moats that data alone rarely builds great companies and that the world is littered with examples of small start-ups with far less data bringing down behemoths with access to many times more data. This is absolutely true, but it stands in stark contrast to companies like Amazon and Google and Yelp.
• Google won its position through creating and consistently innovating on the best website search algorithm in the world built on its mountains of data, and using the algorithms it developed on that data to destroy its competition through its more relevant search results.
• Amazon was the first retailer to effectively use its data to create completely new product discovery experiences like recommendations and to allow for effective and relevant searches over its massive product catalog.
• Yelp may be the best example of a data moat. It captured more restaurant review data early on than anyone else and no one has been able to effectively compete with this data advantage for most of the life of the company.
Of course, all three originally had far less data than their competitors, but they were able to more effectively use the data they did have and eventually turned that effective use of data, coupled with their eventual collection of massive quantities of data, into an effective moat.
The reasons Google, Amazon, and Yelp were able to create effective data moats, but brick and mortar retailers who originally had far more data were not, is something we continue to see first hand. A company like Toys R’ Us had massive amounts of data on its customers. It could have used that data to personalize those customers’ experiences in ways no one else could. It could have used that data to learn about where their customers were having bad experiences and fix them. It could have used that data to give its customers the best product discovery experiences available anywhere. It didn’t. And it wasn’t for a lack of data.
Instead, the problems for most older enterprises tend to be rooted in cultures that pay lip service to data’s effectiveness, but are not able to turn that into prioritizing the effective use of data. We have worked with multiple examples of companies who ran A/B tests without outlier detection, or collected data, built products on that data, but lacked controls to verify the data was accurate (it wasn’t). These are the types of problems that take what could be a data moat and drain it. They are also the types of problems that tend to be endemic to a culture and are incredibly difficult to change from the inside. The engineers at these companies who try to change them tend to eventually get fed up and leave. It is a hard thing to change a culture, but without the right culture, data is useless.
Once Amazon showed the power of data and technology, its competitors raced to catch up by forming divisions aimed at operationalizing their own data, but aimed to do this internally. At the time, they still had more capital and more data than Amazon, but arguably Walmart, with its 43% year-over-year ecommerce sales growth rate is the only one really succeeding, through aggressive acquisitions and third party partnerships that forced a more data-centric mindset into the company from the outside and made it rival Amazon in technological capabilities.
In the end, data moats do exist and some of the most successful companies in the world effectively use them, but they are not predicated on data alone. Without a data-centric culture that ruthlessly prioritizes the effective use of data, no moat can exist. But, companies who have created such a mentality and coupled it with an overwhelming preponderance of collected data, have been able to use it as a massive competitive advantage. This is both a warning call to enterprises with massive amounts of data, but the wrong culture to use it, and a rallying cry to startups who can create a data-centric culture, gather enough data to make it useful, and beat their larger competitors to creating a moat.
And creating that data moat is so important for utilising artificial intelligence and machine learning. It can take a minimum of 10 million data points to create a useful prediction from ML.
Data moats are real, but data-centric culture is as important as the data itself. Big Tech knows exactly how to play this game – regulators be damned.
