How Leading AI Companies Strategically Use Data
By: Alexandre Gonfalonieri
Tags: Business, Artificial Intelligence, Machine Learning, Data Science, Startup

Based on my experience in two leading AI organizations, I realized how data acquisition has become the main element of building an AI strategy. The way we think about acquiring data in such companies is highly advanced since it is a long-term plan with diverse sources of data. Above all, we do not expect to make money out of our data acquisition before several months.

In my opinion, data is perhaps the most underutilized asset of companies. Any application of AI and ML will only be as good as the quality of data collected.

The AI race highly depends on who owns the most relevant data. If we analyze some of the largest and most advanced firms in the field of AI, we can see how data collection has become central in their strategy. Uber, in its IPO document, details how the company is collecting data about cab demand as well as food demand. In reality, the company is profiling the demand for its market like no other transportation service ever has.

The need to forecast demand is not new. Its the scale that is different.

Local cab services might have focused on forecasting peak demand. But Uber looks more broadly than this.

Facebook is mastering facial recognition via its DeepFace Deep Learning application by virtue of owning the world’s largest repository of photos.

Google, the leading “AI-first organization” collects far more data than Facebook. Its vast portfolio of services, from Android to Google Search to Chrome to Google Pay, provides a large amount of data.

Almost all Google products or services are built with data acquisition in mind. The Android mobile operating system, Chrome web browser, applications like Search and Maps, and publisher tools like Google Analytics and AdWords can glean information from web searches, map inquiries, and other online activities without you knowing — or controlling.

In order to justify this heavy data collection process, Google has said in the past it uses much of that data to improve its products. The data collected can lead to more relevant search results, but also be used to feed AI algorithms…

Partnerships

Leading AI organizations do not only rely on a single data source… One of my missions within the AI team was to secure several strategic partnerships with organizations that could provide us with relevant data. The idea is to quickly secure those deals before the competition.

For instance, Google’s DeepMind has a five year deal with ten British National Health Service hospitals to process the medical data of 1.6m patients. Though more data doesn’t always give a linear return.

It is important to understand that data is the main competitive differentiator when it comes to AI.

Algorithms can be copied.

In most cases, I can assure you that it’s not that hard for most organizations to figure out what algorithms competitors are using. Our main focus when building AI strategies is data. Researchers have shown that given access to only an API, a way to remotely use software without having it on your computer, it’s possible to reverse-engineer machine learning algorithms with up to 99% accuracy. Small companies built around a single machine learning API could lose any competitive advantage.

I find out that leading AI organizations realize that data and analytics are unlike any traditional corporate assets. Data is a digital asset that never depletes and can be used simultaneously at near-zero marginal cost across an infinite business and operational use cases.

More Data

Our quest of data goes beyond the data we know about…

Have you heard about dark data?

Dark data: the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.

Indeed, large firms are more and more interested in dark data. The idea is that you probably want to collect as much data as you can afford, for as long as you can, while mitigating risk. Actually, we assume that data might prove valuable but most of the time, we do not know when precisely. With the maturation of AI techniques, that time when that dark data becomes useful may be very soon...

Dark data is valuable for two reasons:

- There’s so much of it

- It could be used to unlock business value.

The more data you have, the more you can learn about every aspect of your operations. The larger the dataset, the more accurate the analysis.

Leveraging dark data could become a new competitive advantage in the AI race.

We spend a lot of time trying to understand the true economic value of our organization’s data since it helps us prioritize technology and business investments.

Feedback loop & Unique dataset

In my opinion, companies don’t need to be a large tech firm to succeed in AI. Obviously, you need enough data upfront to start a solid project. This initial dataset will attract customers who, in turn, will create more data for the product. After a period of time, you might have enough data yourself to have a defensible business if you choose to focus on a very specific business issue.

And this actually makes their business, in my opinion, defensible because even the global giant tech companies, as far as I know, do not have a particular data asset for every possible situation/business issue, which makes their business at least challenging for the very large tech companies to enter.

Strategic data acquisition.

During my experiences, our main goal was to build a proprietary data set that our competition was unable to replicate. This goes beyond individual data collection processes and involves creating a virtuous data cycle by embedding data collection into the creation and industrialization of our products.

Propriety Data: Internally generated data that contain technical or other types of information controlled by a firm to safeguard its competitive edge. Proprietary data may be protected under copyright, patent, or trade secret laws.

For instance, we created a digital platform that encourages innovation and data sharing from internal and external partners.

Another crucial reason to start with gathering data is to gain first mover advantage in your industry. Large tech firms have dominated their industries because they were the first to begin building data sets. Their data sets have become so large, and their data collection and analysis so sophisticated that they are able to grow their competitive advantage.

With each iteration, they are putting more distance between themselves and the competition.

Specific dataset

All data sets are not created equal. Deep data sets focused on solving specific problems are better than large, broad data sets. Dynamic, constantly refreshing data sets are vastly superior to static data sets, typically regardless of their size.

Basically, the harder they are to access or replicate, the better it is for your company. Proprietary data will help you build a strong defensible competitive advantage in the market.

Defensible Position

I have seen too many times data collection considered as a series of one-off exercises used to inform company strategy. Data collection must be built into the core product itself. In other words, every time a user engages with your product, you must collect data from the interaction that will make future usage of the product even better.

The data captured must be designed precisely to serve the needs of your models, which are designed to serve the needs of the product functionality, which is designed to meet the needs of the customer.

This requires significant architecture (technical, legal and data security) to work, but is key to maximizing the value of the system.

Data value

Decision-makers tend to forget a few things about data. First of all, the size of the data set is only one part of the equation. The value of your data set is defined by context. Indeed, some applications require models to be trained to a high degree of accuracy before they can provide any value to a customer, while others need little or no data at all. In my latest AI project, the company shut down the project because we only reach 95% accuracy… The remaining 5% would have represented too many potential issues with final customers.

Some data sets are truly proprietary, others can be easily copied. When it comes to data in itself, some data decay in value over time…

Most AI applications don’t require 100 percent accuracy to create value. In general, the application determines the value of the data.

However, solutions, where AI is essential to the product, would likely need to equal the accuracy of the status quo (human-based) solutions. In this case, the goal is to match the performance of human experts, and an immense volume of data might be needed before even reaching the production stage.

A growing number of business issues can be solved with near 100 percent accuracy with a very small training set, because the problem being modeled is relatively simple, with few dimensions to track and few variations in outcome.

However, the truth is not every problem can be solved with near 100 percent accuracy. The amount of work from 90 to 100% accuracy is often underestimated. The volume and quality of data you have can’t solve some problems given the current state of the art and perhaps your specific business issue. It might sound easy to just say “let’s add more data” but actually it will you cost you a lot and require a tremendous effort from your team...

We call the amount of training data needed to reach viable levels of accuracy the performance threshold.

From a strategic point of view, I noticed that domains requiring fewer data to reach the performance threshold are not very defensible.

Basically, new entrants can easily gather enough data and create a similar solution. How do you protect yourself from an AI perspective? Obviously, data is not the only element to take into consideration…

With large tech firms making it easier to access AI toolkit, data sets are one of the most important ways to differentiate. A truly defensible data strategy cannot be the result of just gathering the largest volume of data. I believe that the best datasets are tied to a particular problem domain.

It’s data, not algorithms, that will decide who wins in the AI race. An AI company built around publicly accessible data isn’t defensible.

Regulation

I noticed that brands are prioritizing greater ownership and control over their data as they ramp up their investments in customer experience and personalized services, according to new research.

Global society is entering a new era of data ownership, and general data protection regulation (GDPR), Europe’s stringent new privacy law, is just the beginning.

For these reasons, as well as competitive concerns and long-standing habits, many companies choose to keep all of their industrial data proprietaries. They retain control of data and can govern how that data is used. It’s more secure and typically makes compliance easier.

AI winners will be those organizations that are the most effective at leveraging data to build strong defensible business models.