Introduction

Advancements in Deep Learning algorithms have emerged at a much more frequent pace in recent years. This creates a situation in which even novel improvements become obsolete very quickly. Not only that, these new developments are usually published online, accessible for anyone. In such situations, how can a company build a moat around an AI-based product? One of the simplest answers is data. 

While the architecture of the artificial neural network is easily replicable, a proprietary dataset generates insights that can give a company its competitive advantage. Such an advantage created due to data is called the data moat.

Overview of Data Moat: 

Whether it is a startup or a multi-million dollar company, organisations need to know about its data, and more importantly, how to use it. With all of the hype around big data, it’s important to keep in mind that not all data is created equal.

A data moat requires two essential things, and missing even one of them makes it harder to create a data moat. It needs 

1. an accurate collection of relevant data, and
2. the culture to effectively use it.

A data moat is a competitive advantage of an organisation against other businesses based on its proprietary dataset. If the entrepreneur is looking to build a sustainable and profitable business, one needs to have strong defensive moats around the company. Some of the greatest and most widely known companies are defended by extremely powerful data moats. For instance, Facebook, Google, and Microsoft have created different data moats that give them a competitive advantage in their business.

Types of Data Moats

On the basis of data, an organisation or a startup can create different types of moats. The prominent ones are listed below:

1. Data as an operational advantage: A key aspect of using data to build an operational advantage involves making data available and understandable to those who are making daily decisions. This includes both technical and non-technical users, i.e. data analysts, engineers, marketers, product managers, etc. For instance, a mobility service provider such as Uber, can dynamically update its pricing based on demand and supply economics.
2. Data as a strategic advantage: Every quarter, the company will make a few highly critical strategic decisions. For a product company, this could be like answering the question, “Which user segments should I be focusing on?” These decisions can be made using varying levels of data maturity, but more granular the data behind these decisions are, it’s more likely that the new strategy will help leapfrog the competition. For instance, a company can use data to determine its next branch location, a strategic step for the business.
3. Data driving a core product advantage: The third kind of advantage is when companies leverage data to drive a core product advantage. The best example of this is Netflix. With years of user data, Netflix knows everything about each of our tastes, likes, and dislikes. Part of this comes from its not-so-basic user data. All of this user data from millions of users means that Netflix can personalise its recommendations to keep each person constantly hooked to its platform. This massive data moat makes it hard for a new OTT business to overthrow Netflix. With data, Netflix has created a core product advantage that is quite hard to replicate by its competitors.
4. Data monetization: The last kind of business moat is turning a company’s data into a business opportunity by itself. While the age-old examples include ad networks like Facebook and Google, which leverage user data to deliver highly targeted ads, there are many businesses that use data to generate direct monetary benefits. For instance, Amazon sends recommendations via our previous purchasing history, food-based businesses send customised coupons based on previous purchases, etc. 

Creation of a Data Moat

To create a data moat an organisation or a startup has to collect the data from every source possible. However, even though data collection is being done, ineffective approaches might render the data completely useless. In general, these four major aspects should be taken into consideration while collecting data:

1. Data Objectives: A tentative idea about the objective of data collection can be quite helpful in taking data collection related decisions. If the analyst doesn’t know why organisations are collecting data and what they are going to do with it, the organisation will struggle to determine what data needs to be gathered and measured.
2. Disconnected Data: Data silos can be a real problem. Are applications sharing data with each other, or are records changing in one data bank without propagating changes to related records in other data sources? This can create mismatched, outdated information – a data administrator’s nightmare.
3. Formatted Data: How is an organisation capturing, storing, retrieving, and displaying the data? Companies can collect the same pieces of data in varying formats, leading to mismatches or faulty data.
4. Duplication: Does the organisation maintain multiple systems that contain data? This creates different sources for the same information, which can quickly lead to incorrect information in one location or another. Even if the organisation has fewer systems, human error can cause duplication, which creates noise in data.

Benefits of a Strong Data Moat

Once an organisation has created a strong moat through data collection, it can help the business in many ways. A strong data moat enables businesses to identify new trends, new opportunities, new features, customer behaviours, ahead of anyone else. Such insights enable companies to make many crucial decisions, i.e. create new features, decide timing for product releases, price products, decide the content strategy, etc. Many companies have leveraged data in the past and gained significant competitive advantage. For instance, Netflix released the TV Series “The House of Cards” based on its data driven insight, which became an instant hit. Google consistently follows data driven decision making to improve its business processes. Southwest airlines has been using data driven strategies to increase its loyal consumer base. Such approaches have been possible only because of strong data moat companies have created for themselves.   

Conclusion

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 prioritises the effective use of data, creating a data moat is difficult. But, companies who have created such a culture and coupled it with an overwhelming preponderance of collected data, have been able to use it as a significant 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 get ahead of their competition. Creating data moats is hard, costly in nature, and needs a lot of effort, but its creation will drive a lot of indescribable value to the business.