AI Trends: Should Data Be Considered a Product?


Historically, businesses have had two approaches to leveraging data.  

Some companies took a local approach, where individual people and teams leveraged their own databases to obtain information based on their needs. Other companies, usually larger ones, have followed a more “big-bang” method, in which the company formed a specialized team to aggregate, prepare and share data. 

What are the problems posed by these approaches? Neither are sustainable. Neither can handle the amount of data coming in from customers and internal operations. Neither allows customers to profit from their own data.

But there is now a third option, according to McKinsey: making data an internal product.

Using data as an internal product provides a competitive advantage to companies like Netflix, Intuit, PayPal and many others.  

Should you follow suit? A diverse group of industry experts answered this question during a recent roundtable discussion . 

Panelists Sejal Amin, Chief Technology Officer at Shutterstock, Jana Eggers, CEO at Nara Logics, Razat Gaurav, CEO at Planview, and Dr. Rich Sonnenblick, Chief Data Scientist at Planview, were joined by moderator Ray Wang, Principal Analyst and founder of Constellation Research, Inc.

This blog post presents their views on using data as a product, including: 

  • The shift in how businesses view customer data 
  • How Data Productization Benefits Businesses and Customers 
  • The importance of ethical data sourcing and gray areas to watch out for  

Listen to the full discussion: AI in the Enterprise: Opportunities, Challenges and the Future 

A change in culture – and mindset – regarding customer data 

Moving company data to cloud computing had many benefits for customers: easier access to data, better security and greater organization. This shift has also led to a massive shift in how businesses perceive, use and manage data. 

At the start of the cloud computing craze, companies like Planview began storing their customers’ data. Many cloud computing companies have approached customer data like a storage unit: you keep it, but you don’t touch it.  

When companies didn’t care about data, developers, product teams, and customer support teams got used to never interacting with data without permission. All data access requires explicit authorization for specific actions.

But the culture has changed. The relationship between businesses and customers, as well as the relationship between teams within a business, has changed, primarily because there is more data available in real time.  

Rather than being a storage unit service, cloud computing companies today operate more like banks – constantly using data to make better decisions for the business and its customers. 

Yes, businesses store data, but they constantly use it, learn from it, and put it to use. Treating data as a product means that the data has its own processes, is customized for different uses, has support (including documentation and interfaces) for different downstream needs, and is are governed by clear rules – again, like in a bank.

Using data as a product benefits both businesses and customers 

There is value in leveraging data to improve products, design features, provide AI/ML capabilities to train models in real-time, and make changes for downstream customer use. Data is essential for analysis and optimization. 

Despite these many benefits, using data as a product and viewing internal teams as customers are new mindsets for many companies. The larger the company, the greater the effort required to create these data products in a systematic, repeatable, and efficient manner.  

By looking at data more broadly, businesses can see opportunities to use data for purposes other than internally. 

If a company has a goldmine of customer data, this may be just the opportunity to increase the value of its existing assets.  

Customers often do their own data science and want to access their own data efficiently. Data as a product is another way for businesses to serve their customers, allowing them to access their own data more efficiently and improve their own processes. 

This is a win-win situation based on information that already exists and is just waiting to be used to its full potential. 

To achieve additional gains on the data front, companies are considering how to scale their offerings in a more sustainable way, requiring less human intervention and more automation and visibility for customers looking for different types of content. 

It’s clear that defining data as a product has internal benefits for a business. However, the same interpretation can also highlight a wide range of values ​​for customers. 

Ethical data sourcing could not be more important 

Shutterstock is a great example of using data as a product. Sejal shared Shutterstock’s experience with ethical data, as well as industry concerns around treating data as a commodity.  

Shutterstock has made several major deals with Amazon, Google, Meta and other major companies. Shutterstock sold them its library or subsets of its image library. In some cases, companies came back and requested a different type of asset or more of the same type of asset.  

As businesses consume more and more data, Shutterstock was uniquely prepared with its ethically sourced assets and corresponding metadata, of which it already had a massive amount, both automatically and manually, through intermediary of its contributors.  

This is exactly what hypermarkets need to develop their offers. And all this at a time when the conversation around legal and ethical data was intensifying, such that Shutterstock’s ethical data was a competitive advantage. 

However, there are gray areas. When selling data as a product, Sejal encourages companies to ask tough questions, such as:  

  • What do you give?  
  • How many do you give? 
  • Is this the right thing to do right now?  
  • What is the ultimate goal of what you are trying to do, and how are you trying to do it?  
  • What type of content are you looking for? What type of metadata are you looking for? 

Finding these answers will help businesses make ethical data sourcing their North Star. 


Should we therefore consider data as a product? This question is timely and the rise of AI capabilities is accelerating the need to answer it.  

This approach offers significant opportunities not only for your business but also for your customers. Ethical data sourcing fosters a relationship with data that customers and stakeholders can trust and value. 

Learn about other implications of AI in today’s enterprise – including the impact on knowledge workers, responsible AI and what organizations can do today – in AI in the Enterprise: Opportunities, challenges and the future . 


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