There's value in your data. It contains insights which are essential to so many roles: developers, entrepreneurs, research firms, scientists, academics, analysts, investors, marketers, designers, consultants, product developers...the list goes on. As challenging economic conditions force consumers and companies alike to become more opportunistic and more innovative, many are beginning to realise their data's value. They're beginning to monetize data to earn money passively and boost revenue.
If you're an individual or company interested in data monetization, then read on, we'll talk you through all you need to know to start a safe and sustainable data business. We'll begin by looking at the differences between selling data as a consumer vs. as a company, then the kind of data on sale and in demand currently, followed by a step-by-step breakdown of how to sell data, and lastly an overview of the different commerce platforms available to today's data provider.
1.1. DaaS companies traditionally
1.2. The future of DaaS companies
1.3. What to keep in mind when monetizing your company's own data
2.1. What to keep in mind when you sell your data as a consumer
2.2. Putting consent and user trust first
2.3. The available data for consumers to sell
3.1. AI & ML Training Data
3.2. B2B Data
3.3. Commerce Data
4.1. The importance of KYC when you sell data
5.1. Data collection and cleaning
5.2. Legal preparation
5.3. Join a data marketplace
5.4. Create data products
5.5. Develop marketing strategies
5.6. Deliver data securely
5.7. Manage sales, grow your data business
6.1. Data marketplaces
6.2. Data commerce platforms
7.1. What does it mean to monetize data?
7.2. How can I ensure the data I sell is compliant with privacy laws?
7.3. How do I determine the value of my data?
7.4. Who might be interested in buying my data?
Let's begin by looking at the most common scenario in data sales: B2B data monetization.
B2B data monetization is the process when one company or organization sells data to another. The seller can fall into lots of different categories. They could be a professional data provider company, whose primary product and line of business is to collect and sell data. In the industry, these companies are often known as data-as-a-service, or 'DaaS', companies.
Traditionally, DaaS companies are data providers which already have experience selling data because that's their core business. They're also known as 'data vendors' or simply 'data sellers', and they are all entities who earn money by selling data. Then within this group, there are different kinds of DaaS companies offering a range of intelligence and services. Some DaaS companies also double-up as SaaS businesses. For example, a customer data provider may also offer marketing software, like audience builders, or even web scraping services to collect data directly from the internet. Others solely sell raw data for analytics purposes. These kinds of DaaS companies include stock market data providers or alternative data providers which really do just sell raw datasets, usually to financial analysts and hedge funds. It used to be that DaaS companies only sold data. However, that is changing.
Data monetization will become mainstream. And this means that more companies, belonging to every industry, each delivering different products and services, will have the chance to monetize their internal data assets. In other words, any and every company can become a DaaS company, because any and every company will have information which others find valuable. It might be historical transaction data showing product sales over time. It might be firmographic data showing the technology stack and human capital a company has in its arsenal. It might be records of wholesalers and import/export receipts which are simply sat in your company's data silo. There is huge demand for this internal intelligence, catalyzed by the need for datasets to train AI models and to improve decision-making. Monetizing this data will become easier as more solutions emerge to facilitate data monetization at scale.
The most important step in building a DaaS company is ensuring that you monetize your company's data in compliance with data privacy regulations. You must ensure that the privacy of company employees and customers is upheld and obtain permission before sharing data such as their contact information. Likewise, you must adhere to intellectual property laws to ensure you're not sharing copyrighted information without the creator's consent.
Also important to consider when you sell your data is the demand that's available for such information. Deal sizes for external data can vary a lot, and data valuation is a nascent methodology. For instance, there's so much demand for AI & ML training data currently, which can make it a more valuable data type than, say, job posting data, for which there is steady, mid-value ticket demand. But this isn't a hard-and-fast rule: really, the value of your data depends on many factors aside from current demand, including its quality and versatility.
We'll look at the most popular and lucrative data categories and the ICP for DaaS companies further into this guide. But not before we look at a different kind of data seller: the consumer themselves, who is able to earn money by monetizing their own data.
Consumer data monetization is when individuals consent to sharing their data with data companies and other internet services and earn extra money for doing so. It's a way for individual users to retain control over their own information. Moreover, selling user data gives consumers a fair share of the revenue which would otherwise go exclusively to the website collecting and selling it.
'If you're not paying for the product, you are the product': it's this adage and many stories of high-profile data leaks from social media platforms which has prompted many consumers to, rightfully, demand complete control over their own data. Failing to remunerate consumers for information collected about them is unethical and illegal. A data product must always uphold the privacy and wishes of the person whose personally identifiable information comprises it.
That's why every website must allow its visitors to opt out of data collection, should they want to. And to put control of personal user data even more securely into the hands of the user, numerous companies are now offering solutions so that consumers can sell data pertaining to their internet activity, purchases, and mobility. Companies like Solipay and Reklaim pay consumers for their data directly whenever this data is collected online. These software solutions make individual data monetization easy. You consent that the software provider can collect data as you browse the internet, and is transferred to your securely and you earn extra cash effortlessly.
As an individual, the level of information you'd like to disclose as you sell your data is totally up to you. You may consent to an e-commerce platform collecting web activity data so they can improve the customer experience. But you could demand that any PII is removed before it's sold, so that the data only contains anonymized web click and search data. It also will also vary depending on the data protection laws effective in your jurisdiction. For example, the General Data Protection Act (GDPR) which is law in countries belonging to the European Union restricts how much data companies can collect and store about users, even if the users are paying customers that consent to data sharing. All websites must allow users to opt out of data collection. This ensures that data monetization remains something which empowers the consumer, rather than exploits them.
Consumer data is one of most common data types for sale. It's extremely versatile and valuable for a range of use cases. For example, a product developer might buy consumer data to better understand the consumer's behavior and pain point so they can enhance the customer experience and create optimized services for them. By the same token, a marketer can also benefit from consulting consumer data to create campaigns which resonate with consumer sentiment. Purchasing consumer data from the consumer themselves would be a great investment in this case, because the data provides a reliable insight into the psychology of potential buyers. For this reason, consumer data comes with a high ROI, and as a result is one of the most frequently bought categories of external data.
There are a collection of other data categories which companies are consistently buying. Let's have a look at other data categories which are in constant demand.
Data valuable to some may have no value for others. It depends entirely on your use case. Nonetheless, there are a select number of data categories which are frequently purchases by companies from a range of industries for a plethora of different use cases. We'll have a look at three of these.
Artificial intelligence and machine learning (AI & ML) algorithms require vast amounts of unique data for training purposes. This has caused a surge in demand for text, image, video, and audio datasets. Companies have begun monetizing their first-party data by signing licensing deals with AI companies. This data sharing is fueling the AI revolution and ensures that the next waves of AI models are based on reliable data.
Demand for B2B data is consistent and high-volume. There are thousands of B2B data providers offering email lists, identity datasets, and firmographic databases. There's so much demand for this kind of data because it makes marketing and sales processes so much more efficient. Imagine you're a B2B marketer: outbound prospecting becomes significantly smarter and more scalable if you've bought B2B leads from a provider who understands your target audience and has created a segment of contacts accordingly. Also, B2B data products are relatively low in cost, meaning they're appealing to customers with varying budget sizes.
Companies that sell commerce data include some of the world's biggest credit card companies. That's testament to how worthwhile it can be to monetize commerce data. As a category, commerce data is broad: transaction data, e-receipt data, point-of-sale data, product data, ecommerce data and online review data all count as commerce data. Its brevity as a data category explains its desirability. Commerce data can be used as effectively for keeping track of product inventory as it can for designing an ecommerce website.
Data is an extremely versatile asset. As such, a data seller enjoys a huge total addressable market of potential customers. Here are just a few examples of who you could sell different types to:
Allowing the data you sell to end up in the wrong hands can be disastrous for companies, especially data startups that are often unable to pay the consequent legal penalties. That's why stringent KYC is paramount before agreeing to sell data to an organization or individual. Thorough KYC involves collecting the following information on the prospective buyer early on in their procurement process:
Data Buyer KYC Checklist
Once you've done your market research and KYC, you can now stop operating your data-as-a-service business. There are 7 steps to this process, which we'll walk you through.
Like starting any business, careful preparation, good planning and a clear GTM strategy is vital to sell data successfully. There are 7 main steps to getting your data company off the ground which cover these phases. Following them will ensure you lay the foundations for a trusted, compliant and valuable DaaS business so you can then start making sales to happy customers efficiently.
Unsurprisingly, the first step to monetizing data is collecting it. This also encompasses making an inventory of all your available internal data assets. You will either collect the data you intend to sell from within your business, or from other party sources, such as data siloes or re-sellers.
Whether you collect the data from within your company or from other organizations, a data cleaning process will almost definitely be necessary before you're ready to sell it. This involves screening the data for anomalies, removing outdated fields, parsing the data to a language which is accessible for the potential buyer.
Collecting, cleaning and organizing your data can be a lengthy process, but it's the foundation for running a successful data business. It'll ensure that you have your data products in order, check that they're high quality, and mitigate any complaints from customers by ensuring data accuracy from the outset.
An essential step before you sell data is to ensure that you have the necessary licenses and permissions to do so. If you're going to sell user data to other organizations, for example, there are strict legal requirements you must follow. As part of your due diligence, it's often worth working with a lawyer to ensure you're complying with the applicable privacy laws. This might mean spending more as you're setting up your data business, but it'll reduce costs in the long-run because you will prevent any hefty fines or legal disputes incurred if you sell data without a license or fail to comply with relevant regulations including GDPR, CCPA, and HIPAA.
Secondly, legal preparation requires you to finalize the relevant contracts with your data suppliers, whether that's the users of your website, the departments within your organization, or a third-party you purchase data re-selling rights from. You need to ensure you have the necessary permissions to sell the data collected from these sources. It's essential you check the fine print of the contracts you have with your sources, as this could affect who you're allowed to sell your data to and the use cases for which the data can be utilized.
For example, there could be a clause in a contract you have with consumers which states you only have permission to collect and sell users data to companies based in the EU. At best, this would dramatically affect your number of potential buyers. At worst, should you fail to uphold this agreement due a legal oversight, your data business would be closed and investigated. So the importance of scrutinizing the contracts you have with sources before you start distributing data to customers cannot be understated.
We could call this step your 'go to market' strategy - or your go to marketplace strategy. Yes, this is when you join a data marketplace to be visible to buyers who are buying data.
In a nutshell, data marketplaces are amazing tools which enable you to reach new customer groups globally. There are many kinds of data marketplace, from open, to B2B, to category-specific marketplaces, such as those which sell exclusively financial market data. Data marketplaces attract thousands of users per week. As a data buyer, data marketplaces offer an easy, secure way to compare providers and their offering. Monda is integrated with some of the world's largest data marketplaces, including those from Databricks, Google Cloud, SAP Datasphere, and Datarade.
Data marketplaces are giving companies the capability to list data products on their platforms. In line with the Digital Services Act, these data product listings and attached data samples comply must with privacy regulations including GDPR, CCPA, and HIPAA, as well as the marketplace's own ethical guidelines.
In general, this step is concerned with generating awareness, interest, and leads in and for your data company. As with any business, there are a wealth of marketing channels available to a young data provider. The right channel for you depends on many factors, such as your budget, the category of data you're selling, and your ICP.
One of the most valuable marketing strategies is SEO. Most companies looking to buy data will start their online search where we all tend to: on search engines like Google. For marketing purposes, it's worth investing in your web pages so that they appear on the top of search results and attract the most visibility and traffic. Monda enables you to build your own search engine-optimized Data Storefront with no code required to attract search traffic and convert them into organic data leads.
You need to deliver data via a method which ensures that your customers can access it which doesn't compromise security. There are several methods to transfer data securely, each catering to specific needs and levels of protection.
One common approach is encryption, where the data is encoded in a way that only authorized parties possessing the decryption key can access it. Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols ensure encryption during data transmission over the internet. Virtual Private Networks (VPNs) create encrypted tunnels for data to pass through, safeguarding it from potential eavesdroppers. For physical transfers, methods like Secure Shell (SSH) establish secure connections for remote file access. Multi-factor authentication adds an extra layer of security by requiring multiple forms of verification. These methods collectively provide a range of solutions to ensure data remains confidential and protected during transfer.
As you start to earn money as your data business grows, it's important to manage your sales and transactions to stay on top of demand. You need to ensure you're delivering the data to the buyer companies punctually, and that you're issuing invoices and receiving payments on time.
Diligent bookkeeping is particularly important if you're striking large-ticket data deals or recurring data subscriptions. Staying on top of one-time purchases is relatively easy to control, particularly if the data marketplace you're using collects cash automatically once they've paid via the payment processor. However, other licensing deals might involve you sending data to customers on a pre-agreed cadence, like monthly or bi-annually. To manage these orders and maintain good business relations which all of your disparate customers, managing your sales requires both the right tools and the right people.
Regarding tools, most established data providers work with CRM software like HubSpot, Salesforce or Zoho to manage sales and their customers' data usage. CRMs enable you to manage deals and deliver timely service to existing data customers.
Regarding people, as your business grows, you will most likely need to hire people to sell your data for you. One of the most exciting trends that the data industry is experiencing is the amount of one-person shows, with a lone founder monetizing data, growing into large data provider companies in a remarkably short amount of time. Consider the likes of Lusha, a B2B data provider which grew from 2 to 300 employees in 7 years. This success case shows just how much potential there is in building a DaaS business.
And to help you start your data business, there are a whole host of SaaS solutions and partner platforms emerging every day. Each is designed to help you at one of the 7 steps of data monetization, and we're seeing an increasing number of E2E platforms which help you with them all.
As we've discussed, data marketplaces are an amazing way of connecting data supply with data demand. Listing your data on the top data marketplaces is a sure-fire way to generate interest in your data offering.
However, the time to integrate with some data marketplace can prove cumbersome. Many require a complex data sync or API integration, which can take months and demand a huge part of your engineering resources. As a solution to this, there exists a different kind of platform on which to sell data. These are a new kind of software known as the data monetization platform.
Data commerce platforms like Monda offer a similar solution to DaaS companies that Shopify offers to online retailers. Namely, to appear on multiple sales channels without the overhead of having to manage each of these channels individually. With one platform, you can create data products, manage deals, and process transactions from each channel centrally. For example, Monda enables providers to publish products on Datarade Marketplace, Google Cloud Analytics Hub, SAP Datasphere, and Databricks Marketplace with one account. Providers can publish their listings in each of these channels with a click, and all leads and business generated from these different channels lands in the provider's Data Sales CRM.
To-recap all we've looked at in our guide on how to sell data, here are the most common questions asked by people looking to start their data business.
Monetizing data refers to the process of providing access to specific data sets to interested parties in exchange for compensation. This could include demographic data, consumer behavior data, or any other type of information that could be valuable for research, marketing, or other purposes.
Compliance with privacy laws can be complex and may vary by jurisdiction. It's important to consult with a legal expert who specializes in data privacy. Generally, you should ensure you have consent from the individuals whose data you're selling, and that you're providing adequate security to protect the data.
The value of data can depend on several factors, including its uniqueness, its relevance to the buyer, and the size of the dataset. It can be helpful to work with a data broker or consultant to determine a fair price.
Many different types of organizations might be interested in buying data, including marketing firms, research institutions, and businesses looking to better understand their customers or market trends.
To sum up, demand for external insight is soaring. There’s never been a better time to start your data business.
Monetizing data can be a lucrative business venture for those with access to valuable datasets, and the process is, with the right preparation, simple. It's important to keep in mind the legal and ethical considerations surrounding the collection and sale of data, including obtaining consent and ensuring compliance with privacy laws. There are various data categories that are commonly sold, including AI & ML training data, B2B data, and commerce data. The potential customers for data are numerous and diverse, ranging from marketing firms to research institutions. By following the steps outlined in our guide, you can start building a high-potential DaaS business.
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