As the world's most valuable commodity, it's no surprise that you can make money with data. Companies are increasingly realizing this and are putting data monetization at the center of their growth strategies. However, what's more surprising is the amount of various definitions for data monetization, with each monetizing model unique to each other.
What exactly do we mean when we talk about monetizing data? What kind of data is for sale? How much is it worth? How easy is it to sell data?
Our Ultimate Guide to Data Monetization 2024 aims to answer all these questions and more. We'll provide real-world examples of data monetization, walk you through data monetization software available today, and explain best practices when it comes to pricing, delivering and profiting from data. Of course, we'll also evaluate the benefits and challenges of data monetization. This way, you can decide whether, amid all the data monetization hype, it's an avenue worth pursuing for your business.
Read on to learn:
Data monetization is the process of extracting commercial, monetary value from raw data. In the simplest possible terms, the process is 'data out, money in'. However, the steps between input and output here can vary. In other words, data monetization can take on different forms.
What's remained consistent is the growing popularity of data monetization. As the amount of data generated and captured globally increases by the second, so too has data monetization increased in adoption. Indeed, selling data has gone from a just-about-viable business model to a no-brainer for many companies and individuals. Partly, this is down to the generative AI revolution which has given rise to LLMs and other machine learning models which require massive amounts of training data.
Consequently, we're at a stage where data monetization is becoming mainstream. It affects every company in tech, many companies in more traditional verticals like retail and finance, and now even the individual consumer.
The simplest way to monetize data is if you're already in possession of data which other parties will pay for. Organizations' internal receipts, contact details, health records, legal documents etc. all count as proprietary data containing valuable insights. Simply put, selling them is the easiest way to monetize data. The step beyond this is launching a fully-fledged business providing data.
Becoming a commercial data provider might entail selling your proprietary data, as we explained above. Alternatively, you could buy a license for a dataset from another party and build your business selling that. Usually, you build the software to help you sell this newly-acquired data. For example, Forager.ai built a web scraping service which collects then supplies constantly-refreshed B2B data to end customers.
An often overlooked means of extracting value for data (ergo monetizing it) is one of the most obvious. That is, by using data to make internal processes more cost-efficient.
Consider when Satya Nadella became Microsoft's CEO in 2014. His first move was to urge employees to question their current workflows using only their internal data. This included time salespeople spent with customers and their closed-won rates. Working with current sales data, Nadella's executives were able to better predict successful sales and see where to improve productivity.
This is a non-obvious example of data monetization. No data was sold, but its value was realized as it was applied to cut Microsoft's costs elsewhere and reduce their margins.
Data monetization offers several benefits for data providers, and also for the wider technological landscape in terms of incentivizing innovation.
The most attractive part of data monetization is that it allows you to generate a new stream of revenue. This benefit is even more promising if you're capitalizing on your existing data assets. Monetizing data gives enables you to extract revenue from assets which would otherwise lie dormant.
Data monetization entails auditing your existing data government processes. So it can also shed light on company's internal data usage. This helps you improve overall data management practices within your organization, with a view to optimizing your data offerings to then sell them.
Lastly, data monetization contributes to a healthy business and technology landscape. With more data available, more innovation and AI development can take place. It also levels the economic playing field. If every company has valuable data to sell, small fish companies can disrupt the companies which overpower them in terms of other capital.
Data monetization, while beneficial, comes with its share of challenges for data providers.
Firstly, there are privacy and regulatory concerns, such as compliance with data protection laws like GDPR or HIPAA, which require careful handling of customer data.
Additionally, ensuring data quality and accuracy is a constant challenge, as inaccuracies can erode trust and reputation.
Lastly, competition in the data market is fierce and ever-growing as data monetization goes mainstream. Data providers must continually innovate to stay ahead.
There's huge potential in selling data. We see this in startups which made data monetization their primary lines of business. We also see it in established companies that recovered from slumps by monetizing internal information - thereby adding a new revenue stream.
Some data categories simply come with higher ticket sizes. We see sustained high demand and high prices for location data and transaction data, for example. This is because the ROI of such data can be huge for businesses and the process of collecting and preparing such data is work-intensive. Companies with big data budgets usually allocate most of their money towards high ticket data categories.
Nonetheless, you can make money selling lower ticket data categories. For example, many companies have successfully monetized B2B contact data and email lists by selling high volume at low prices.
There's some fluctuation in demand for data depending on many factors. For example, demand for annotated image and transcription datasets lay dormant until the explosion of generative AI in 2022. Now we see that AI, ML, and LLM training data is very valuable. Demand for data is very much tied to the wider market and economic conditions.
Your potential gains from data monetization depend on how much you can invest in your data business. If you're lacking a team of marketers and sellers, your monetization success is limited. Data monetization can generate passive income, but you should expect the money you make to be relative to this low effort. If you're running a sideline data business, it's unlikely you can depend on data monetization as your primary revenue source.
As stated in the Harvard Business Review, the software you need to monetize data most likely already exists. So it doesn't make sense to build your own, data management, sales and distribution tool. Rather, utilize the manifold SaaS products which make your life as a data provider easier.
Data marketplaces are probably the best known data monetization platforms. The data marketplace platform operates much like the online retail or wholesale marketplace. Just as ecommerce sellers list their products on Amazon and AliBaba, data sellers list their datasets and APIs on data marketplaces like Datarade Marketplace, Google Cloud Analytics Hub, and Revelate. Data buyers can then browse data products and compare samples, before connecting with the provider securely and eventually making a purchase.
Data marketplaces are two-sided markets. There’s the data provider, who is looking to commercialize their data assets. Then there’s the data buyer, who wants to find a trustworthy data source who meets their requirements.
Using data marketplace technology to monetize data makes it easy for new data providers to market, manage and sell their data. Typically, data marketplaces make money on a revenue share basis. That is, they take a commission fee once a data provider closes a sale successfully via the platform. How data marketplaces are priced makes them an attractive option for data providers, who only pay when they win business.
The simplicity and efficacy of the data marketplace as a business model has had some negative repercussions, however. Namely, the popularity of data marketplaces for buyers and providers has caused the number of different marketplaces to explode.
Consequently, companies looking to monetize data are faced with a paradox of choice. Which data marketplaces should they prioritze when deciding on channels through which to sell their data? How do they deal with the overhead of managing leads and orders from each separate marketplace? Is one data marketplace more suitable to a provider's data offering than another? These data provider pain points have given rise to another data monetization platform whose purpose is to solve these dilemmas: the data commerce platform.
A newer breed of software is the data monetization platform. Data monetization platforms build on the concept of a data marketplace to offer a more sophisticated solution. It's still a nascent software category, but there are notable players earning a name for themselves as excellent SaaS products to monetize data.
In the B2B space, for example, there are data monetization platforms solving the data provider's paradox of choice. Much like how Shopify enables retailers to sell across many ecommerce sites easily, Monda enables data providers to sell across multiple data marketplaces with one account. Providers create data products on Monda and then publish them in data marketplaces in a click. Ultimately, this makes it easier for companies to monetize data. They can start a data business and get exposure on the biggest marketplaces with minimal sales channel management.
Another B2B data monetization platform is Harbr, which enables companies to start their own data business privately. That is, without listing datasets on public data marketplaces. Rather, Harbr enables companies to build a data storefront, create data products, and scale by automated distribution.
There are also B2C data monetization platforms. Most data marketplaces don't support individual data providers; they'd rather work with companies than consumers. However, new platforms are making data monetization accessible for the individual consumer who wants to profit from their proprietary data.
For example, Hyde is a B2C protocol for private data monetization. By connecting accounts like Netflix, Twitter, or Spotify, individuals can use Hyde to build a significant stream of passive income.
Another SaaS for data monetization is offered by data visualization tools. Data visualization software makes data more visually appealing. That is, easy to analyze, interpret, and act upon, as opposed to pretty... Data visualization tools make for excellent data commerce platforms. They help data providers create great data products they can easily monetize.
AltHub enables companies to transform raw, unstructured data into analytics-ready alternative data products. AltHub's technology creates data products aimed specifically at hedge fund customers. Each product includes all the data attributes and visualizations a hedge fund manager would expect, formatted as cleanly presented, tickerized charts.
'White labeling' is becoming commonplace in the data industry. Basically, white labeling is the process of purchasing the rights to sell a product from the original owner. You then re-sell the product under a different brand.
White label services are a staple in traditional commerce (from coffee to electronics) and are becoming more popular in data commerce. They provide a low-risk, fast-entry path into data monetization. Prospective data companies can buy data from established data providers and start selling data products they know have value whilst building their own brand.
Pricing policies for data monetization vary. Indeed, data valuation is a huge and contentious topic. Data capital is a relatively new commodity. The Economist only made their infamous proclamation that data is 'the new oil' in 2017. As such, methods of valuation are still being developed.
Nonetheless, if you're looking into data monetization, here are the most important data pricing models to know about.
Data-as-a-Product is when you sell data as a flat-file download or made-to-order dataset for a standard price. The benefit of the data-as-a-product model is that you can sell datasets instantly, without much customer personalization required. Working with ready-to-sell datasets reduces the overhead of preparing custom datasets on-demand. As such, the data-as-a-product approach to data monetization is often the most scalable way of making money from data.
However, data-as-a-product doesn't really work if you're selling to to enterprise clients. These data customers require a more tailored approach. This is known as data-as-a-service.
Data-as-a-Service (or DaaS) is a popular model of data monetization. As the data seller, you calculate prices for your data on a case-by-case basis. Factors affecting the data price include its volume, quality, metadata, and refresh frequency.
Or, you charge customers for a subscription to a data API for a given price. This is a more stable form of pricing DaaS because it brings you predictable, if not standardized, data revenue.
A particularly innovative pricing model for data monetization is data-as-collateral. Aimed specifically at companies looking to fundraise, Gulp Data provides monetary loans in exchange for a copy of their data. Gulp Data evaluates the data based on its metadata, including total size, type of data, and age. Once you pay off the loan, Gulp Data deletes your data, including backups and samples, from all custodian infrastructure.
Data-as-collateral is a remarkable way of monetizing data which gives young companies the liquidity they need without waiting 6+ months for investor funding. And in these cases, whatever the ticket size of the dataset, efficiency can be priceless.
The final step in the data monetization journey is delivery. Data providers can employ various methods to transfer data to a paying customer.
Firstly, there are digital downloads, where the customer accesses and retrieves the data files from a secure online portal. This is usually the case with data-as-a-product.
The most popular data delivery method is by providing API (Application Programming Interface) access. allowing the customer to programmatically retrieve specific datasets.
More traditionally, there's periodic data delivery via email, FTP, or cloud storage services. With this more analog approach, data providers must ensure the customer receives regular updates or batches of data conveniently.
The choice of delivery method usually depends on the nature of the data, customer preferences, and security considerations. All of this is worth keeping in mind as you monetize data, as ultimately, you're only going to build a sustainable data business by satisfying the end user.
For the data end user, what's most important is that they can analyze the data right away. When it comes to data analysis, the most-used BI tools include Looker, Qlik, Tableau, Power BI. Most of these platforms offer embedded delivery capabilities as standard, or they offer specific embedded-only licensing tiers. Many also operate their own data marketplaces where you can sell datasets and charts.
This way, the data customer journey spans data discovery all the way to end fulfillment. Obviously, this makes data monetization easier for the provider, who can deliver their data seamlessly and win satisfied customers.
150+ data companies use Monda's all-in-one data monetization platform to build a safe, growing, and successful data business.
Explore all featuresMonda makes it easy to create data products, publish a data storefront, integrate with data marketplaces, and manage data demand - data monetization made simple.
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