Monetize your data to create a new revenue stream for your company
Whether you are trying to establish a new niche, carve out a bigger share of a mature and competitive market, or increase the value of your existing products or services, data can be used to gain a competitive advantage and increase your revenue.
In this article, we will establish what data monetization is, how it translates to business use cases, showcase how it impacts business performance, and offer guidance on how to start monetizing data today.
Data monetization is a business model in which a company’s own data is used to get economic benefits.
The economic benefits are increased via two general data monetization methods:
Although the article will heavily focus on monetizing your own data, don’t forget you can get all the benefits and more by acquiring data from another company that monetized it.
Let’s now look at those data monetization strategies in more detail.
Generally, there are 4 use cases of how a company can monetize its data assets.
Raw data sets can be sold directly to external stakeholders. There are multiple use cases for selling raw data sets directly:
Data providers are commonly found in financial markets (selling performance data of a single financial instrument in real-time via API calls) and B2B markets (Gartner succeeded in establishing itself as one of the best data providers out there).
Instead of raw data, data is pre-processed by the seller and only insights are shared with buyers and stakeholders. This includes forecasting reports, market trends, survey summaries, customer behavior insights, and other data insights that impact the bottom line.
From embedding analytics into your product to offering analytics as a service via an API in real-time, data analytics as a product or service can be used to upsell existing customers or establish new sources of revenue.
Unlike data insights as a product or service, data analytics is more interactive, offering stakeholders the ability to build their own business intelligence and improve decision-making.
For example, an app can offer a higher paid tier to allow the visualization of specific metrics in dashboards.
Companies often come together and share data sets for mutual benefits.
For example, a telecommunication company and a financial institution might have their own customer data sources. By blending them, they get a better understanding of who the customer is - telecommunication understands better the purchasing patterns in their customer base and the financial institution gets insights into what media their customers are consuming.
Mutual data sharing and enrichments can be indirectly connected to revenue increases. For instance, many companies share data with their business partners to strengthen the strategic partnership (better supplier deals, faster ticket resolutions, …) rather than gaining a direct economic advantage.
Now that we understand the data monetization methods and strategies, it is time to ask: is data monetization even worth it?
Irrespective of the type of data monetization, there are 4 advantages to the process.
The most direct and tangible advantage of data monetization is new revenue in your bank account.
The revenue streams can come from a new product (selling data, insights, or analytics directly), by upselling existing customers (e.g., paying a premium for analytics or insights from your product usage), or by discovering new business opportunities via internal uses of data and exploratory initiatives (e.g., mining your own data to discover better customer segments or opportunities to cut costs).
In data-mature markets, the low-hanging fruits have already been collected by data-driven companies.
Data monetization allows you to gain a competitive advantage over your business rivals by offering a better product or service.
Customers perceive products with embedded analytics as a value-add, sharing big data insights is a customer recruitment strategy (OK cupid has been doing it for a decade), and ultimately, discovering what customers want and including the feature or functionality in your product elevates your offerings.
You can leverage data for intangible benefits. Sharing data and insights along your supply chain and among your business partners can strengthen your strategic partnerships that help you and your partner grow.
For example, our client Rohlik, the e-commerce giant, shares customer purchase data with its suppliers, to help them be better informed about future demand, so they are always stocked for Rohlik’s needs (read the entire customer story here).
Customer data can be used for data-driven optimizations to your own product and services.
The internal use of data can elevate the customer experience (therefore leading to increased customer loyalty), streamline product development (build features and functionalities your customers need), and optimize operations (discover unnecessary costs and risks and mitigate them).
Entirely new products can be born at the intersection of multiple benefits
For example, Facebook used internal data monetization to develop one of the best advertising platforms in the world.
By relying on its own data from the social media usage, they integrated the data into their advertising algorithms (superior product) and offered them to others.
Companies could reach better audiences than on other advertising platforms at the time (competitive advantage) and they got a free introspection on how advertising was working (analytics as a service).
But the use of data for getting advantages also comes with its own challenges.
As with any business model, to be successful at data monetization you have to rise and meet multiple challenges.
Some of the challenges are familiar for a new product or service launch: determine if there is a demand on the market, set up the correct pricing, market and advertise your offerings, …
Other challenges are specific to data monetization. Let’s look at the latter.
Data can be offered as a product or service directly as raw data, as insights, or it can be embedded as analytics into your existing apps (for a price or for a product improvement).
Sharing data with external stakeholders needs more support and explanation than when company data is used internally. Whether it is raw, insights, or embedded analytics, you will have to equip your data with tools for interpretation,
External stakeholders might be unfamiliar with how data is generated in your industry, what specific metrics mean, or how to interpret missing or anomalous data.
A best practice is to build a data catalog alongside your data. The data catalog equips your data sets with all the necessary (meta)data so the data sets can be self-serviced.
Data sharing is often limited by regulatory compliance. From privacy laws (like GDPR) to intellectual property regulations and consumer acts, you need to make sure the data you share (in)directly does not violate any regulations.
A best practice employed by successful data monetization companies is to equip your data monetization strategy with a data governance framework that clearly defines rules of engagement, sharing and privacy considerations, and data sharing permissions.
When you expose products to external users, there are multiple data quality challenges you need to consider. From versioning your data (changes) to validating data sets and defining permissible formats for data downloads and exports, it is a best practice to equip your entire data pipelines with QA tests and expose internally the documentation for reading data consumers.
Data sharing can be technologically hard. Often companies have the raw data sets or even the insights. But their path to full data monetization is hindered by technical challenges for which they do not have the resources or technical know-how to surpass them.
Whether it is constructing an API for secure data sharing, a reverse ETL pipeline for embedded analytics, or a data exchange platform to act as a data brokerage between two parties merging data sets, the technical challenges are often the hardest (and most laborious) to surpass before a company can fully tap into the benefits of data monetization.
The best practice for surpassing technical challenges is to rely on third-party solutions and platforms that do the engineering heavy lifting for you, so you can focus on the business model of your data monetization strategy.
Keboola can help you with all your technical challenges.
Keboola is the data stack as a service platform that helps you build up and automate all the data operations necessary to monetize your data.
Keboola can help you on your data monetization journey in three ways.
Monetize your data directly with Keboola.
The platform allows you to build and automate the entire data pipeline with a couple of clicks. From integrations to data quality, from data governance to data security, Keboola offers you an ecosystem of end-to-end solutions covering all you need for data sharing. It even comes with an out-of-the-box data catalog to equip your data and metadata with definitions.
You can share the data and insights directly with your stakeholders, or embed them into your own products with reverse ETL.
Keboola integrates with API endpoints or file-based systems, so your data sets can be integrated into product features that delight your customers. While keeping the data safe from prying eyes and fully governed.
Schedule a free consultation to start your data monetization strategy immediately.
Keboola helps you get clean, compliant, and verified data from data brokers. When collecting data from other companies (data brokers), there are multiple challenges of integrating data to your own cloud-based or on-premise data warehouse. Keboola helps you mitigate those challenges by taking care of all the technical parts of the data consumption lifecycle.
Additionally, Keboola can help you assist as an intermediary between you and another business company, so you can share, blend, and enrich each other’s data, while Keboola takes care of the regulatory checks, data quality verifications, and technical heavy-lifting.
One of the largest banks in Europe already worked with Keboola to improve client scoring and customer segmentation by enhancing their customer data with data from a telecommunication company.
Here is the story:
Two leaders in their own field - a telecommunication company and a financial institution - approached Keboola with a simple ask: help them with data exchange.
The companies wanted to share and blend two data sets that could help them each enrich their customer database. The financial institution desired customer information on media consumption and browsing, so they could create better marketing and sales personas and advertise more efficiently. The telecommunication company craved information about where their users actually spent their money, so they better understand the purchasing decisions and potential of their clientele.
The issue?
Both companies had the technical know-how. But complying with regulations on data sharing, implementing an auditable solution, and putting in place checks and balances - in other words, creating an end-to-end secured and governed data brokerage or data exchange - was prohibitively expensive. To the point that the benefits of data sharing would not outweigh the technical costs of implementing a data exchange platform.
The solution?
The Keboola Data Exchange provided a secure, fully auditable environment that accepted one-way hashed and encrypted IDs from each company, and produced enriched datasets with IDs removed, along with an audit report of any activity over the data in the secure environment to prevent any malpractice. Keboola verified the data for quality assurance at data ingestion and at data consumption. The resulting fully anonymized data set was then shared within a separate collaborative environment. The environment was also fully audited and was - complying with regulation - used only for testing and development of a customer model.
Keboola can help with all your data monetization challenges and set you up for success.
Contact us and schedule a free consultation and start monetizing your data today.
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