DPM Community Notes #1: Data Monetisation
Takeaways from the London Data & AI Product Management community on launching external-facing data products beyond your core business
Intro / meta note from Nick
After 2.5 years of running data & AI product management meetups in London and Barcelona (and inspiring folks to start their own in Montreal and Paris), I’ve been meaning to document the insights from our discussions. These conversations are too valuable to stay in the room!
So when
(who started the Montreal DPM meetup!) took wonderfully detailed notes during our September session, I knew it was finally time to make this happen.On Tuesday, September 23, 2025, the Data Product Management Meetup in London brought together data enthusiasts to explore data monetisation - specifically, how to launch a data service beyond your core business offering. Most participants were from organisations where data isn’t the primary product.
Below is a structured summary of the key takeaways. It’s not a comprehensive guide, but a collection of thought starters and reflections from the session.
A big thank you to
for writing 90% of the below article, to Graham Libaert for reviewing it, and to the other 5 roundtable participants for an excellent discussion last month (we’re keeping the other names private, because we operate under the Chatham House Rule).Lastly, special thanks to Harbr Data for sponsoring September’s meetup, and for being our most active supporter since the community’s inception 🙌
What Is Data Monetisation?
We started by clarifying our terms. “Data monetisation” typically refers to one of two definitions:
The narrow definition: Selling data externally
Represents a new revenue stream by offering data as a product or service to external customers
Requires a robust go-to-market strategy, including marketing, sales, and customer support
Requires strong data governance to tackle data quality, access control, and compliance issues that, if neglected, can lead to financial or legal consequences
The broader definition: Making money from your data
Focuses on optimising internal data usage and evaluating data value
Typically managed by data teams operating as cost centres
May involve chargeback models to allocate costs within the organisation
Our discussion focused primarily on external monetisation (i.e. the narrow definition).
Finding Your First Customers
The first step is understanding who your data could serve. Unlike your core business customers, data customers may come from adjacent industries or entirely new markets. For example, a retailer’s customer data might be valuable to logistics analysts or urban planners.
Research customer pain points and explore how data can solve their challenges. This customer-centric approach ensures relevance and highlights the importance of data discoverability - making it easy for potential customers to find and engage with your data.
Look to ‘hack’ the feedback loop: Get feedback as quickly as possible! This will help guide your decision-making and investment - after all, in the early days you don’t know if your data has value (or how much).
The Power of Data Combination
Because we often don’t fully understand our prospects’ business landscapes, the real value often lies in linking datasets to create richer, more actionable insights.
For example, footfall data becomes much more insightful when combined with sociodemographics: you go from knowing how many people visited a location to understanding who those visitors are - their age, gender, or home postcode.
Making Data Discoverable
Show the Metadata, Not the Data
Make metadata visible (descriptions, sample insights, and usage context) without exposing the full dataset. This creates a “FOMO” effect: potential customers see what’s available and want to explore further.
Think of it like a storefront window: you’re not selling the whole inventory, but you’re showing enough to make people want to come inside.
Simple Signals Matter
One participant shared adding a “Become a Data Partner” button to their website. It’s a low-effort, high-visibility tactic that signals openness to collaboration and invites dialogue.
This visibility moves you from passive availability to active engagement. However, exposing data externally also reveals governance weaknesses, such as poor data quality or inadequate access controls.
The Quality Bar Is Higher for External Customers
Multiple participants emphasised this: external customers have higher expectations than internal colleagues. Data quality issues that internal teams tolerate become dealbreakers for fee-paying customers. If there’s an outage, schema change, or other incident, you could breach contract terms or lose customer trust. Unlike internal colleagues, your customers have alternative vendors.
Key takeaways:
Get your governance in order before selling data.
Target use cases that match your current data quality levels. For example, if your pipelines sometimes fail overnight and cause a 1-day delay, don’t target clients who need real-time data 24/7.
Packaging: Services and Delivery Formats
To meet external quality standards, invest in supporting services:
Support models: Customers need someone to talk to for onboarding, troubleshooting, or clarifying data definitions.
Quality management: Implement validation checks, version control, and continuous monitoring.
Incident management: Have a clear process to detect, communicate, and resolve issues quickly.
Access control: Define who gets access to what, and ensure compliance with regulations like GDPR.
Choose the Right Delivery Format
The format should align with your customer’s technical capabilities:
Raw Data as a Service (DaaS): Ideal for tech-savvy clients like data scientists or engineers who prefer to ingest and model data themselves. Offers flexibility but requires technical infrastructure.
Embedded BI within existing SaaS: Designed for non-technical users who need ready-to-use insights. Dashboards or curated reports embedded into workflows reduce friction and accelerate decision-making.
API Access: For customers who want real-time or automated integration. Supports dynamic use cases like supply chain optimisation or predictive analytics.
Data Apps or Interactive Tools: Packaging data as a lightweight application can drive better usability, allowing users to explore scenarios or personalise insights without touching raw data.
Tailoring the packaging ensures accessibility and value, but requires a clear understanding of costs to sustain the offering profitably.
Pricing: The Hard Part
When we got to “how do you work out the right price?” there were giggles - because pricing multi-industry, multi-use case products with complex cost bases is hard.
We grouped pricing options into three categories:
Cost-based pricing: Add a margin on top of production costs
Value-based pricing: Price based on the commercial value the data enables for the client. The same data could be priced completely differently depending on use case or industry (e.g., hedge funds might pay 10x more than government departments).
Market-based pricing: Look at how competitors price equivalent products, especially when you’re new to a market.
Options 1 and 3 are typically low-margin. Option 2 is ideal but hardest to achieve, either because (a) you don’t have a clear idea of your data’s commercial value to clients, or (b) competitors are willing to sell for much less.
Among the group, there was consensus that pricing should be as simple as possible - complex, multi-variable pricing models make it harder for customers to understand costs and commit to buying
The other key factor influencing pricing is unit economics (the revenue and costs per customer).
Understanding Your Unit Economics
You need to know how much each sale adds to your bottom line - and whether each sale generates more than it costs.
For example, if you produce a standardised weekly dataset at a fixed cost of $1,000 and sell it for $100 per client, you break even at 10 clients. Every subsequent sale becomes profit (though there’s usually some variable cost like storage or customer success staff).
If you’re producing something bespoke for each client (e.g. custom data engineering, connectors, enrichment) ensure that extra work is reflected in the price.
Critical Questions to Answer
Key questions you should know the answers to:
How much are we spending to deliver each data product to each customer?
If we doubled our customer base tomorrow, how much would costs increase?
For complex products with many dimensions, do we understand (even roughly) how each adds to cost?
How does total cost break down into storage, compute, licenses, and labour?
Understanding these costs is essential to price sustainably and decide whether to bundle services, offer tiered access, or charge per usage.
A sustainable monetisation strategy hinges on a clear cost model that covers development, maintenance, storage, computing, and support. By parametrizing these costs, organizations can automate pricing for consistency and scalability.
For those leveraging LLMs, offering model-switching options within packages can optimize costs based on use cases.
Start pricing simple -perhaps a fixed rate- then evolve into tiered models based on factors like geography, time range, or market segment.
The ‘Free Sample’ Trap
Several participants mentioned the trap of offering free samples, especially when producing them requires custom work. Free samples are often seen as worth $0: clients don’t prioritise their evaluation, and you spend months waiting for feedback.
When a customer pays (even a nominal amount you can credit toward the final price) they’re signalling commitment. It means the data is valuable enough to get budget approval, but also for them to actually spend time evaluating the data and make a decision.
In short: Avoid giving complete datasets away for free. (We’re talking about actual datasets here, not a 5–100-line sample meant to show what’s inside.)
Selling Data Products: Working with Sales Teams
Toward the end, we explored the cross-functional collaboration required to make data monetisation work, focusing on when to use your existing sales team, how to align incentives, and how to build effective partnerships.
Should Your Sales Team Sell Your Data Product?
If your business already sells data products, you have trained salespeople. But for everyone else (supermarkets, telcos, SaaS companies) your salespeople are probably the wrong fit, at least initially.
In the early days, you likely haven’t identified a solid product-market fit:
You have use cases in mind but aren’t sure which will be your big winners. For example, you know your socio-demographic data can be used across many industries, but you’re not sure which ones will get the most value or pay a premium.
Target customers may not yet be aware of your data’s value. For example, commercial real estate businesses are used to traditional tenant engagement, which doesn’t involve showing footfall data.
Your current customer base might be totally different from data product buyers. For example, an EV chargepoint operator sells to property managers but wants to sell data to automotive manufacturers - your salespeople don’t have contacts in automotives and don’t want to spend time in an unfamiliar market.
Your sales process is more consultative: you diagnose business problems in unfamiliar industries and propose solutions. This differs from commoditised sales, where your team has narrowly-defined target customers and specific pain points your product solves. Selling data can be more complex because you’re also selling a transformation in how clients work.
(By the way, this is why consultancies don’t typically employ dedicated salespeople -consultants do the selling because they also need to diagnose problems and create solutions.)
If your sales team already takes a consultative approach, you might add data products to their portfolio. But if they’re used to selling a specific product to customers who know they want it, you need to be more closely involved.
Aligning Incentives
Salespeople are “coin-operated” - motivated by clear financial incentives like commission. This matters in two ways:
If selling your data product is much harder than selling the rest of the portfolio, salespeople won’t prioritise it.
If you’re trying to get facetime with a client, the account owner might think you’re stealing their commission. Clarify they’ll get their commission regardless.
Introduce data product-specific incentives tied to data sales or bundled opportunities. This motivates sales teams and encourages them to spot new ways to embed data into existing deals.
Building Trust and Partnership
Salespeople are protective of their clients and don’t want to risk losing commission. You need to demonstrate you’re (1) safe to put in front of clients, and (2) someone who can help them sell more.
Frame your involvement as helping close deals. Focus on how your involvement in the consultative sales process means a bigger commission check and faster deal close - not that this will “help establish product-market fit.”
This is classic change management: not everyone will be onboard from day one. Focus on salespeople who see the potential and join forces with them first. Results speak louder than product visions on PowerPoint slides.
The Sales-Data Product Manager Partnership
One effective strategy is pairing Sales and Data Product Managers in a “good cop, bad cop” setup.
Sales professionals excel at building rapport, understanding pain points, and positioning solutions. They speak the customer’s language and identify where data products fit into strategic conversations. However, they often lack technical understanding of the data’s potential, making it hard to pitch its value.
Data Product Managers bring technical credibility, strategic clarity, and realistic boundaries. They translate data capabilities into business outcomes and ensure customers understand what’s possible, scalable, and compliant.
Together, they create a balanced client experience. Sales learns about the data offering; Data Product Managers gain insight into customer language and priorities. Over time, this builds a stronger go-to-market rhythm.
There’s more to it…
While the above is long, it’s just scratching the surface!
Some of the topics we didn’t get to (or barely touched on):
Cannibalisation with core business and/or other data products
What your contracts can and cannot help with
IP rights & different licensing models
Building privacy-first data products
Ensuring regulatory compliance
Working with Finance
Skills & roles needed
Data Marketplaces
Go To Market
Partnerships
…and more
There’s a lot to it!
Call for feedback: If data monetisation is a topic of interest, we can do more deep-dive articles and/or video events 👀 Let us know! Leave a comment, send an email, or drop me (
) a DM.PS: We’ll be joined by Anthony Cosgrove this week (15th Oct) for another community webinar, and it will be very relevant to the topic of data monetisation! So if you found the above interesting, definitely join us on the 15th.
About the Data & AI Product meetup
As mentioned at the top, this discussion was one of six roundtable discussions that happened in parallel during last month’s London Data & AI Product Management meetup.
In London and Paris (which I,
, run directly), we meet monthly.Our blurb about the community:
Data and AI product management is still a young discipline, and there aren’t many spaces dedicated to learning from peers.
So we started this meetup in 2023 to change that! Since then, it’s grown into a vibrant community with chapters in London, Barcelona, and Paris.
Whether or not “product” is in your job title, if you’re involved in shaping data, analytics, and AI initiatives (e.g. product managers, strategists, BAs, data scientists, engineers, analysts) you’ll find like-minded people here.
This is an informal, welcoming space to swap lessons, share challenges, and enjoy drinks and snacks along the way 😊
Upcoming events:
October 14: Monthly meetup in Barcelona
October 15: Online webinar with Harbr’s Anthony Cosgrove (which, by the way, will be super relevant to the topic of data monetisation!)
October 28: Monthly meetup in London
& in case you missed it, we also recently held two other webinars for the DPM community that you can watch:
Building Influence Without Authority: Lessons from 10 years at McKinsey with Clare Kitching
Stop Building Data/AI Products Nobody Uses! How UX & Product Boost Adoption with Brian T. O’Neill
Thinking of starting your own local chapter? Let me know! And check out the below article / call to action to find out more:
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