Latest from our blog
Discover insights, updates, and helpful content.
In the digital era, data is no longer a mere by-product of business operations—it’s a strategic asset in its own right. The most successful companies treat data as tangible and important as cash reserves or intellectual propertypinsentmasons.com. Some even include data on their balance sheets or profit-and-loss statements, reflecting the real monetary value they derive from informationpinsentmasons.com. This shift in mindset—from viewing data as exhaust to valuing it as the new oil—is driving firms to seek out ways to leverage data for competitive advantage and new income streams. Yet the opportunity is far from saturated: while tech giants have built empires on data, only about 9% of small companies have launched data monetization initiatives (versus ~25% of large enterprises)businesshubone.com. In other words, most organizations still sit on untapped data goldmines, and even smaller players can generate new revenue or efficiency gains by treating their data as a standalone product.
Why Treat Data as a Product?
Harnessing data as a product means managing and packaging it with the same rigor as any customer-facing offering. This mindset is exemplified in modern “data mesh” architectures, where data isn’t just an internal asset; it’s considered a product with defined owners, consumers, and quality standardsdeloitte.com. Treating data as a product compels teams to ensure their data is high-quality, discoverable, and usable—ultimately unlocking more business value. Data products can range from raw datasets to AI-ready insights, but to be productizable, data generally needs to meet certain criteria: high quality (accurate, well-structured), uniqueness or exclusivity (not easily available elsewhere), and relevance to a clear demand or problem. Data that is one-of-a-kind or real-time (e.g. unique sensor feeds, niche customer behavior patterns) tends to carry greater value than commoditized information available from public sources. For example, generic demographic shopping data has little value since it’s widely available, whereas real-time shopping preferences in a niche segment can be highly valuable to certain buyersmckinsey.com. In short, data becomes a true product when it is fit-for-purpose and provides unique answers that customers (internal or external) are willing to pay for.
Types of Data Products. Data products fall along a spectrum from raw to refined. On the raw end, companies can offer Data-as-a-Service (DaaS) – essentially selling access to raw, unprocessed datasets (e.g. a feed of anonymized transaction records or sensor data). Further up the value chain are analytics and insights: providing reports, dashboards, or benchmark studies that analyze the raw data into actionable intelligence. For instance, some firms package their data into industry benchmarking reports, giving clients a view of performance metrics against broader trends. Others provide continuous analytics via APIs or dashboards—sometimes dubbed Insights-as-a-Service (IaaS)—where the heavy analysis is done for the customertrianz.com. There are even cases of offering a full analytics platform on top of the data (allowing clients to slice and dice data in a self-service manner), which carries the highest value but is essentially a sophisticated software product bundled with datatrianz.comtrianz.com. The key takeaway is that the more curated and integrated the data product, the higher its value. Selling volumes of raw data can certainly generate revenue, but packaging that data into contextualized information or decision-support tools yields greater value and stickinessmckinsey.com. Successful data monetizers often start with raw data offerings and progressively climb the “value ladder” by adding analysis, context, and easier consumption methods for their users.
Identifying Monetizable Data Assets
The first step in any data monetization journey is a frank audit of your data landscape. It’s common for organizations to discover that they only use about 20% of the data they collect, leaving the other 80% as idle “dark data”conduktor.io. This dark data—logs, historical records, customer interactions, etc., languishing in databases—represents wasted potential (and even a liability, incurring storage costs and compliance risk without delivering value). By shining a light on these under-utilized datasets, you may find high-value information that can be repurposed for new services or insights. For example, years of customer support tickets might be mined (with proper anonymization) for product feedback trends; or machine performance logs could feed into a predictive maintenance solution. The goal is to surface data that others would find useful or that could improve internal decision-making.
When evaluating which data assets are worth monetizing, consider the following criteria:
Freshness: How up-to-date and real-time is the data? (Real-time or frequently updated data commands a premium, since stale data yields less actionable insight. As one expert bluntly put it, “Data has zero value until it’s used,” and its value decays rapidly if not timelyconduktor.ioconduktor.io.)
Uniqueness/Exclusivity: Can this data be obtained elsewhere, or do you have a unique source? Data that is proprietary or hard to replicate (e.g. collected through your own operations or community) is inherently more monetizable. If a dataset is abundant in the market (say, generic census data), its value will be low or zeromckinsey.com.
Relevance and Usefulness: Does the data solve a problem or answer questions that a specific audience cares about? High-value data usually ties directly to improving outcomes – for instance, helping companies reduce costs, understand customers, or meet regulatory requirements. Put yourself in a potential customer’s shoes: what decisions or benchmarks would they gladly pay to improve? If your data can provide that, it’s a candidate.
Also assess quality and privacy aspects early: any data product must be built on reliable, well-governed data, and must respect privacy laws or contractual obligations. Before monetizing a dataset, ensure you have rights to use/sell it (check customer agreements, data provenance) and that it can be shared in a compliant way (e.g. anonymize personal identifiers to adhere to GDPR/CCPA).
Crafting Your Data Monetization Strategy
Define your target market: Will your data product serve internal stakeholders or external customers? An internal monetization strategy focuses on using data to boost your own company’s performance (e.g. better decision-making, efficiency, cross-selling), essentially treating other departments as the “customers” of your data. An external strategy means you’ll offer data or data-driven services to outside customers for revenue. Both approaches can be pursued in parallel, but each requires a different go-to-market plan and mindset. Internal monetization often yields cost savings or competitive advantage, while external monetization creates a direct new revenue stream. Some organizations even start internally (proving value through cost savings) and later productize that same data externally once it’s refined and validated.
Choose the right data product format: Packaging is everything. Think about how your end-users (be they internal executives or external clients) would prefer to consume the data. Common options include:
Raw data feeds or APIs: ideal for tech-savvy customers who want to ingest data into their systems or models. For example, a fintech might pay for an API feed of real-time transaction data to fuel its trading algorithms.
Analytics reports & dashboards: great for delivering insights to less technical audiences. These could be monthly industry trend reports, benchmarking studies, or interactive dashboards that visualize key metrics. Packaging data into a report with commentary can make it more accessible and immediately valuable to business decision-makers.
Benchmarks or index products: if you have aggregate data across many users or firms, you can produce benchmark statistics (e.g. an “industry health index” or performance percentile metrics). Many B2B SaaS companies do this by anonymizing and aggregating customer usage data to sell benchmark reports that clients use to compare themselves against peersluzmo.com. This provides value while maintaining privacy.
Insights-as-a-service: offering custom analysis or predictive insights derived from your data on an ongoing basis. This often involves a combination of a platform and service – for instance, providing a portal where customers can explore the data with built-in analytics tools, or delivering alerts and recommendations (powered by your data) that integrate into the client’s workflow.
When crafting your offerings, align with your corporate goals. Ask how data products can support broader objectives such as revenue diversification, enhancing customer loyalty, or enabling upsell of core services. For example, some companies bundle premium analytics with their main product as an upsell: giving basic insights for free but charging for advanced analysis. This not only creates a new revenue line but can increase customer retention by embedding your services more deeply. In mature markets where differentiation is hard, a unique data product can also be a market differentiator. Studies show that companies effectively leveraging data monetization enjoy better customer acquisition and retention rates than peersbusinesshubone.combusinesshubone.com. Even if you don’t intend to sell data outright, using data strategically (indirect monetization) can precipitate new product ideas or improvements that drive revenuebusinesshubone.com – for instance, uncovering a new customer need from your data analysis could lead to a product feature that attracts new business. In sum, your strategy should clearly answer: Who is the data product for? What problem does it solve for them? And how does that benefit our business (revenue or otherwise)?
Business Models & Pricing
Monetizing data is as much a business model innovation as it is a technical endeavor. You’ll need to decide how you will charge for your data product and under what terms. Several common models include:
One-time licensing vs. Subscription: Will customers buy your data in a one-off transaction (e.g. purchasing a historical dataset or an annual report) or pay a recurring fee for ongoing access/updates? Subscription models (monthly or annual fees for continuous data access) are popular for data feeds and regularly updated insights, ensuring a steady revenue streamgetmonetizely.com. One-time licenses or per-download fees might make sense for static reference data or specialized reports needed infrequentlygetmonetizely.com. Many successful data businesses use a hybrid approach—offering a subscription for core data access, with premium add-ons or one-off deep-dive reports at additional costgetmonetizely.com. For example, Nasdaq’s data marketplace provides subscription access to financial datasets but also sells specialized reports on a per-transaction basisgetmonetizely.com.
Freemium or Tiered Access: Consider providing a basic level of data or analytics for free (or free to existing product customers) to draw interest, while charging for more comprehensive access. A freemium model might offer limited dashboards or a small slice of the dataset at no cost, allowing potential users to see the value and then upgrade for full accessluzmo.com. Alternatively, implement tiered pricing: e.g., a Basic tier with limited data (good for small clients or trial usage), a Professional tier with more complete data and standard analytics, and an Enterprise tier with full access plus customization or integration supportgetmonetizely.com. Tiered models let you capture value from different segments and have been shown to improve customer satisfaction and retention compared to one-size-fits-all pricinggetmonetizely.com. For instance, a SaaS company might include a standard analytics dashboard in its mid-tier plan, but reserve advanced benchmarking or API data access for its top-tier plan or as a paid add-onluzmo.comluzmo.com.
Value-based Pricing: Traditional cost-plus pricing (charging based on your cost to collect/process data plus a margin) often undervalues data products. Instead, anchor your price to the business value delivered. If your data helps a client save $1M in logistics costs or increases their sales by 5%, you can price in relation to that value. Research indicates that value-based pricing can boost profits substantially (10–30% higher than cost-based approaches)getmonetizely.com. For example, Bloomberg can charge premium prices for its financial data feeds because those insights drive lucrative trading decisions for customers, far outweighing the cost to supply the datagetmonetizely.com. When setting prices, talk to potential customers to gauge how they quantify the benefits of your data (e.g. time saved, risk reduced, revenue gained)getmonetizely.com.
Metrics and KPIs: As you launch data products, track metrics similar to any SaaS or product business. Key measures include Customer Acquisition Cost (CAC) for your data product (are you efficiently marketing and selling it?), Average Revenue Per User (ARPU) or per client (how much value are you extracting per customer, and can you expand it with upsells?), and churn/retention rates of data customers. Early on, it may be wise to focus on growth and adoption over immediate profit. Like a startup, a new data product may not be hugely profitable in year one, but success can be indicated by growing usage and low churn. According to McKinsey, many companies separate their data product unit’s finances and incentivize metrics like customer growth, recurring revenue, and lifetime value/CAC ratio in the first couple of years rather than raw profitmckinsey.com. This encourages teams to invest in customer success and iteration, which ultimately drives a healthier business. Once you have multiple customers, also analyze usage patterns (e.g. which features of your data service are most used, which data sets are most popular) to inform pricing and packaging tweaks.
Tip: Don’t be afraid to pilot pricing with a few friendly customers. Data products are new terrain for many, so gather feedback. Perhaps offer an initial free trial or proof-of-concept to demonstrate value, then work with the customer on a pricing model that feels fair. Many data providers, for instance, start with a low-cost pilot or freemium period to drive adoption, then scale pricing once the customer has integrated the data into their operations (at which point it becomes more “sticky”). Also, ensure your sales team (or whoever is selling the data product) is equipped to articulate the data’s value—this often requires new collateral, demos, and possibly technical pre-sales support to answer detailed questions. Pricing and selling data is a different animal than selling traditional products, so plan accordinglymckinsey.com.
Operationalizing Data Products
Launching a data product isn’t just an idea exercise—it requires the right technology and processes to deliver reliably at scale. One critical component is your data pipeline and platform: you’ll need infrastructure to collect, clean, and prepare data, and then to distribute it to customers (whether via APIs, portals, or reports). For raw data products, companies often build a data marketplace or “storefront” portal where customers can browse data sets, preview samples, and download or query datamckinsey.com. If delivering analytics or insights, you may need to stand up scalable analytics platforms or embed BI tools to serve multiple clients simultaneously. Modern cloud architectures make this easier than in the past, but it’s still a significant effort. McKinsey notes that establishing a strong data foundation (with proper storage, processing, and governance capabilities) can take 6–15 months depending on the complexity of your datamckinsey.com—so budget time and resources for this foundational work. The good news is, once the pipeline is in place, adding new data products or customers becomes much faster.
Scalability and reliability are paramount because data products often need to deliver continuous value. Ensure you have monitoring in place for data quality and uptime. Nothing will erode trust faster than a data feed that goes down frequently or reports that contain errors. Consider implementing automated data quality checks and alerts so that any anomaly in the data (e.g. a sudden drop in volume, missing fields, etc.) triggers an internal review before it impacts customers. Many providers establish Service Level Agreements (SLAs) for data quality or delivery timeliness, especially for mission-critical data. For example, if you’re providing a daily benchmark report by 9am each day, you might commit to a certain delivery uptime or cut-off for corrections.
Don’t overlook the human aspect: operationalizing also means setting up support processes and possibly new roles. Who will handle customer inquiries about the data? Do you need a data customer success team to help clients integrate and use the data effectively? These roles are often necessary, as consuming data products might be new for your clients. Additionally, your sales and account teams may need training to sell an intangible like data and to interface with more technical buyer personas (e.g. a customer’s data science team). Some companies choose to spin off the data product unit as a quasi-independent group or subsidiary, to give it freedom to adopt new processes and even new branding distinct from the core businessmckinsey.com. Whether or not you go that far, be prepared to adapt your operating model: product management for data offerings, new pricing and billing systems, legal agreements for data licensing, and so on.
Finally, consider distribution partnerships—data marketplaces (like Snowflake’s Data Marketplace, Datarade, etc.) can help you reach customers without having to build all the infrastructure yourself. These act as app stores for data, where you can list your data product and handle subscriptions/downloads through the platform. This can offload some engineering work (though the marketplace will take a revenue cut). It’s an option worth exploring, especially for smaller providers.
Managing Risk & Compliance
Monetizing data comes with a web of privacy, security, and legal considerations. Chief among these is ensuring compliance with data protection laws. Regulations like the GDPR in Europe and CCPA in California set strict rules on personal data usage—violations can lead to hefty fines and reputational damagetrianz.com. So if your data product includes any customer or user data, you must bake compliance into the design. Strategies include anonymization (removing or tokenizing personal identifiers so that data can’t be traced back to individuals), aggregation (providing only statistical summaries that can’t be reverse-engineered), and obtaining clear user consent where required. Privacy isn’t just a legal box to tick, but a trust issue: a misstep could not only bring legal trouble but also destroy customer confidence in your brand. The Facebook–Cambridge Analytica scandal is a famous example of how perceived misuse of data sparked public outrage and regulatory scrutinymeegle.com. The lesson is clear: be transparent and ethical in how you monetize data. If using customer data, consider opt-in programs or even sharing some value back with customers (e.g. discounts or insights for those who contribute data), and always communicate what you’re doing with the data.
Security is another non-negotiable. When you become a data provider, you’re expected to protect that data against breaches. Implement strong access controls (both internally and for customers), encryption, and monitoring for any unauthorized access. If delivering data via API, use API keys or OAuth with careful permission scopes. You may even explore cyber insurance or specific liability insurance if you’re providing critical data to other businesses—some contracts might require you to carry it, to cover damages in case of data errors or breaches. Speaking of contracts, have a solid legal agreement for your data product usage: it should define how the data can be used by the customer (to prevent them from reselling your data without permission, for example), limit your liability, and include any Service Level commitments. Many providers include clauses that prohibit using the data to identify individuals (if anonymized) or to avoid unethical uses.
Good data governance is essential, especially when your data goes external. Internally, you might tolerate slightly messy data, but once paying customers rely on it, you need robust governance processes. This includes maintaining data lineage (knowing the source of each data element), version control for datasets, and a process for correcting errors. If an error is found in the data, how will you communicate it to customers? Having an incident response plan for data issues is as important as one for technical outages. You might also implement usage monitoring—both to understand how customers use your data and to ensure they’re sticking to the terms (for instance, some agreements might limit sharing the data with third parties, so you’d need to detect any unusual access patterns). In summary, treat data monetization with the same seriousness as a financial service: regulated, audited, and controlled. This will not only keep you compliant but also set you apart as a trustworthy data partner.
Case Studies & Examples
B2B SaaS Platform Selling Benchmarks: Consider the example of a software-as-a-service company that accumulates rich usage data across its client base. Rather than keeping that data purely for internal use, they can anonymize and aggregate it to create industry benchmark reports. Gartner, for instance, leverages anonymized client data to publish reports that set industry benchmarks for various technology sectors, providing tremendous value to readers while preserving client confidentialityluzmo.comluzmo.com. Similarly, many SaaS firms (in HR, finance, marketing tech, etc.) have realized their unique vantage point: they can see across many companies’ operations. One HR platform packaged insights from millions of job listings and salaries to offer a “Talent Market Benchmark” subscription for HR leaders. Another example is Salesforce, which created a Data Studio for secure data sharing – it allows companies on its platform to share and even sell anonymized data with trusted partners as a new revenue sourcegetmonetizely.comgetmonetizely.com. The lesson from SaaS benchmarks is that if you have data that helps businesses compare or improve themselves, you can productize it (as reports, dashboards, or data services) to both enhance your core offering and open new revenue. These initiatives often start as value-add features (e.g. a dashboard for customers to see how they stack up against peers) and can evolve into standalone paid products.
Logistics Firm Monetizing Route Data: Even traditional industries are transforming data into products. FedEx, for example, ships 17 million packages per day, generating massive data on routes, timing, and logistics performancelogisticsviewpoints.com. They realized “information is just as important as the package” being deliveredlogisticsviewpoints.com. FedEx’s Dataworks division enriches internal shipping scan data with external inputs like weather and traffic, and uses it to offer new predictive services. One such product, FedEx Surround, provides real-time tracking and risk alerts for critical shipments (e.g. medical vaccines), allowing intervention to prevent delayslogisticsviewpoints.comlogisticsviewpoints.com. By monetizing this as a premium service for customers with high-value shipments, FedEx turned its internal efficiency data into a value-added product. Another logistics example could be a trucking fleet that analyzes years of route and fuel data to produce an optimization service for other transport companies – for instance, selling insights on the fastest routes under various conditions or an API that alerts shippers about likely delays on certain lanes. In the automotive realm, even car manufacturers are looking to monetize vehicle data (e.g. usage stats, engine performance) by selling it to partners like insurance companies or service centers for preventive maintenance programs. These cases highlight that any company with complex operations can find a data by-product to monetize: if you’ve optimized routes, schedules, inventory, etc., those learnings or the raw data behind them may be valuable to others in your ecosystem. Just ensure you’re not giving away a competitive advantage without proper compensation.
Lessons Learned: Across these examples, a few common themes emerge. First, start with a clear use case – both Gartner and FedEx honed in on specific customer needs (industry comparisons for the former, shipment risk management for the latter). Data projects built “because the data is there” often flounder; built because a customer has a pain point, they flourish. Second, maintain trust and ethics. Gartner’s success rests on strict anonymity and data governance so clients are comfortable participating; FedEx’s value proposition is only as good as its reliability (99.9% success for vaccine delivery with their data tools) which builds trust. Finally, be prepared for an iterative journey. FedEx began by improving internal operations with data (proof of concept) before selling those insights to customers. Many companies pilot their data offerings with friendly customers or internal use, learn and improve, then scale up the external product. A pitfall to avoid is underestimating the support and change management needed—both internally (getting your sales, legal, and support teams up to speed) and for customers (educating them on how to use and derive value from your data). Also, beware of “garbage in, garbage out”: data quality issues have derailed more than a few monetization initiatives. One retailer’s attempt to sell data fizzled because of formidable data quality problems discovered after launch, teaching them to invest in cleaning and standardizing data before offering it to othersmeegle.commeegle.com.
How Holistc™ Helps
As you consider turning your data into a revenue generator, Holistc™ can be your accelerator. Holistc’s platform is designed to surface, package, and secure your high-value data assets – helping you go from raw data to market-ready data products in weeks, not months. Instead of struggling with disparate tools, Holistc provides an end-to-end solution: from data discovery and quality validation, to easy packaging of datasets or dashboards, and secure distribution to your customers with built-in compliance controls. In short, Holistc™ handles the heavy lifting (data pipelines, API management, user access control, audit logs for compliance), so you can focus on crafting the right strategy and content for your data product. With Holistc, companies have launched data monetization pilots in a fraction of the time it would normally take, quickly iterating on pricing and packaging. Whether you aim to offer a simple data feed or a full insights service, Holistc™ empowers your team to do it confidently and safely.
Next Steps
Is your organization ready to start monetizing data? Here’s a quick self-audit checklist to kick things off:
Inventory Your Data Assets: List the data your business collects (operations, customer behavior, sales, IoT sensors, etc.). Identify which data sets are under-utilized or “dark.”
Spot the Gold Nuggets: For each data set, ask: who else could benefit from this information? How might it solve a problem or improve a decision for them? Jot down a few potential internal or external users for the most promising data.
Evaluate Feasibility: For your top 2–3 data product ideas, consider quality and privacy. Can the data be shared ethically (e.g. anonymized)? Is it reliable and updated enough to be valuable? Also, what format would make it most useful (raw feed, report, API, etc.)?
Once you’ve identified some candidates, prioritize one to pilot. You can download our free “Data Monetization Workbook” for a structured template to assess and plan your data product idea. This workbook guides you through defining your value proposition, choosing a business model, and addressing governance considerations. And if you’re looking for expert guidance, we invite you to book a strategy call with our Holistc™ team. We’ve helped organizations across finance, retail, and tech launch data products, and we’re happy to discuss how you can turn your company’s hidden data into real value. Your data has a story to tell—and potentially, profits to earn. Don’t let it just gather dust; monetize it and drive your business forward!
