Data has rapidly become the lifeblood that fuels digital transformation and competitive advantages for modern enterprises. But data’s value remains largely untapped without systematic monetization strategies to actively convert latent data assets into tangible revenue streams.
Done right, data monetization unlocks game-changing new value for organizations – increased profits, mutually beneficial partnerships, accelerated innovation, and data-driven decision making. Global digital leaders like tech giants Facebook, Google, Microsoft, and Amazon have built entire empires by monetizing customer and operational data. Most every other major company is now racing to follow suit.
In this comprehensive guide, we will map out the immense opportunities of data monetization and provide an expert playbook to launch highly rewarding data monetization initiatives.
Why Data Monetization Matters More Than Ever
Before exploring tactical how-to advice, it’s important to understand why data monetization should be an urgent priority on every executive‘s agenda given today‘s data-centric business landscape.
The global market size for data monetization is projected to exponential grow in the coming years, reaching a staggering $19.1 billion according to IDC. The tech research firm highlights 3 macro trends driving this astounding adoption of data monetization across industries:
1. The Data Analytics Market Surge
Investment in data analytics, AI, and advanced visualization continues to accelerate as organizations try to become more data-driven. But most companies are unable to actually monetize their growing data assets – the next evolutionary step to deliver full value from data.
Leading organizations now recognize data as a “natural resource” on par with other tangible assets, necessitating initiatives to refine data into sellable products.
2. Increasing Need for Revenue Diversification
With economic turbulence challenging many traditional revenue streams, companies urgently require new sources of profit. Both startups and mature enterprises are turning to data monetization to power much needed diversification.
Innovators extract maximum value from data assets by packaging and optimizing them specifically to serve customer needs on an ongoing basis. Creative data monetization delivers more predictable, recurring revenue.
3. Surging Potential of Data Partnerships
As the prominence of organizational data assets grows exponentially, mutually beneficial data partnerships are skyrocketing. Combining unique data sets creates richer insights and powers incredible new products.
Via secure data marketplaces and exchanges, companies are forging partnerships to share data assets. Joint data products better serve shared customers and end markets, advancing both parties’ competitive positioning.
Let‘s now explore the types of data with monetization potential before detailing ways to tap into this $19 billion opportunity.
8 Categories of Data Capable of Delivering Value
While the specifics vary widely across industries and business models, these 8 data categories possess considerable inherent value:
1. Customer Data
Granular behavioral data on how specific customers engage with brands – purchasing patterns, product/content preferences, pain points, churn risks factors, lifetime values, etc. Extremely useful for hyper-personalization and loyalty initiatives.
2. Transaction Data
Specific details on individual product purchases, service consumption habits, usage metrics, and shopping interactions collected over time. Critical for understanding micro-market nuances.
3. Operational Data
Real-time and historical performance data from across corporate functions – sales, finance, inventory, manufacturing, fulfillment – essential for monitoring enterprise health.
4. Industry Benchmarking Data
Market share percentages, competitive adoption rates, aggregate performance metrics, ranking data, and other comparative industry stats not available elsewhere.
5. Location/Route Data
Geospatial patterns related to traffic flows, shipment routes, public transit usage, foot traffic, weather patterns over time and region. Supports network optimization for partners.
6. R&D Data
Emerging patents, proprietary research studies, pre-release product testing metrics, and scientific data assets originating internally. Jumpstarts innovation cycles industry-wide.
7. Partner Ecosystem Data
Unique supply chain visibility from interconnected systems across expansive corporate ecosystems. Next-generation transparency for vendors.
8. External Market Data
Macroeconomic forces, local demand shifts, commodity pricing trends, micro geo-economic statistics. Contextualizes internal data.
This analysis only scratches the surface of monetization potential. Creative data packaging tailored to specific customer problems expands opportunities exponentially.
Now let’s explore popular methods for actually monetizing data assets to drive recurring revenue…
5 Models to Monetize Data Assets
While still an emerging discipline, companies have proven 5 viable models for wringing ongoing revenues from data:
1. Package Raw Data Feeds
Structure granular records and data points into API-accessible feeds subscribed to by customers for internal analysis. Facilitate self-service access.
2. Sell Analyzed Reports/Insights
Digest raw data sets into analysis summarizing perfrmance benchmarks, emerging trends, and actional insights beyond partners’ internal abilities.
3. Embed Predictive Analytics
Integrate machine learning modules trained on rich proprietary data directly into partners’ applications to prescribe actions aligned ahead of events.
4. Offer Data Query Services
Provide experts, tooling, and infrastructure for customized data exploration. Deliver results tuned precisely to nuanced Line-of-Businesses problems.
5. Launch Data Marketplaces
Operate transactional platforms, leveraging blockchain in some cases, for external entities to buy/sell/exchange data products. Facilitate discovery.
The most lucrative models provide data and insights customized exactly to partners’ strategic business contexts and questions, yielding incredible value.
Now let’s look at the step-by-step process to build sustainable data monetization initiatives internally…
10 Steps to Monetize Data Like an Enterprise Leader
Evolving from traditional product-centric business models to data-fueled leadership positions takes much more than just deciding to charge for data assets one day. Developing the capabilities for ongoing data monetization requires concerted strategic planning and execution across 10 key steps:
Step 1: Articulate Core Business Objectives
Define the primary driver behind data monetization. Pure revenue goals or an enabler of wider digital transformation? Establish success metrics like new profit totals, data-centric partnership formed, or operational efficiencies gained.
Step 2: Audit Existing Data Assets
Catalog all internal and external data streams already captured in corporate systems. Classify relevance to external entities and inherent value. Uncover gaps blocking monetization readiness down the road.
Step 3: Model Target Data Products
Speculate potential offerings aligned to customer needs, then work backward to required data. Incorporate insights from customer advisory boards on product-market fit. Will you also enrich first-party data with external data acquisition?
Step 4: Architect Data Infrastructure
Design future-proof pipelines, lakes, warehouses, and governance to collect, process, and analyze data at enterprise scale. The robustness directly impacts monetization products down the line. Don’t forget data quality guardrails.
Step 5: Develop Analytics Capabilities
Assemble cross-disciplinary data science teams combining business context, statistical methods, and software skills. Analytics fuel data monetization, so invest heavily. Employ automation to accelerate time-to-insight.
Step 6: Implement Data Security
To maintain customer trust in data sharing, implement layered controls – access management, microsegmentation, robust encryption, network security, end point protection, and data loss prevention. Adopt zero trust framework backed by cyber insurance policies.
Step 7: Meet Privacy and Compliance Mandates
Appoint centralized governance teams to continually track evolving regional regulations and address data privacy stipulations around consent, pseudonymization, age restrictions, etc. Don’t lose partnerships to policy violations.
Step 8: Structure Monetizable Units
Organize aggregated data feeds, interactive self-service analysis portals, insights-packed reports etc. into modular units as sellable data products. Devise pricing tiers and packaging aligned to customer contexts.
Step 9: Test and Refine Offerings
Solicit feedback from friendly customers and prospects on data offering relevance to priority problems. Tailor product features, packaging, positioning, and pricing based on responses before market rollout.
Step 10: Market, Sell, and Engage
Educate commercial teams on unique value propositions of data monetization offerings. Motivate partners through incentives. Continuously gather customer feedback driving product enhancements over time. Monitor KPIs including customer satisfaction, product ROI delivered, data engineering velocity, and obviously revenue totals.
The devil lies in developing mature capabilities around these 10 pillars over a multi-year timeline with sustained executive backing and cross-team collaboration. But companies who stick the course are discovering incredible partnerships and previously unattainable profits.
Critical Enablers of Enterprise Data Monetization
Beyond the 10 preceding adoption steps, 5 essential organizational enablers set apart the world’s most advanced data monetizers – units leading digital disruptors rely on heavily:
1. Data Marketplaces and Exchanges
Transactional platforms facilitating external discovery, collaboration, and orchestration involved in buying/selling/sharing data assets at scale between enterprises. Can utilize blockchain.
2. Advanced Analytics Practices
Sophisticated statistical analysis, machine learning modelling, and visualization tools mixing internal data, open data sets, and first-party data to unearth trends and insights partners crave.
3. Data Science Teams
World-class data experts bridging software engineering, analytical methods, design principles and business context into cohesive teams generating monetizable data outputs.
4. Cyber Insurance Investment
Supplementary policies financially protecting against data breaches, leakages, hacks, or insider threats derailing data monetization initiatives and eroding precious customer trust
5. Executive Commitment
Given multiyear commitment required for data monetization success, founders, CEOs, and governing boards must provide consistent strategic support and investment covering talent, tech tools, and operational changes.
There are no shortcuts to replicating the data-first competitive advantages infusing digital disruptors growing at exponential rates in every major sector. But organizations focused and willing to embrace the data economy zeitgeist can still craft their own lucrative flavor of data monetization!
Now let’s pivot to the biggest risks jeopardizing data monetization success…
Despite incredible potential, externalizing and productizing previously underutilized data carries legitimate risks if not managed properly. 8 primary threats to address include:
1. Customer Data Misuse
Without consent, anonymization, and security controls, sharing data externally raises severe privacy issues destroying consumer trust and company reputation fast. Prioritize ethics.
2. Cybersecurity Compromises
High-profile breaches make security non-negotiable. If sensitive data is leaked/stolen during monetization, customers will walk away instantly from risky vendors.
3. Non-Compliance Penalties
Violating strict regional regulations around consumer privacy, localization etc carries heavy fines completely erasing hoped-for data monetization gains in a flash.
4. Legacy Tech Constraints
Attempting data monetization within legacy, fragmented IT systems quickly gets complex and costly. Modernization becomes prerequisite.
5. Organizational Silos
With coordination needed across technology, analytics, sales, privacy, and security units, ingrained silos severely slow or obstruct data monetization momentum if not addressed.
6. Pricing Complexity
From packaging modular offerings to incentivizing customers and optimizing price discovery, pricing B2B data products proves persistently challenging early on. Expect pivots.
7. Partnership Misalignment
Rushing into data partnerships without aligned incentives, contractual protections, and tight coordination introduces substantial risk of failure on joint data initiatives and products.
8. Measuring Impact
As monetization offerings scale over years, complexity obscures clear measurement, attribution and optimization. Without crisp metrics tightly coupled to core value drivers, leaders risk flying blind.
But with programmatic risk management and sustained strategic commitment to developing complex organizational capabilities, none of these barriers need thwart committed data monetization ambitions over the long run. The world’s most prolific monetizers have pioneered proven paths forward.
Data Monetization Blueprint by Sector
While tactics and offerings must be tailored to individual company contexts, analyzing adoption blueprints across sectors provides an invaluable head start, helping leaders imagine possibilities.
Here are snapshots of data monetization in action across 5 industries:
Banking
Banks including Citi, Chase and HSBC monetize purchase data, spending habits, and personal finance metrics by selling benchmarking reports to institutional customers like retailers, hospitality brands, and airlines trying to optimize offerings to align with emerging consumer trends.
Insurance
Auto insurers like Progressive and Geico sell policyholders discounted premiums in exchange for collecting vehicle sensor data. This is anonymized and resold to urban planners trying to optimize infrastructure around mobility patterns and accident black spots.
Retail
Amazon monetizes their goldmine view of customer shopping habits, Prime subscriber engagement and loyalty metrics, inventory data, supply chain visibility and more via Amazon Web Services data offerings. Brands directly leverage insights to boost personalization and inventory planning.
Software
Microsoft monetizes real-time visibility platform usage data from their 400+ million Office 365 commercial subscribers by selling access to partners like Dell wanting to fine tune future configurations and preload software based on insights around growing organization needs.
Industrial
Jet turbine manufacturers GE and Rolls Royce are monetizing real-time sensor outputs tracking turbine efficiency for airline customers by selling comparative benchmarking to utility providers also managing large power generating assets.
As niche as each sector remains, the examples confirm overarching commonalities around partnering closely across supply chains to enable mutually beneficial outcomes from shared data asset utilization.
The Road Ahead: Innovations Accelerating Data Monetization
Far from a static one-time initiative, savvy data monetizers are forging ahead with bleeding-edge innovations to accelerate profitable outcomes:
Embracing AI to Enhance Personalization
Rich customer analytics informs hyper-personalization of data products and insights tailored to individual partner needs in real time via artificial intelligence capabilities.
Launching Blockchain-Based Marketplaces
Cryptographically enhanced data exchanges facilitate trusted discovery, transactions, and exchange of data assets through tokenized participation models via blockchain-powered platforms.
Cultivating Data Broker Partners
As intermediaries between data rich companies and insight-hungry organizations, data brokers provide economical avenues to external data monetization while handling customer acquisition costs.
Conclusion: Data Delivers a Sustained Competitive Edge
In closing, data monetization represents the cutting edge of digital transformation – underpinning the data-first business models granting Google, Amazon, Meta, Microsoft and other digital leaders decisive competitive edges.
With data permeating the foundations of nearly all organizations today, converting latent data assets into tradeable, recurring value accelerates growth considerably.
But hastily jumping into data monetization without 12-36 month commitments to developing security, analytics and talent can erode hard-won customer trust instantly.
By taking a programmed approach outlined here spanning strategy, execution, and sustained innovation across security, analytics and specialization – leaders can securely transition existing companies into thriving data-first powerhouses.
The window for proactive adoption is closing fast however as defensive-minded laggards find data assets commoditized overnight by more sophisticated players already monetizing at scale today.
