Thought Leadership written by Sameer Rahman – Technology Leadership Council Member and CEO of DataMonet.
In today’s digital age, data is more than just a by-product of business operations—it’s a powerful asset that, when harnessed effectively, can be transformed into significant revenue streams. The companies that understand this have already positioned themselves at the forefront of their industries, creating new products, services, and even entire business models from the data they generate.
But how exactly can a business turn data into £s?
Have an Offensive Data Strategy
While defensive data strategies focus on protecting existing assets, minimising risks, and ensuring compliance, offensive data strategies are about growth—using data to create new products, enter new markets, and unlock new revenue streams. Companies like Uber, Amazon, and Netflix thrive because they prioritise offensive data strategies, continually seeking ways to leverage internal and external data sources to drive innovation and commercial success.
An offensive data strategy is about more than just using data to optimise existing processes; it’s about creating entirely new business opportunities. This could involve developing new products based on customer insights, entering new markets by leveraging predictive analytics, or even monetising data directly by selling insights to third parties. The key is to see data not just as an operational asset but as a product in its own right.
Key Components of an Offensive Data Strategy
- Data as a Product: Turn your data into a marketable product or service.
- Innovation through Data: Use data to fuel the creation of new products and business models.
- Market Expansion: Leverage data insights to explore and dominate new markets.
- Customer-Centric Solutions: Create personalised experiences that boost customer loyalty and revenue.
Big Brands Leading the Way
Uber – The Power of Google Maps, leveraging external data to develop your product
Uber’s meteoric rise in the ride-sharing industry is a prime example of how data can be the cornerstone of a business model. While Uber’s innovation in ride-hailing disrupted traditional taxi services, the company’s reliance on Google Maps data was equally revolutionary. Google Maps provided Uber with the detailed geospatial data it needed to create a seamless experience for users, from pinpointing pickup locations to optimising routes.
Over a three-year period, Uber paid $58 million to Google for access to its mapping services—a substantial investment, but one that was crucial to the company’s success. Without the precise and reliable data from Google Maps, Uber’s real-time location tracking and route optimisation wouldn’t have been possible, and the ride-sharing giant might not exist as we know it today. Uber’s story illustrates how leveraging external data sources can create entirely new business models, transforming a simple idea into a billion-dollar enterprise.
Amazon and Netflix: Monetising Data Through Personalisation
Amazon and Netflix are masters of data monetisation, utilising offensive data strategies to create highly personalised experiences that drive customer loyalty and revenue.
For Amazon, data is at the heart of everything it does, from personalised recommendations to dynamic pricing. Every click, search, and purchase on Amazon generates data that the company analyses to predict consumer behaviour, optimise supply chains, and refine product offerings. Amazon doesn’t just use data to improve its own operations; it monetises it by offering services like Amazon Web Services (AWS), where businesses can leverage Amazon’s data expertise for their own benefit.
Netflix, on the other hand, has revolutionised entertainment consumption through its use of data. The streaming giant collects vast amounts of data on viewing habits, which it uses to recommend shows and movies to users. Astonishingly, 80% of the content watched on Netflix comes from its recommendation system—a clear indication of how effective data-driven personalisation can be. This not only keeps viewers engaged but also provides Netflix with insights to guide content creation, ensuring that the platform continually offers what its audience wants.
But, Offensive Data Strategy can be used equally to good effect for SMEs.
Small Companies, Big Impact: How Startups Can Monetise Data
Strava
- Industry: Fitness and Social Networking
- Data Monetisation Approach: Strava, a fitness app for tracking cycling and running activities, gathers massive amounts of data on user workouts, routes, and performance. Strava monetised this data by offering premium subscriptions that include advanced analytics, personalised training plans, and detailed performance tracking.
- Success: Beyond individual subscriptions, Strava also sells anonymised data to city planners and transportation departments. This data helps cities understand how people are moving through urban spaces, allowing them to design better infrastructure for cyclists and pedestrians. This dual approach has made Strava’s data both a direct revenue stream and a powerful tool for influencing public policy.
Aire
- Industry: Fintech / Credit Scoring
- Data Monetisation Approach: Aire is a UK-based fintech company that provides alternative credit scoring solutions. It uses consumer consent data to build a more accurate picture of an individual’s creditworthiness, particularly for those who are underserved by traditional credit scoring systems.
- Success: Aire monetises this data by selling its credit insights to financial institutions, which use them to make better lending decisions. By providing a more inclusive and accurate credit scoring model, Aire has helped banks and lenders reduce default rates and expand their customer base, especially among those with thin credit files.
Darktrace
- Industry: Cybersecurity
- Data Monetisation Approach: Darktrace, an AI cybersecurity firm, uses machine learning algorithms to analyse network data and detect cyber threats in real-time. The company collects and processes large volumes of data from its clients’ IT systems to identify unusual patterns and potential threats.
- Success: Darktrace monetises this data by offering it as a service (SaaS) to businesses across various industries. The company’s AI-driven insights help organisations protect themselves against cyber threats, making their data and analytics platform a critical part of modern cybersecurity strategies. Darktrace has grown significantly and is now a publicly traded company, demonstrating the value of data-driven cybersecurity solutions.
So, how do we go about Monetising data?
How to Monetise Your Data: A Step-by-Step Guide
- Data Audit: Catalogue your data, assess its quality, and clean it up. (Skills: Data Management)
- Pinpoint Valuable Data: Analyse customer and operational data to spot trends and market opportunities. (Skills: Data Analysis)
- Define a Monetisation Strategy: Identify your data’s value and choose a monetisation model—whether selling data, offering services, or enhancing products. (Skills: Strategic Planning)
- Build and Test Products: Develop prototypes, run pilot tests, and gather feedback. (Skills: Product Development, Software Engineering)
- Invest in Technology: Implement tools for data management, analytics, and visualisation. (Skills: Data Engineering)
- Hire or Upskill Talent: Bring in data specialists or train your existing team to build data expertise. (Skills: Talent Management, Training)
- Establish Data Governance: Set up policies for data usage, security, and compliance. (Skills: Data Governance, Legal Compliance)
- Explore Partnerships: Partner with others to access complementary data or co-develop products. (Skills: Commercial Strategy, Negotiation)
- Launch, Measure, Refine: Roll out your product, monitor performance, and continuously improve. (Skills: Product Launch, Marketing Strategy)
- Market Your Offerings: Clearly communicate the benefits, build trust, and use customer feedback to refine your approach. (Skills: Communication, Customer Relationship Management)
Conclusion
In today’s digital economy, data is one of the most valuable assets a company can own. By adopting an offensive data strategy and treating data as a product, businesses can unlock entirely new revenue streams. Whether you’re a multinational corporation or an SME, the potential for data monetisation is vast—if you know how to harness it. From personalised recommendations to external data partnerships, the path from data to pounds is clear, and the rewards are significant for those who are willing to invest in their data strategies.
The post From Data to Pounds: How Businesses Can Monetise Their Data into Lucrative Revenue Streams appeared first on Technology Connected.