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Turning Data into Dollars: How You Can Actually Make Money with Data

Cover Data as the New Gold: Navigating the Digital Frontier. Generated by DALLE

It seems like everywhere I turn, people are talking about how data is the new gold. Companies are collecting tons of information from all sorts of places, hoping to strike it rich. But here's the thing: just having data doesn't guarantee success. The real question is, how do you actually make money with it? In this blog post, I'll share some strategies and approaches that can help you turn data into a profitable asset.

But first, let's take a step back and talk about what we mean by "data." In simple terms, data is just a collection of facts, observations, or information that can be processed, analysed, or used to gain insights. It can come in all sorts of forms, like numbers, text, images, audio, or video. Data can be neatly organised, like in a spreadsheet, or it can be more unstructured, like social media posts or customer reviews.

You might be be surprised by some of the things that count as data:

  • Every click, scroll, and interaction on your website
  • The information about your email communications, like who sent it, who received it, and when
  • Social media interactions, such as likes, shares, comments, and mentions
  • Customer support logs, including questions, complaints, and resolutions
  • The content inside PDFs, reports, and other documents
  • Transcripts from customer calls, meeting recordings, or video content

These are just a few examples, but the point is that businesses can collect and use all sorts of data to make money. The key is to recognise the value of data in all its forms and to come up with strategies to turn that data into insights and opportunities that generate revenue.

The Data Gold Rush

A study by IDC predicts that the global data sphere will grow from 33 zettabytes in 2018 to a whopping 175 zettabytes by 2025. To give you an idea of how much that is, if you stored 175 zettabytes on DVDs, your stack of DVDs would circle the Earth 222 times!

DVDs DALLE graphic An exaggerated visual representing the data on the planet using DVDs. Generated by DALLE

This massive growth in data is thanks to the rise of connected devices, social media, digital transactions, and the Internet of Things (IoT). Every interaction, every click, and every sensor reading adds to the ever-expanding universe of data. Companies across all sorts of industries are investing heavily in collecting, storing, and analysing data, hoping to uncover valuable insights that can help them make better decisions, improve efficiency, and gain an edge over their competitors.

But here's the thing: just having a ton of data is only the first step. The real challenge is figuring out how to extract value from that data and turn it into something profitable. A lot of companies make the mistake of collecting data just for the sake of it, without a clear plan for how to monetise it. They might have impressive data warehouses and fancy analytics tools, but if they can't use those insights to drive real business outcomes, their data is nothing more than an untapped resource.

To truly strike it rich in the data gold rush, companies need to treat data as a strategic asset. They need to have a clear understanding of what data they have, what insights they can gain from it, and how those insights can be translated into tangible business value. This takes a mix of technical know-how, industry knowledge, and business savviness. Later, I'll cover some of the strategies and methods you can use to start extracting value from your data.

The Power of AI

Artificial Intelligence (AI) has been a real game-changer when it comes to monetising data. AI helps businesses find patterns, correlations, and anomalies in their data that would be impossible to spot through manual analysis. In fact, a study by McKinsey Global Institute found that AI could potentially create $13 trillion of additional economic output by 2030.

One of the most exciting ways AI is helping businesses is through predictive maintenance. Imagine you're running a factory with hundreds of machines, each one generating data on things like temperature, vibration, and pressure. In the old days, you'd have to manually analyse all this data to figure out if a machine was about to break down. But with AI, you can automate this process and predict when a machine needs maintenance before it even starts to show signs of wear and tear.

But AI isn't just useful for keeping machines running smoothly. It's also a powerful tool for fighting fraud. Banks and financial institutions are using AI to analyse transactional data and customer behavior to spot any suspicious activity. Mastercard has taken this to the next level with their AI-powered fraud detection system. This system can analyse billions of transactions and detect fraudulent activities, boosting fraud detection rates on average by 20%, and reducing the number of false positives by more than 85%.

And let's not forget about the magic of personalised recommendations. E-commerce giants like Amazon and Netflix have turned this into an art form. They use AI to analyse every little thing their customers do, from what they click on to what they buy, and then use that data to provide scarily accurate product or content recommendations.

Netflix, for example, attributes over 80% of the content watched on their platform to their AI-powered recommendation system. That's right, AI is basically deciding what you should binge-watch next. And the craziest part? You probably love every minute of it.

But AI isn't just for the big players. Even smaller businesses are getting in on the action with chatbots for customer support. These AI-powered helpers can analyse customer questions and provide instant answers, taking a huge load off of human support staff. And the more they interact with customers, the smarter they get.

H&M, the fashion retailer, has seen great success with their AI chatbot. This virtual fashion guru can give personalised style advice, recommend products, and even help with order tracking and returns. It's like having a personal shopper at your fingertips, 24/7.

The bottom line is, AI is an incredibly powerful tool for monetising data. But it's not a magic wand. Businesses need to invest in the right infrastructure, talent, and governance to really harness its potential. They need data scientists and machine learning experts who can build and train these AI systems, and they need to make sure these systems are transparent, explainable, and aligned with ethical principles.

Strategies for Monetising Data

Alright, so how can you actually make money with data? Here are a few strategies to consider:

Data-Driven Products and Services

Personalised Recommendations

Imagine you're an e-commerce company sitting on a goldmine of customer data. By analysing purchase history, browsing behaviour, and demographic information, you can create highly personalised product recommendations that cater to each individual's preferences. Amazon's "Customers who bought this also bought" feature is a prime example of how data-driven recommendations can drive sales and enhance the customer experience.

DVDs DALLE graphic An example of Amazon's "Also Boughts" for a gardening book

To implement personalised recommendations, start by collecting and integrating customer data from various touchpoints, such as website interactions, purchase history, and customer support inquiries. Use collaborative filtering algorithms to identify patterns and similarities between customers. Develop a recommendation engine that suggests products based on individual preferences and behaviour. Continuously monitor and refine your recommendations based on customer feedback and engagement metrics.

Predictive Maintenance

In industries like manufacturing and transportation, equipment downtime can lead to significant financial losses. Data-driven predictive maintenance can help you stay ahead of the game by identifying potential issues before they occur.

By collecting sensor data from equipment, such as vibration, temperature, and pressure readings, you can build machine learning models that predict when maintenance is required. This proactive approach can reduce downtime, extend equipment lifespan, and optimise maintenance schedules. Companies like GE and Siemens have successfully implemented predictive maintenance solutions, saving millions of dollars in repair costs and lost productivity.

To get started with predictive maintenance, identify the critical equipment in your operations and install sensors to collect relevant data. Develop machine learning models that analyse the data and predict potential failures. Integrate the predictive insights into your maintenance workflows and use them to schedule proactive repairs and optimise resource allocation.

Data Licensing and Partnerships

Location Data Licensing

Companies in industries such as retail, real estate, and tourism are willing to pay top dollar for accurate and comprehensive location data. They use this data to analyse foot traffic patterns, optimise store locations, and target location-based advertising. Foursquare, a location technology platform, has successfully monetised its location data through partnerships with companies like Apple, Uber, and Microsoft.

To pursue location data licensing, ensure that you have obtained proper consent from users and comply with privacy regulations such as GDPR. Anonymise and aggregate the data to protect individual privacy. Package the data in a format that is easy for partners to integrate and use. Establish licensing agreements that outline data usage rights, pricing, and performance metrics.

Data Marketplace Participation

Data marketplaces are platforms that facilitate the buying and selling of data between companies. By participating in these marketplaces, you can monetise your data assets and access valuable data from other providers.

Marketplaces like Snowflake Data Marketplace and AWS Data Exchange enable companies to securely share and consume data in a centralised platform. They offer a wide range of data categories, including financial data, healthcare data, and consumer insights. By listing your data on these marketplaces, you can reach a broader audience and generate new revenue streams.

To succeed in data marketplaces, ensure that your data is high-quality, accurate, and well-documented. Provide sample datasets and clear pricing information to attract potential buyers. Leverage the marketplace's tools and services to manage data access, billing, and customer support.

Data-Driven Decision Making

In today's competitive landscape, making decisions based on gut instincts is no longer enough. To stay ahead of the curve, businesses must harness the power of data to inform their strategies and drive growth. Data-driven decision-making is the process of leveraging insights from data to guide business actions and optimise outcomes.

I've seen firsthand the transformative power of data-driven decision-making in my role as a researcher and as a co-founder of Numvio. In today's fast-paced business environment, relying solely on intuition is no longer sufficient. To stay competitive and drive growth, businesses must leverage the insights hidden within their data.

However, through my experiences working with various companies, I've realised that the journey from raw data to actionable insights is often riddled with obstacles:

  • Data Silos: Data is often scattered across different departments and systems, making it difficult to gain a holistic view of the business.
  • Data Quality: Poor data quality, including inaccuracies, duplicates, and missing values, can lead to flawed insights and decision-making.
  • Skill Gaps: Analysing complex data sets requires specialised skills and expertise that many businesses lack in-house.
  • Time Constraints: Extracting insights from data can be a time-consuming process, especially when dealing with large volumes of information.

(you can read more about my perspective on the challenges to becoming data driven here)

To address these challenges, various solutions have emerged in the market. Business intelligence (BI) tools like Tableau and Power BI provide data visualisation and reporting capabilities, enabling businesses to gain a better understanding of their data. However, these tools often require significant manual effort to prepare and analyse the data, and they may not provide the advanced analytics capabilities needed for complex decision-making.

Data science platforms like DataRobot and H2O.ai offer machine learning and predictive analytics capabilities, allowing businesses to build and deploy advanced models for decision-making. While these platforms are powerful, they often require a high level of technical expertise to use effectively, limiting their accessibility to many organisations.

Conclusion

Turning data into dollars is no easy feat, but it's definitely achievable with the right approach. By leveraging the power of AI, implementing effective monetisation strategies, and having a solid data strategy in place, businesses can unlock the true value of their data. Remember, data alone isn't enough; it's what you do with it that counts.

As you embark on your data monetisation journey, keep in mind that success requires a strategic approach. Start by defining your data monetisation goals, assessing your data assets, and developing a roadmap for implementation. Foster a data-driven culture within your organisation and empower your team with the tools and skills necessary to harness the power of data.

The opportunities for data monetisation are endless, limited only by your imagination and ability to execute. So, seize the moment, dive into your data, and unlock the plethora of insights waiting to be discovered. The future belongs to those who can turn data into dollars, and with the right strategies and mindset, that future can be yours.

Now, I invite you to share your own experiences and insights on data monetisation. Which strategies have you found most effective? What challenges have you encountered along the way? Let's engage in a dialogue and learn from each other's successes and failures.