13.01.2023 | Nikolay Valiotti

Complete Guide on Data Monetization: How to Make the Most Profit from Your Data

With 99% of blue-chip companies like Bloomberg or Phizer investing in Big Data and AI heavily despite all the crises, based on NewVantage Partners’ report, it’s no wonder that today’s business landscape is lapped in data. Many SMBs have jumped on the bandwagon, too, making a rush for data more engaging. However, the above survey reveals that even among the mainstream businesses, less than 40% have succeeded in managing their existing data as an asset.

But data must be handled as a valuable business asset and can be transformed into tangible outcomes, not to mention operational and other improvements, with proper data monetization. Let’s look into the data monetization concept and discuss how to monetize data differently.

The Concept of Data Monetization

Like any monetization, this concept stems from the possibility of turning something non-monetary, such as your business data, into new revenue streams or at least other measurable benefits. These perks may vary from reduced costs and improved productivity to a larger market share or increased market value.

For example, Verizon, the largest wireless carrier in the US, managed to reduce support calls by 43% and cut down tech dispatches by 62%, thus lowering operational costs while enhancing customer experience. To achieve these impressive results, they collected offline and online data sets through various channels and used them to derive actionable insights, which were then shared across executives and customer-facing teams to implement effective informed decisions.

Verizon

In turn, Tableau, another US-based company, this time specializing in data visualization and business intelligence, provided Verizon with tools helping to digest the big data and convert it into actions. However, Tableau uses opportunities to collect data to generate its own analytics, further adding to the company’s offerings: advisory services or instructor-led classes.

Advisory services or instructor-led classes

These examples show that data collected by a company can be shared among internal and external stakeholders, leading us to two major ways of data monetization.

Direct and Indirect Ways of Monetizing Data

What Is Indirect Data Monetization?

As the name suggests, internal data monetization covers all scenarios where a company avails of its own data, although without getting revenue straight away, which is why this approach is also called indirect. This is the main reason behind the data collection efforts of every company — to find room for improvement in business operations and make the best possible decisions rather than using the figures just to create beautiful charts and infographics. While things like risk mitigation, employee productivity improvement, or better customer experience may not result in immediate profits, they eventually lead to cost reduction or gain in sales (and sometimes even both), boosting the company’s performance metrics.

What Is Direct Data Monetization?

Contrary to internal data monetization, the direct method implies data sharing aimed at profit-making but geared toward third parties. Here, a company shares collected data or valuable information sourced with external stakeholders to receive revenue or alternative business benefits. Direct data monetization also comes with a range of opportunities, starting from information bartering, where companies swap customer data to grow their bases of potential clients, and ending with selling data to interested businesses on a subscription basis.

Mixed Approach to Data Monetization

Although the two data monetization types require somewhat different analytics tools and skills, a company can enjoy the best of both worlds with a proper business strategy. We have already described how Verizon leverages data and analytics to optimize internal operations and improve customer service. Still, the telecommunications giant doesn’t stop here and expands its data monetization capability through B2B services. It incorporates aggregated and anonymized data into cutting-edge solutions for businesses, such as Verizon Connect fleet tracking, and creates additional revenue streams using the direct method.

Mixed Approach to Data Monetization

As data monetization possibilities are so diverse, let’s slice and dice each type separately to understand better what benefits you can reap from data collection and how different data monetization strategies can be implemented.

Monetizing Data Externally

Monetizing Data Externally

What Data Needs to Monetize?

As previously stated, a company can monetize data by making it available to customers or partners. But that’s where you stumble across the first important question — are you going to provide data in the raw form, or clean and process it in other ways to ensure data quality, or take it a step further and shape it into insights?

For example, an e-commerce platform that collects customer purchase data can sell a list of every purchase made on the platform without even structuring it, or it can dig into the data silos to come up with ready-for-use insights on buying preferences, which will be of greater value to the company’s partners and other businesses. Obviously, adding your expertise to the pack is a good move. Still, it will require investing more time in research and analysis along with onboarding advanced analytics tools for data processing and visualization.

Whichever way you choose, here is a choice of data monetization ideas to get the ball rolling:

  • Conduct customer surveys
  • Interview industry leaders
  • Hold polls among businesses within your vertical
  • Collect benchmarking data
  • Spot industry trends
  • Make forecasts
  • Compile lists of useful customer information

All in all, you are free to enrich your company data with usage data, competitive data, and other data types obtained through multiple sources.

How to Use Your Information Assets for Data Monetization?

Depending on your business strategy, the structure of your company, and the data you own, you can profit from sharing the assets in several ways and even combine some of the following methods:

  • Provide other organizations with direct access to your data in return for getting valuable information from them or gaining some preferences for your business.
  • Attach data analytics to your existing offerings as a value-added component available in paid packages, top-tier deals, or for premium members only.
  • Create a one-time product and sell it to customers or third parties via your own channels or some kind of data marketplace.
  • Wrap your data into an information product or service, which is constantly updated and distributed on a subscription basis.

Frameworks and Channels for External Data Monetization

Data as a Service

Let’s go back to the raw data vs. insights problem. In the first case, you will serve as a DaaS provider, selling datasets to data brokers or directly to companies looking to increase the breadth and depth of available data for further modeling and analysis. The Data as a Service model is suitable for partnerships aimed at nourishing joint data assets, such as data sharing between healthcare providers and insurance companies. You will also have a range of data marketplaces that connect data sellers with potential buyers. The datasets can be delivered via API integrations or a downloadable data dump.

Insights as a Service

IaaS, which stands for “Insights as a Service,” is a new kid on the block, but the trend to switch from delivering structured and unstructured data to providing businesses with action plans and guidance derived from this data is gaining momentum. Since many organizations don’t have sufficient resources to sift through all those hefty volumes of data, which leaves a significant share of the assets unused, the demand for novel and actionable insights is only growing. If you manage to present your data in a meaningful way, your customers and third parties, like data brokers, will be happy to get a hold of it.

You can distribute the insights via email, provide them as downloadable PDFs, or offer embedded analytics with the visualizations and reports baked directly into a business application. In the latter case, you will need to manage the data and develop an embedded analytics tool to integrate your analytical capabilities into the app.

Internal Data Monetization

Internal Data Monetization

What Goals Can Be Achieved?

This data strategy focuses on generating valuable insights from data collected by a company. But before you can decide what data to gather, you need to define your goals since different data sets are suitable for addressing various challenges. Let’s learn what benefits a company can get from internal data analytics:

  • Reveal and automate repetitive tasks to eliminate human errors, streamline business processes, and boost productivity.
  • Improve decision-making using a 360-view of the company’s workflows, real-time updates, and prompts on the most effective next moves based on prescriptive analytics.
  • Save costs by identifying all kinds of inefficiencies and taking preventative measures with the help of predictive analytics before major problems arise.
  • Facilitate planning and optimize resource allocation with insights powered by historical data and smart predictions.
  • Enhance customer experience and strengthen customer loyalty by detecting and removing customer journey bottlenecks, anticipating customer demand, and improving customer support.
  • Maximize the outcomes of targeted marketing and advertising campaigns with better segmentation, well-rounded value propositions, and amplified engagement.
  • Reduce employee turnover rate and onboarding costs with better HR management.
  • Uncover security risks and remove insider threats to save your company from big losses by leveraging user and entity behavior analytics.
  • Get insights into product development and find ways to elevate your products and services.

Surely, these are not all the goals achievable with the right data at your fingertips, but the list gives you a good idea of the competitive advantages your company can arrive at. Any actions triggered by data analytics resulting in an increased revenue stream or reduced costs count toward indirect data monetization.

Data Monetization Tools for Internal Use

The range of solutions used by modern businesses to analyze their data is highly diverse, but the basic data monetization tool most organizations need is a data management system. You can’t jump to getting insights from your datasets without storing, managing, and distributing them properly, the more so that it’s not difficult to find an analytics platform with built-in visualization and other features suitable for your needs. Here are some categories of data management tools you may want to consider:

  • Behavioral data management. These solutions are designed to capture interactions between users and an organization, for example, by tracking how many users visit its website daily.
  • Customer data management. It focuses on customer-facing processes, such as customer service or sales, and can group clients based on demographics, preferences, and other criteria.
  • Data warehouses. These platforms provide storage for a company’s data and ensure data security and accessibility.
  • Product analytics. These tools are built to deliver data on the production aspects and can help companies improve processes and outputs for products and sales.
  • A BI platform transforms data sets into business insight and contributes to informed business decisions and strategic planning.

5 Steps to Get Started with Data Monetization

Although some steps on your path to direct and indirect data monetization will vary depending on the chosen method, there are some best practices applicable to every scenario:

  1. Consider the value of your assets. Data analytics comes at a cost: you may need to pay for a subscription with a BI platform or purchase a license for a data monetization tool, and even labor costs associated with collecting data inside the organization should be factored in. All these expenditures may exceed the value of the end product you bring to the market, or the outcomes of its internal use may not be worth the trouble.
  2. Evaluate your existing tech stack. It is quite likely that your current performance capabilities jar up against your audacious ideas, so you should revise the company’s technological capacity through the lens of the selected strategy. You may need to invest in upgrading your infrastructure to align with your goals, so learn what the cost is and how it goes first.
  3. Govern your data. Once you decide to enter the game, you will need to develop internal standards addressing data quality and establish policies governing data usage. By managing data availability, usability, integrity, and security, you will ensure that the insights from the information assets deliver the expected value to internal and external stakeholders.
  4. Provide accessibility, security, and compliance. Although you can safely use aggregated and anonymized data for direct and indirect monetization, you still need to check that your procedures and policies comply with all applicable domestic or international regulations. On the other hand, your data protection efforts shouldn’t obstruct access for authorized users, so you have to think through access requirements carefully.
  5. Build leadership and culture. Whether you engage in internal or external data monetization, you have to clearly communicate your strategy throughout your organizational structure, as it will require commitment from senior leaders and cooperation from different departments. Your teams should understand these efforts’ importance and take on relevant responsibilities assigned to every member.

Does Data Monetization Work?

According to the latest research, the data monetization market is expected to grow at a CAGR of almost 20% and exceed $7 billion by 2027 while being estimated at nearly $3 billion in 2022. It’s a good reason to follow in the footsteps of the companies already capitalizing on their data assets.

You need this guide, because it includes:

  • evolution of data stack
  • reasons why many well-known tools do not meet the challenges of the new world
  • vital structure of modern data stack
  • the newest tools for every part of modern data stack
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