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With the increasing amount of data that is streamed and processed daily, many companies have realized non-traditional and innovative ways to use this information, such as data monetization. Data monetization is the process where company-generated data is used to create a measurable economic benefit.

This can include selling data to third parties or using data internally to improve processes or realize new innovation opportunities. 

Companies practicing data monetization are realizing such benefits like cost reduction, revenue increases, and opportunities for new data-related services. Since the current benefits of data monetization far outweigh initial financial and time investments, there’s no better time for your business to learn how to develop a competitive advantage through effective data gathering and analysis.  

  • What is Data Monetization?
  • How Does Data Monetization Work?
  • Data Monetization Strategies
  • Data Monetization Examples
  • Data Monetization Platform
  • Data Monetization Trends
  • Data Monetization Companies
  • Data Monetization Mckinsey
  • Data Monetization Market Size
  • Data Monetization in Healthcare
  • Types of Data Monetization
  • Data Monetization Ideas
  • Big Data Monetization Strategy
  • Healthcare Data Monetization Examples
  • Telecom Data Monetization Examples
  • Mckinsey Data Monetization Automotive
  • Mckinsey Data Analyst Salary
  • Mckinsey and Company Data Engineer Salary
  • Mckinsey and Company Data Scientist Salary
  • Data Monetization Models
  • Data Monetization Pricing Strategies
  • Data Monetization in Banking
  • How to Monetize Data Insights
  • How do Companies Monetize Their Data?
  • How to Monetize Your Data Safety
  • Payment Data Monetization
  • How do you Make Money With Data Monetization?
  • How to Monetize AI
  • Mobile Data Monetization Strategies
  • How to Monetize User Data
  • Monetizing Data Science
  • Monetizing Data Assets
  • Data Monetization Value Chain
  • Data Monetization Consultant
  • Big Data Analytics Monetization
  • Logistics Data Monetization
  • Importance of Data Monetization
  • How Much Money can you Make Selling Data?

What is Data Monetization?

Data monetization is the process whereby company-generated data is used to create a measurable economic benefit. Businesses often experience advantages such as increased revenue or reduced expenses as a result of monetizing their data.

Read Also: What is Demand Generation in Marketing?

Companies can also use their data to construct slightly less tangible benefits, such as new partnerships or improved supplier terms, by sharing their data with third parties in a mutually beneficial arrangement.

In some cases, organizations realize their data has enough value to begin offering data services to a sufficient number of outside companies. Facebook and Google were pioneers of this trend; utilizing their free platforms to create enormous data assets to sell across the globe.  

How Does Data Monetization Work?

Data monetization is using your data to add to or increase your revenue stream. This isn’t a new concept, but it’s never been easier to do. So many modern data and analytics tools make it easy to “white label” an analytics solution or package your dataset as a viable product.

Data monetization can be achieved through both internal and external methods:

  • Internal data monetization is the method of using data and analytics to make informed business decisions that turn into measurable improvements to the way a company does business. By modernizing their data and analytics ecosystem, any organization can use their data to improve internal processes and practices, such as more targeted marketing campaigns, identifying upsell opportunities, and improving the customer experience—all of which maximize company profitability.
  • External data monetization is the method of creating a product or service using your internal data assets and selling them to a third party. This can be in the form of benchmarking or forecasting reports, survey data, one-off datasets, or any kind of consumer interaction data. As long as you have a data asset that you can quantify, you can monetize your data externally. For example, if your business collects consumer data, you can package up the insights and provide premium access to benchmarking data as well as guided analytics to your customers.

Data Monetization Strategies

Whether optimizing business performance or packaging insights for sale to third parties, an effective data monetization strategy should present the clearest path for extracting insights from big data. The strategy should define the need for establishing a solid IT foundation with a well-governed, centralized data store, advanced analytics, and various business intelligence (BI) tools.

Once the infrastructure is established to properly glean data-driven insights, any legal risks, data protection barriers, competitive barriers, data availability problems, and data delivery methods should be carefully considered. Improper data monetization techniques can lead to hefty fines, cybersecurity risks, and irreparable reputational damage.

The strategy should also clearly define the end state. This will involve a combination of physical, technical, and logistical conditions that transform extended conversion of datasets into new revenue streams. Change management must also be factored into the new strategy to ensure the entire enterprise adapts to the new business model.

Data Monetization Examples

To give you a better understanding of how data monetization works in a practical sense, it’s worth giving you a few implementation examples from companies within different industries. 

Data monetization in consumer goods 

AB InBev is the world’s largest brewing company. Throughout their growth, they have unsurprisingly acquired many other brands. Today, AB InBev comprises of a portfolio of over 500 global and regional beer brands spread across 100 countries.

Onboarding this number of brands has naturally presented a few challenges. At one point, AB InBev had 27 different Enterprise Resource Planning (ERP) systems and 20 different integration systems to try and tie all of the disparate systems together.

Naturally, AB InBev quickly realized the best way to collate all of the data in one place would be to create a cloud-based data hub. Using a centralized data hub, AB InBev is now able to make more accurate forecasts and reduce product time to market — allowing them to dominate their industry. They now produce the top three selling beers in the US market today, which can be attributed largely to their new data strategy. 

Data monetization in agribusiness

In a high-volume low-margin business, farmers need to have real-time variable information, such as field-level weather and commodity prices, at their fingertips. Digital Transmission Network, or DTN, has been providing those data points to the agricultural industry for over 30 years.

Much like AB InBev, DTN had invested in several different data systems to provide the information that large corporations such as John Deere, Monsanto, and Pioneer utilize daily.

However, DTN struggled with the continued investment it took to manage and maintain an increasing amount of complex applications across several different networks. This strategy limited future growth and product innovation.

As a result, DTN decided to create a cloud-based data tool containing a clear and consistent set of interfaces for each different data field. This eliminated the need to implement millions of costly point-to-point integrations and facilitated a much better user experience. 

Today, DTN’s integrated platform is quickly becoming the industry standard for agricultural business data sharing. They monetize their information through a combination of subscription fees and value-added services. 

Data Monetization Platform

A data monetization platform (also called data selling platform) is a software solution that helps companies to monetize their internal data assets. In contrast to data selling platforms that are more geared towards professional data sellers and their sales forces, a data monetization platform might also work well for companies that are just starting out and exploring monetizing their data.

While companies usually use those platforms in self-serve mode, some vendors offer additional data monetization services, basically putting data monetization on auto-pilot for their customers – that just receive a monthly report and paycheck for their monetized data.

If you are into video content for example, you can use platforms like:

  • Muvi
  • InPlayer
  • Dacast
  • Kaltura
  • Brightcove

Data Monetization Trends

Global data monetization market was valued at USD 2.78 billion in 2021, and it is expected to reach a value of USD 10.9 billion by 2028, at a CAGR of 21.54% over the forecast period (2022–2028).

The major factors credited for the growth of the market include the huge volume of data generated and decreased cost of data storage, as a result of technological advancements. Moreover, the rapid adoption of advanced analytics and visualization solutions is also driving market growth.

Large enterprises accounted for a market share of more than 70% in 2021, as they are continually adopting new and developed technologies, in order to get a larger market share and boost overall production and efficiency. Moreover, large corporations have large corporate networks and numerous revenue streams.

As a result, they generate a massive amount of data. Whereas, the SMEs category is projected to witness a higher CAGR, of 22.7%, during the forecast period, owing to the large numbers of startups that utilize data analytics for business decisions.

The North American data monetization market generated around 35% revenue share in 2021, due to the rise in the penetration as well as the adoption of data monetization services, software, and platforms, and the presence of a large number of data providers in the region.

In North America, the U.S. is a larger revenue contributor with a maximum number of data monetization vendors headquartered in the country. However, the Canadian market is expected to grow significantly at a CAGR of 13.9% during the forecast period.

This can be ascribed to the surging integration of digital technologies into organizational processes across industries and the increasing need for data monetization solutions to acquire actionable insights from massive volumes of data generated by digitized business operations every day.

On the other hand, the APAC market is projected to gain momentum at a CAGR of 22.3% during the forecast period. The increasing adoption of digital services, such as IoT, mobility, AI, cloud, and over-the-top services, and the rising investments in technological updates in the region are the major factors boosting the market growth.

China is the largest revenue generator in the regional market, holding a share of around 40% in 2021, and the country market is likely to project the fastest growth, advancing at a CAGR of 27.6%, during the forecast period.

This is attributed to the presence of a large number of MSMEs and major firms, as well as the constant digitization of business processes, and the increase in the volume of data generated every day. The growing enterprise rivalry is also pushing organizations to capitalize on the value of data, which, in turn, increases the adoption of data monetization solutions.

India is also one of the significant economies in the region, which is expected to capitalize on data monetization, owing to favorable government regulations. For instance, in February 2022, the national government published a policy ‘Draft India Data Accessibility & Use Policy 2022’ for all the data collected, generated, and stored by every government ministry and department, under which all the data will be open and shareable barring certain exceptions.

Data Monetization Companies

Companies such as Amazon, Facebook, and Google have monetized their data and used it to fuel the growth of trillion-dollar businesses. Inspired by their success, organizations across all industries are taking a closer look at their own data to uncover opportunities to create value.

If you are planning on monetizing your data, look into the following companies.

EVEN FinancialUSAPrivate915
Affirmed NetworksUSAPrivate795
Chalkline SportsUSAPrivate767
Teralytics AGSwitzerlandPrivate752
PwCUnited KingdomPrivate734
SkimlinksUnited KingdomPrivate729
Allot Communications Ltd.IsraelListed675

Data Monetization Mckinsey

Results from the newest McKinsey Global Survey on data and analytics indicate that an increasing share of companies is using data and analytics to generate growth. Data monetization, as a means of such growth, is still in its early days—though the results suggest that the fastest-growing companies (our high performers) are already ahead of their peers.

Respondents at these companies say they are thinking more critically than others about monetizing their data, as well as using data in a greater number of ways to create value for customers and the business. They are adding new services to existing offerings, developing new business models, and even directly selling data-based products or utilities.

Moreover, responses from the organizations that are seeing the most impact from their data-and-analytics programs offer lessons to others striving to make the most of their data. Those companies have, according to respondents, established a strong foundation for analytics in a few ways: clear data-and-analytics strategies, better organizational design, and talent-management practices, and a greater emphasis on turning new data-related insights into action.

Data Monetization Market Size

The global data monetization market size was valued at USD 1.30 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 24.1% from 2020 to 2027. Data monetization is the sharing of data between companies. It is the process of using data to generate revenue or create new revenue-generating streams.

Data monetization is of two types direct data monetization and indirect data monetization. Direct data monetization is the selling of raw data. In this type, companies generate revenue directly by selling the data. Direct data monetization involves selling a companies’ analysis, bartering or trading data, and creating one or many APIs.

In terms of indirect monetization, companies use their data to create a measurable impact. Indirect monetization helps companies reduce costs, improve productivity and efficiency, develop new products or services, and discover new customer types or business categories, among others.

Europe data monetization market size

Several factors, such as rising enterprise data volume, awareness towards data monetization, and external data sources, are anticipated to drive the market growth. Furthermore, growing usage of data processing and artificial intelligence, adoption of data-driven decision-making approaches, and advancements in big analytics, among others, is expected to fuel the growth over the forecast period.

Several companies are inclined toward data monetization since it provides data usage optimization, customer loyalty, reduced operating costs, improved compliance, boosts profitability, strengthens partnerships, and enhances customer experience and understanding.

Similarly, data monetization also adds maximum value to products and services, streamlines planning and decision-making activities, improves collaboration and data sharing amongst external and internal stakeholders, and increases targeted product/ service marketing and propositions. Such factors are anticipated to drive the market growth over the forecast period.

Data Monetization in Healthcare

Data monetization refers to the process of using data to obtain quantifiable economic benefit. Organizations can monetize their data by providing data access to third parties, commonly referred to as direct monetization, or by using analytics to derive insights from data to improve internal processes, products, and services, known as indirect monetization.

The overall data monetization market is poised to touch a valuation of $USD 707.86 billion by 2025, making it a valuable instrument for healthcare systems and related companies.

There are various ways that data can be used to generate revenue: to negotiate beneficial terms or conditions with business partners; for bartering information; selling data outright, either via a data broker or independently; or as information products and services, including information as a value-added component of an existing offering.

Let’s take a closer look at how data monetization, particularly direct monetization, could work in healthcare.

  • Data-oriented, personalized products as add-on services: A health insurer can provide wellness recommendations utilizing data from an individual’s personal health devices, in the form of a fee-based offering.
  • Data-as-a-Service: Share the data assets in their raw, native form. In this case, diagnostic labs might share de-identified diagnostic data as a paid offering to pharmaceutical research organizations, who could in turn use them to gain more insights into disease conditions and symptoms.

Types of Data Monetization

There are several methods for monetizing data, but the one organizations choose should give the agility and flexibility to extract the most value from big data sources. To help you decide which method works best with your data strategy, here are the four most popular data monetization strategies:


This data monetization strategy is the simplest to implement and typically operates on a direct business-to-customer (B2C) model. The data could be raw and unstructured, aggregated for a high-level overview, or anonymized when the source data contains personally identifiable information (PII). This is a form of direct data monetization.

This path also offers the lowest potential for revenue generation. Raw datasets still need to be analyzed to generate insight, and Data-as-a-Service only provides raw data. This means buyers get no value until they load and analyze data via analytics or BI software and tools.

If the selling party lacks the people power to analyze data before the sale, this is a good opportunity to generate revenue without increasing employee workloads since data can be provided largely as-is.


Where Data-as-a-Service delivers raw data for analysis by buyers, Insight-as-a-Service provides summarized analytical insights, such as competitive insights or customer behavior trends. The insight is generated from numerous sources, including internal datasets and external primary and secondary data sources.

Enterprises can sell these insights as a one-off report, or continuously through embedded analytics applications for ongoing revenue generation. This is another example of direct data monetization.

For companies using data monetization in this context, more work is required to generate insights and visualizations. This approach must also be aligned with prospective buyer requirements, meaning partial insights could generate no revenue at all.

Since analysis has already been performed, Insight-as-a-Service provides more value to buyers and warrants a higher price than Data-as-a-Service.


This approach will look similar to Insight-as-a-Service, as customers can access insights in return for payment. The difference here is the scope of data access and analytics functionality.

For instance, customers get real-time controlled access to analytics and BI visualization tools operated by the selling data provider. This data provider could be a research company with large-scale datasets on an industry. This is another direct data monetization strategy.

The benefit is zero setup and zero maintenance for the buyer, much like how cloud computing means enterprises do not need to manage server hardware. It’s functionally similar to an internal analytics environment, except that ownership is solely with the data provider.

As an all-in-one solution, Analytics-as-a-Service offers the most potential to generate revenue for data providers, but also carries the greatest IT management burden. Overprovisioning data services access could also lead to data breaches and leaked confidential information. Strict cybersecurity policies should be in place with this approach.

Data-Driven Business Models

A data-driven business model aims to leverage every available source of data in pursuit of efficiency and productivity. This could include sales, marketing, human resources, finance, or any other business department. This is an indirect data monetization method, serving to benefit the company by analyzing its own data.

For example, system logs and crash dump files are created when a server outage occurs. This data can be centralized and analyzed to identify repeating network problems and improve IT service desk productivity.

Another example would be if customer buying habits have changed, causing products to become overstocked. Sales metrics can be analyzed to visualize sales volumes over time and proactively identify trends to improve supply chain efficiency and optimize stock levels.

Extend this vision of full data utilization across the entire business, and you have a data-driven business model that relies on factual insights rather than the highest-paid-person’s-opinion (HiPPO).

To summarize, there are three main direct data monetization approaches: the selling of raw data, the sale of packaged insights generated from raw data, and enabling direct access to a data analytics platform owned by a third party.

Indirect data monetization, such as with a data-driven business model, enables companies to strategize and optimize their operations to reduce costs or increase revenues, thereby indirectly monetizing the data via insight-driven action. As the complexity of the approach increases, the potential for greater revenues grows in parallel with the complexity of IT and cybersecurity management.

Data Monetization Ideas

There are a few data monetization strategies to choose from that actually offer great opportunities, depending on the maturity of the organization.

Data as a service

The first one is the simplest of all business models listed below, called data syndication. Anonymous, aggregated data is being sold to intermediate companies or end customers. Then, companies and customers can operate on data mining.

Telecommunication operators provide geolocation data to allow city planners to design more effective traffic systems or smart city solutions. In retail, the gathered shopping data allows better personalization of discounts and offers and a deeper understanding of customers’ shopping habits and preferences. All to make sure your customers’ satisfaction level increases!

Insight as a service

The second idea for monetising data is combining internal and external data sources to provide valuable insights. In essence – the company doesn’t share the data as in the first model, only the insights derived from the data.

One chemical company created a decision-support model that enables ship operators to save funds on CO2 and fuel. Thanks to the mobile application, they can optimize their investments by analysing coating choices.

Analytics-enabled platform as a service

The third one is definitely more complex but also really valuable. This model takes the insights from the second model and makes them available in real time through a cloud-based platform. Partners of the platform can access it anytime, and if enriched with an API, they can even use it to create real-time-based triggers. 

GE’s platform Predix is responsible for developing energy management systems for lighting and energy. The company allows customers to make cost-reduction decisions by simplifying energy processes, leading to automation and operational efficiencies. All these are thanks to their prescriptive analysis around energy use, maintenance, and other outcomes.

Multiple industry platforms as a service

And finally, the most advanced model of monetising data is a multi-industry platform as a service we like to call the synergetic data model. Taking a step into the future, TASIL strives to connect different industries in terms of their data.

Matching various partners’ data sources in real-time would create an invaluable source of information that can be used for data monetization strategy (again, both internally and externally) and bring additional revenues far more prominent than the previous models.

Big Data Monetization Strategy

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Data monetization is the process of using data to increase revenue. The highest-performing and fastest-growing companies have adopted data monetization and made it an important part of their strategy.

By combining this data with traditional data from operational data systems and other more traditional sources it is possible to create new sources of value for an organisation; both internal value and value realised from creating data based products and services that other organisations or individuals might wish to pay for or subscribe to.

Some good examples are:
– Internet advertising data and the ability to better target ad’s to the right consumers.
– multi party leads via a telematics system (like Sprint’s Velocity)
– Experian’s Mosaic classification system and other similar products

Healthcare Data Monetization Examples

Healthcare companies can use various strategies to collect or acquire data. This can include both, organic and inorganic methods.

Collecting data from customers.

Using this strategy, a company can understand their customers, take decisions related to marketing, store locations, and create hyper personalized solutions. Flatiron has developed its own oncology based EMR platform by collecting data of 2 million patient records as of 2018. Lifesciences organizations use these datasets for various use cases in areas such as R&D, and clinical trials.

Partnering or purchasing data.

John Deere in partnership with Cornell University has created a data platform (Ag-Analytics, data platform that syncs with John Deere’s operations center to access and analyze farm data) which has become a source of revenue and has created significant value for their farmer ecosystem. Using this platform John Deere performs analytics of the data and farmers use these tools for estimation, forecasting, risk management of crop maintenance, and soil health.

Acquire a company.

Companies that are not able to process the data or not able to get the data required from any partnerships, could go for acquiring a company. Kroger has acquired a data analytics firm 84.51, which helps its biggest suppliers to understand the behavior of customers (60 million households) that shop at Kroger. This helps the supplier design better solutions and services.

Building an ecosystem.

A company that possesses a significant amount of proprietary data and can buy or partner for additional data may be able to orchestrate an ecosystem that other companies participate in. Goldman Sachs in 2012 acquired a credit-reporting firm TransUnion and converted it into a data mining giant in just 3 years. TransUnion now has a large base of data sets, it continuously analyses those data and sells it to insurers and lenders.

For these strategies to create full impact, companies need to build a data-first culture. This can be done by investing in skills specific to using analytical insights. They should also run change management programs to create new mindsets and ways of working, and break silos by making cross-functional teams to share data, and create new roles and governance process.

Telecom Data Monetization Examples

Big data monetisation in telecoms has been an area of activity for the last few years. However, telcos’ interest levels have varied over time due to the complexity of delivering and selling such a diverse range of products, as well as highly variable revenue opportunities depending on the vertical.

Provision of insight to entertainment/sporting venues is a relatively common use case today that uses customer movement insight and sensor data. There is also opportunity for analytics such as customer segmentation and behaviour. Projects telcos have reported participating in tend to include a significant consulting component, so this is best suited to operators with a consultancy team.

Other opportunities around content consumption patterns are more difficult for telcos. Telcos may well have insight from their set-top boxes and other platforms that will be of interest to content providers, but it is a mature market which is used to ingesting different types of data and it does not seem a popular use case.

We explore the main data monetization models and use cases across 4 verticals.

1. Agriculture

Most activity in the agriculture sector is seen from large multi-national telcos with mature IoT propositions. Not all the biggest telcos report pursuing such projects, though, with case studies most likely from those with a strong presence in developing markets, or with large multinational enterprise customers present in developing markets.

Most opportunities are related to IoT and sensors and include a mix of connectivity services with storage and analytics of the payload data. For more complex and specialist the use cases, telcos are much more likely to play a connectivity only role. For example, in crop management, NTT Docomo offers hardware and analytics, but many other telcos instead choose to work with specialist platform vendors.

2. Manufacturing

Much of the discussion about future telco activity in this vertical is linked to the provision of 5G services to allow Industry 4.0 capabilities. The most visible telco manufacturing solutions are often linked to historic associations with a particular industry; for example, Vodafone and T-Systems solutions within the automotive industry.

Barriers for telcos to overcome include rolling out 5G capabilities fast enough to satisfy manufacturers and enable the swap out of LTE, creation of flexibility in their offerings and ease of access through on demand provisioning etc.

The table suggests that there is very little financial value for data/analytics in the vertical, but this is linked to the prevalence of IoT use cases where data analytics will not be sold as a separate service.

It is likely that telcos which choose to focus aggressively on 5G and edge computing for manufacturing are most likely to take advantage of the data/analytics opportunities – predictive maintenance and the provision of analytics for autonomous vehicles on the factory floor look most promising.

Some of the solutions where telcos are most active in manufacturing, such as asset management, supply chain analytics and transportation/logistics solutions, are also provided to other verticals. These are therefore captured in the section considering horizontal solutions for all verticals.

3. Retail

Historically, this was one of the first verticals targeted by telcos with customer movement inside products. Developing the products was often hampered by the difficulties of finding the right person in a retail organisation and the likelihood of non-standard requirements from every retail customer. However, among larger telcos with ambitions in data/analytics there is now a reasonably mature retail product set.

Ongoing opportunities divide into three categories:

  • Customer movement insight products: These tend to be the most feasible project as they are more mature and use telco data, for example for store placement calculations.
  • Customer insight products: Related projects use customer insight (demographic, sociographic) rather than geolocation data. For example, the open data platform described above could be accessed by retailers, hoteliers or other types of customer in this vertical.
  • IoT/small cell opportunities: There are additional data/analytics opportunities which use small cell, video and CCTV data to track customers in small spaces or within a shopping mall – however, these are considered of lower feasibility because they require rollouts of these capabilities and potentially IoT related products such as sensors. These opportunities subdivide between those that require specialist analytics and those that require additional AI capabilities such as facial recognition. All of these use cases require a sustained focus on the retail sector and its needs, plus enough rollouts of small cells, wifi, beacons etc to make a business case for adding data/analytics on top.

4. Transportation

Like other verticals, most of the most accessible financial opportunity is from customer movement insight provided to passenger transport companies such as trains and buses. This is a reasonably mature use case for telcos.

Much of the rest of the opportunity is related to mature fleet management markets where there are limited opportunities for adding data/analytics. Lastly the connected vehicle market provides various potentially feasible opportunities to add data/analytics to IoT deployments.

Mckinsey Data Monetization Automotive

Automotive data monetization offers value in the form of cost optimization and improved customer experience. The ecosystem around data is well established in technology/telecom industries which will influence adoption in automotive as industries converge.

Several factors contribute to the growing amounts of available car data. An increasing number of sensors—present in vehicles and integrated into mobility infrastructure—allow the capture of information on nearly every way a driver uses a car, how that car functions (or malfunctions), and everywhere it goes.

Organizations that leverage this connected technology to develop new, in-vehicle experiences for drivers and passengers could create a significant competitive advantage. This market comprises more than 30 car data-enabled use cases, focusing on providing customers new features and services ranging from connected infotainment to remote vehicle diagnostics, from emergency breakdown automated calling to tailored advertising in the car.

For industry players, three value-creation models underlie these use cases: revenue generation, cost reduction, and enhancement of safety and security.

Mckinsey Data Analyst Salary

The average McKinsey & Company Data Analyst earns an estimated $73,940 annually, which includes an estimated base salary of $69,065 with a $4,875 bonus. McKinsey & Company’s Data Analyst compensation is $3,845 less than the US average for a Data Analyst. Data Analyst salaries at McKinsey & Company can range from $45,000 – $95,000.

The Marketing Department at McKinsey & Company earns $1,253 more on average than the Legal Department.

Mckinsey and Company Data Engineer Salary

The average McKinsey & Company Big Data Engineer in San Francisco earns an estimated $112,629 annually, which includes an estimated base salary of $97,915 with a $14,714 bonus. McKinsey & Company’s Big Data Engineer compensation is $34,845 more than the US average for a Big Data Engineer. Big Data Engineer salaries at McKinsey & Company in San Francisco can range from $61,000 – $187,000.

In San Francisco, The Marketing Department at McKinsey & Company earns $3,189 more on average than the HR Department.

Mckinsey and Company Data Scientist Salary

The average McKinsey & Company Data Scientist earns $150,000 annually, which includes a base salary of $120,000 with a $30,000 bonus. This total compensation is $20,792 more than the US average for a Data Scientist. Data Scientist salaries at McKinsey & Company can range from $120,000 – $180,000.

The Engineering Department at McKinsey & Company earns $3,492 more on average than the Customer Support Department. Comparably data has a total of 2 salary records from McKinsey & Company Data Scientists.

Data Monetization Models

Let’s look at four ways to monetize data and improve the revenue model.

1. Internal Data Marketplace

Imagine the scenario: A new project is about to start in your organization. To fulfill the end goals, you need good data available. Where do you go? You reach out to your data engineering or business intelligence teams. You reach out to the application operations team if source data is required to get extracts.

Activities such as these have a cost associated with them. You would be paying a fee to an internal or external operations team. Or you would be burdening an already overwhelmed engineering team.

The creation of an internal data marketplace solves this problem. It is a method of shopping for the data you need in an internal portal with a clear articulation of:

  • What is the data about?
  • What is its intended purpose?
  • What are its limitations and risks?
  • Who are the key subject matter experts of this data?

This information allows the new project team to check in/out the data.

The project team incurs a fee from their capital expenditure budget for this convenienceThis fee is used to fund the running of the data marketplace. The key here is not to profit from internal teams; it is to change the organization’s culture. People start getting used to the idea of self-serving this data.

2. External Data Marketplace

This step will be easier to overcome if data is already monetized internally. The internal data marketplace will help iron out early challenges such as bad data quality. Sharing data externally will also have regulatory implications.

All regulations must be followed; the data needs to be anonymized and aggregated. It can then be offered to other businesses that would benefit from the information. The revenue model could be based on perpetual licenses of rarely moving data or a subscription model for fast-moving data.

Royal Mail has done a great job of this by monetizing their PAF (Postcode Address File). Each organization is collecting enough data in this day and age to make it monetizable. This can be classed as a data-as-a-service model.

3. Products and Services

Think outside the box – the data you are collecting and storing may only be worth thousands to you. It may be worth millions to someone else. How can you productize this information? WorldPay did an excellent job nearly 10 years ago when they published this research paper.

WorldPay provides payment facilities to major retailers. They capture millions of transactions and their data points through the payment machines. Using this information, they can show the retailers which segments of customers are the best to target and at which time of year. This information helps the retailers take tangible actions ahead of the next sale/shopping event.

Although it seems like WorldPay conducted this research for free, they could have monetized it by charging the retailers for the insight. So, can you create an “insights as a service” model using your data for other businesses?

4. Personalized Services

The top three items on this list are best monetized with business customers. What about direct customers (i.e., B2C)? It is hard to sell raw data to a consumer as they don’t care about it as a business would. You must understand what consumers care about and enable it using your data, whether that’s personalized information or insights for their service.

Personalizing services to your customer will build loyalty, as people value the personal touch in customer service. Starbucks has cracked this code with a fairly successful 25 million member-strong loyalty program. The key to this is personalization.

Data Monetization Pricing Strategies

Here are some factors that determine the commercial value of a dataset:

Data Edge

Your customers will look to gain an edge from your data. It needs to be either faster or more accurate than what they are using, or it must offer a unique insight previously unavailable to them. This seems simple and obvious but one thing that often gets overlooked is just how accurate Wall Street’s estimates actually are.

These estimates come from analysts who have been honing their craft for decades. It’s not sufficient to make an accurate prediction; you must do it faster and better than Wall Street can.

Monetization Strategy

Customers should be able to convert the edge offered by your data into trading or investment profits, via a clear and straightforward monetization strategy. The more direct the connection between your data and a profitable trading strategy, the more valuable your data.

Deep Market

The best monetization opportunities are found in large, liquid markets. Data that predicts the behavior of small or illiquid securities is inherently less valuable. For example, your data may be predictive of penny stock movements. But very few Wall Street professionals will be interested in trading penny stocks, which means your data would not likely find a hedge fund audience.

Uniqueness and Replicability

The more unique your data, the more valuable it can be. Are there others who can replicate either the data you have or the signal you are likely to produce? Are other versions of your data available? Are there proxies that achieve the same purpose? If the answer to all these questions is no, then you are likely to have a very valuable data asset.

Exclusive Access

If everyone in the market has access to a given dataset, it won’t have much value. For this reason, it’s important to restrict access to a few select customers. Exclusive distribution partnerships help create a sense of value for clients.

Table Stakes Potential

New and alternative datasets typically begin life as exclusive assets available only to a select few, at a very high price. Gradually, knowledge of the data diffuses through the market and its value diminishes.

However, a few datasets survive and become “table stakes”: they are no longer exclusive but become required purchasing by everyone in the market. At that point, participants who don’t have these datasets are at a disadvantage. The most valuable datasets are those that have the potential to become “table stakes”.

Assets Under Management (AUM)

The single strongest indicator of how much a client will pay for a dataset is how big the client is. This is not just because larger clients can afford to pay more (though that is certainly the case); it is also because larger clients can derive more profit from the same data.

Assume a given alternative dataset is expected to generate 1% in “excess” returns for a client. That dataset would be worth more to a hedge fund managing $5 billion (where 1% = $50 million) than it would be to a fund managing just $100 million (where 1% = $1 million).

Note that we are talking here about “excess” returns: these are the returns that accrue from using the alternative data, above and beyond the returns that the manager would have made without that data.

Return on Data Investment

Given the risks and uncertainty involved in all investing, hedge funds will expect a 10× to 20× return on a data investment. So, if a fund expects to make $1 to $2 million in excess trading profits by using your data, they should be willing to pay you $100,000 for that data.

Of course, the same dataset can be sold multiple times, to multiple funds, at no extra cost to you. (The cost of duplicating a data asset is zero). However, this imperative must be balanced against the need to keep your data closely held and hence expensive.


One way to maximize income while remaining narrow is to build a variety of data products from the original raw data asset. You can slice it and dice it differently, depending on the profile of the firms you are targeting: fundamental versus quantitative, small versus large, hedge fund versus investment bank, fast versus slow access and so on. This allows you to sell essentially the same data product multiple times without diminishing its alpha.


In some cases, a potential client may ask you for exclusivity — i.e., you can only sell your data to them and no one else. This is a fascinating quirk of the hedge fund world — the fewer the people who have access to a dataset, the more an individual firm can profit from it. Consider offers of exclusivity if the bid is near your expected total revenue from selling the dataset to multiple customers.

The price you charge for your data asset is one of the most important business decisions that will determine the success of your new data venture. Price your data product correctly and you may well be creating the foundation for a lucrative new revenue stream.

Data Monetization in Banking

Data monetization is the process of obtaining a direct or indirect economic benefit from large pools of data. Organizations can either sell or grant access to their large pools of data to third parties in exchange of monetary or some other economic benefit. On the other hand, they can use this data for their own objectives such as improving their customer experience or internal processes.

Financial service providers -develop data from two points; internally (this can be referred to as zero- or first-party data), and from external sources (second- or third-party data). Internally generated data is developed from questionnaires, polls or surveys that the financial institution may conduct with its customers.

For example, when logging out of a banking platform they may ask you to rate your experience. Additionally, this internal data can be developed when they bank analyses the behavior of its clients. For example the bank may analyze how their clients spend their money and how often they receive payments into their accounts in order to develop useful insights.

Externally generated data tends to come from other parties outside the financial institution, and the company does not have a direct relationship with the people whose data they are viewing. These external parties can also be using this data for their own internal processes for example retail companies.

One example of third-party data sharing is here in East Africa where we have NCBA bank entering into a partnership with a telco, Safaricom to offer credit and savings options to Safaricom’s customers via Mshwari. In order to analyze the risk profile of these customers, NCBA bank gets access to their mobile money spending and consumption habits and is then able to extend credit to them without these customers having a direct relationship with NCBA.

Additionally, a financial services firm can get access to this data from a third-party data aggregator who collects data from different sources. Financial institutions primarily use externally generated data to compliment their internal data and develop a wholistic view of their target markets.

For financial institutions monetizing their data is a clear formula for developing stronger relationships with their customers.

How to Monetize Data Insights

“It can be difficult to determine which data sets offer the most value to customers,” cautions CITO Research. To help determine which will prove to be the most lucrative, the following is a recommended step-by-step approach to data monetization based on serving the needs of customers while creating additional revenue streams.

Step 1: Perform business intelligence internally

Look at the usage of your systems and products. Create data visualizations for internal use so that you can analyze who’s using most of your products and services. Determine anomalies and how you should change things based on what you are learning.

Step 2: Share visibility with your clients and partners

Give customers access to your data. The next logical step, after analyzing customer performance internally, is to roll out to your customers (i.e. client companies). By embedding dashboards and visualizations into your product or application you can provide customers with a access to a basic level of analytics, free of charge.

This provides them with visibility into their own usage and helps spark the beginning of data monetization. For example, e-commerce companies can provide partners and suppliers with data on how they are performing, what their consumers are searching for, purchasing, and so on, and then adjust their operations accordingly.

Step 3: Add self-service or extensibility

You may not realize how valuable your data is until you see others using it. Based on your free offerings, customers will begin to ask for new views, new angles, and will likely ask you for white labeling and on-brand customization in order to provide a seamless experience for their own users or stakeholders. Your answer is “Yes, for a fee.”

What is more, this is where self-service analytics becomes an invaluable solution. By providing your customers (no matter their analytics know-how) with the ability to create and customize their own dashboards, you increase analytics adoption, allowing end-users to make more informed decision with has a positive effect on their business and yours.

Step 4: Look at the information you can aggregate

You have benchmarking data that customers want — information on how they are performing compared with others. With no other objective way to gain this type of information, the aggregate data you can provide becomes extremely valuable to customers and partners. And this is where a tiered approach comes into play.

With the right data analytics solution, you can provide your customers with a paid, premium level of analytics within your product with access to more information, richer insights and the aforementioned customizations.

Step 5: Find ways to personalize the data

The more specific and personalized you can make the analytics you deliver, the better. Consider mixing in external data sources like geodata, address enhancement, machine, weather, demographic, or business data to enrich the data you already have. And of course, with self-service you can give your customers the ability to customize the experience themselves.

Step 6: Keep listening to your customers

As your customers request new types of analytics, such as churn analysis, internal activity reports, or longer views of historical information, you’ll get ideas for additional data products.

How do Companies Monetize Their Data?

It’s not enough to simply have data. The value of data comes from the insights it creates, the processes it optimizes and its ability to enable better decision making. The reality is, despite data and analytics hype and expectations, most organizations are not successfully monetizing their data. 

If you have being wondering how you can benefit from the data available to you, the 3 tips below will provide a clear path to help you achieve your goal.

Use data to optimize the business

Organizations that realize the promise of analytics and BI platforms and act to optimize them across the business will find true value and recognize opportunities that were not previously apparent. 

Dow Chemical created value by optimizing business processes. First the company identified which teams were using which parts of the BI for what purposes. If that team was gaining a lot of value from an underutilized solution, they were asked to share their wins and stories with other parts of the business.

If there was a part of the business looking for a particular solution, the team guided them to the most effective option. The constant feedback loop and iterative solutions enabled substantial revenue growth. 

Use data to address business challenges

One of the biggest challenges with data is that it can exist in far-flung siloes and fragments. Different business groups have individualized set-ups and collect their own data for their goals, but companies often lack a cohesive overarching narrative. This makes it difficult to use the data for anything in the real world. 

This was exactly the issue at Turku City Data, a Nordic AI platform provider, which found itself unable to bridge the gap between data and real-world problem-solving. The organization’s solution was a flexible graph analytics framework.

This meant data from across the business was organized at a level of abstraction such that every data point represented a person, object, location or event. Turku City Data used this easy-to-understand frame as a common language to express and explore business problems in their contextual and structural richness.  

Use data to gather better data

A common mistake that organizations make when it comes to monetizing data is looking only at readily available existing data for opportunities. It’s an understandable mistake for organizations that have been led to believe that data itself is inherently valuable.

However, global technology company ZF Group decided that a counterintuitive approach might make more sense. Instead of looking at data they already had, the organization selected markets to target and took a close look at what type of data would create value for that market. 

Leaders realized that the data the organization already had — and indeed that most organizations have ― offered limited value, as it is often about common subjects and optimized for internal usage. Data monetization requires unique data that organizations don’t already possess.

According to the company, they typically have only 80% of the data they need to create a new product, and the challenge is where to find the remaining 20% that makes the product really valuable. For example, the organization sells IoT-sensor-enabled ball joints that generate data that is used to train predictive maintenance algorithms.

The organization then sells consumer-friendly analytics and visualizations to enable predictive maintenance programs. This means that the organization is constantly looking for new opportunities to create data that may not even exist yet, which in turn makes that data valuable to others.

How to Monetize Your Data Safety

Whenever you are trying to monetize your customer data, you have 2 options. You can either go for direct or indirect monetization depending on your business when it’s required to monetize your data. 

Direct monetization:

Refers to the process of directly converting your data into revenue for your business.

You can monetize directly by selling your products in your website or app, by selling your data segments or through your own PPC ads as well. Direct monetization means monetizing directly from your own sources.

Indirect monetization:

Refers to the process of indirectly monetizing by sharing the derived data insights, publishing ads of partnered brands, and so on. Here, you’ll indirectly monetize your data. Indirect monetization helps you profit through indirect sources.

However, low-quality data is a matter of concern in data monetization efforts. It is essential to process the data gathered from users before monetizing it.

Otherwise, the result may not be as expected by you. For in-depth understanding, let us dig into different types and sources of data.

If you plan to sell customer data, Data market is where you can sell or buy the customer data. Some prefer a centralized data market while some go for decentralized data market.

Centralized Market:

Customer data is stored and marketed under one centralized organisation. Here the buyers and sellers trade under one centralized system.

Decentralized Market: 

The marketers form different networks to create their marketplace without any centralized system. The data market is where you can monetize your data to accomplish significant economic growth for your business.

No doubt, you have a higher opportunity to revenue through customer data monetization, but there are some risks as well. Since your customer data is a revenue-generating asset, it is continuously under threat. So, you should give utmost importance to data security.

Payment Data Monetization

With the global digital payment market valued at nearly $69 billion in 2021, payment data monetisation is critical for enterprises to leverage as they face margin pressure and customer expectations head-on. 

Today, the most successful financial institutions are working less like monolithic legacy enterprises of previous decades and more like agile, innovative technology enterprises, evolving and pivoting swiftly to meet global and consumer demands. With data at the core being utilised effectively, new opportunities can be leveraged to create new revenue streams, building greater loyalty and creating efficiencies.

When considering the evolution of payments, the last 40 years have gone from heavy, costly infrastructures in mainframe services and fixed field messages in the 1980s to the relational world in the 1990s where each bank had its own schema and concept, disconnected from the rest, eventually leading us to today with readable, flexible and dynamic capabilities with JSON ISO20022 as the new standard.

To compete in this  landscape of digital and real-time payments, data must be leveraged and investments must be made in existing services to deliver innovation. For this reason, supporting payment data monetisation initiatives through infrastructure investments is a top priority.  

Making the Right Investment

Choice overload can lead consumers to be overwhelmed or dissatisfied. With this in mind, financial institutions need to make strategic decisions about existing services, and the abundance of options can be overwhelming. Investments need to be prioritised based on needs, services, and consumers, as well as considering the costs, people, and resources needed to mount and maintain these services, all while maintaining the endpoint of innovation in sight. 

Payment data monetisation means investing in the right services. But payment data includes everything from transaction records to data contained within messages and monetising it can be relevant for a number of use cases including using payments data to improve internal operations, identifying clerical errors and optimising procurement processes.

On the customer side it can also impact the straight-through-processing rate, which is the percentage of transactions that are passed through the system without manual intervention, as well as incentivising customers to make payments at different times to optimise the liquidity position of the bank, enhancing corporate-facing services and tracking payments and forecasting cash balances far more accurately. 

All of this can only be made possible by unlocking the power of data by investing in the big three: data infrastructure, the cloud, and single view capabilities. In a recent survey of top bank executives, their most sought-after services are rooted in the big three, and include:

  • Consolidated real-time data from multiple banks in a single dashboard
  • Real-time cash forecasting
  • Better security and fraud protection
  • Real-time cash balances

Data infrastructure, cloud technologies, and single view capabilities build a foundation for payment innovation and leadership in the financial services sector, which can then lead to adding on the right existing services. Ultimately though, a foundation built on agile infrastructure is needed to compete with modern, digital institutions. 

Cloud Technology

Cloud-based, digital platforms are integral to modernisation. However, having any infrastructure in the cloud does not equal innovation or agility. An indicator of possible future performance and success issues is not having a proper cloud migration and storage strategy today. As one of the most ancient industries, the banking sector is unsurprisingly attached to monolithic legacy applications.

To differentiate themselves, they must leverage modern data architectures with greater agility. This can seem intimidating as it requires migrating thousands of applications built over the last few decades or changing complex organisational requirements. The stories of cloud failures are also out there, but “data monetisation is a strategy, not a product.”

There must be a paradigm shift, where banks communicate a business strategy, and use the cloud not as the business strategy but as a tool to facilitate their business strategy, and make the switch from thinking in applications to thinking in platforms, in order to move past simple digitalisation. Institutions can then unlock long-term gains by combining data assets and integrating payments data into an enterprise-wide data platform strategy. 

How do you Make Money With Data Monetization?

The two most obvious ways for companies to commercialize data are:

  1. Data is collected and analyzed for product development purposes, used to create better products, which are then to customers. This results in increased sales, products with higher added value or more closed deals.
  2. Data is used to identify problems and bottlenecks in internal processes, which are then eliminated to improve business efficiency and profitability.

Following are four inspirational ways for companies to make money with their data, with real-life cases to illustrate how they work in practice.

1. Selling insights to customers

Taking existing data, aggregating and enriching, and then selling it to customers as new valuable insights makes sense. Reports, online dashboards and indexes can be standalone products bundled with the company’s existing offering, helping increase the price of the bundle that is sold to customers.

User interfaces can be augmented with machine learning applications to help customers get what they need or interact with the brand. New insights are created “on-the-go”, as a part of the customer encounter.

2. Empowering the sales force with data

A sales organization’s role in a company is to maximize sales. Data can be a highly effective tool in reaching that target. Smart companies empower their sales force with rich customer data sets that help them easily identify customer problems, potentially churning customers and sales leads.

With data, the sales force can give better product presentations, improve customer service and use more tailored sales argumentation when meeting customers. Smart companies position themselves as outstanding data leaders, and sales people play an important role in delivering that message to customers.

3. Using data in marketing and advertising

Data that tells us something about consumers and their interests can be used to create marketing and advertising solutions. You have two options: either the company uses its data to optimize its own marketing and advertising or sells its data to other companies, so they can do it.

4. Selling data to players up and down the industry value chain

Companies often view their business as a “silo” and the data they have, they’ve derived from their own operations and own customer interactions. They use it for their own purposes only. The reality is that they’ve been operating in network environments and value chains where the final customer delivery is the result of the joint effort of several collaborative companies.

In recent years many companies have woken up to the fact that these networked business environments create opportunities for sharing and capitalizing data from company to company. Data can be an important asset in optimizing the operations and cooperation of the players in the value chain.

Companies can monetize their data by selling it to their suppliers and vendors down the value chain or by selling it to retailers, resellers and other sales-related partners up the value chain – or both.

How to Monetize AI

In simple terms, there are two main ways you can monetize AI. You can do so indirectly, by making AI part of your products or services, or directly, by selling AI capabilities to customers who in turn apply it to solve particular problems or build their own AI-enhanced offerings.

Indirect monetization

With indirect monetization, built-in AI capabilities contribute to an offering’s overall value, but are not the sole source of that value. Take the recommendation engines used by Netflix and Amazon, which make use of advanced machine learning technologies and algorithms. While customers appreciate the recommendations AI generates, they’re just one of many factors that motivate customers to subscribe to these services.

Even when AI is the main draw, as with the Nest Learning Thermostat or self-driving cars, those AI smarts, while key, are still just part of the package. One thing’s for sure, however. Whether you package products or services, incorporating AI into them has tremendous potential to drive monetization by making whatever you sell more useful and enticing for your customers.

So how can you add AI to your offers? Companies with deep pockets can emulate Google, Apple, Facebook, carmakers, and telecoms and invest millions hiring teams of engineers, launching skunkworks, and buying up AI startups. But what if yours is among the 99 percent of companies that don’t have those options? Fortunately, there’s a more affordable route.

Direct monetization: AI as a Service

“AI for everyone.” That’s how Salesforce describes its new AI service, Einstein. And it perfectly captures the essence behind the growing field of AI-as-a-Service (AIaaS). As with many on-demand services, it enables practically any company to acquire highly sophisticated capabilities with minimal investment, paid for incrementally through subscriptions or usage-based mechanisms.

Among AIaaS providers, one of the best known is IBM and its Watson platform. Watson understands human speech and can find answers to extremely complex problems in seconds. Watson first made headlines back in 2011 when it beat reigning champs at Jeopardy. Since then, the platform has been commercially applied to a widening range of AI-for-hire tasks.

For example, it’s helped doctors at Sloan Kettering Cancer Research Center and the Cleveland Clinic make better decisions about patient treatment from hundreds of thousands of variables. H&R Block is using Watson to create virtual assistants smart enough to complete income tax forms.

AIaaS is a broad category that encompasses many sub-disciplines within AI. Not surprisingly, a number of them, such as machine learning, are now also available for hire. Amazon Machine Learning is a prime example. According to a recent forecast, revenues from Machine Learning as a Service (MLaaS) are expected to hit $20B a year by 2025.

Mobile Data Monetization Strategies

or publishers, apps serve two key purposes: to get your content in front of more people and to bring in revenue. Most publishers have that first part down – it’s the app monetization strategy that’s the tricky part.

Your app can bring in revenue in dozens of ways, but which of these app monetization strategies best fits your content, your audience, and your needs? Different apps may need different app monetization options. A gaming app isn’t the same as a news app so you’ll want your mobile app development strategy planned accordingly.

Discovering the right combination is one part trial and error and one part research. And keep in mind that it is vital to work with an app developer or team of developers to build your mobile app from the ground up with your app monetization strategy in mind.

These eight strategic mobile app monetization options can help maximize your app revenue:

  • Monetizing Your App with a Download Fee
  • App Subscription Model to Bring in Consistent Revenue
  • Flexible App Monetization with ‘Freemium’
  • In-App Ads to Leverage Your User Base
  • Affiliate Marketing for Your App
  • Monetizing Through In-App Purchases
  • App Data Monetization
  • App Transaction Fees
  • Partner with Expert App Monetization Strategists

How to Monetize User Data

We all know customer data is very valuable, but what we do with it is a different story.

Most companies use customer insights as way to achieve a successful CRM or customer experience program. Data is used to enhance customer experiences, improve service quality, target marketing efforts, capture customer sentiment, increase upsell opportunities and trigger product and service innovation.

According to Olive Huang, research director at Gartner, direct monetization of customer data, such as organizations exchanging information for goods and services, is as old as the grocery store loyalty card. It’s the increasing magnitude – greater volume, velocity and variety – of customer data that now presents organizations with new opportunities for monetization.

“Customer information has always been central to any CRM strategy, but the growing wealth of information from digital channels — from social media, location and context-sensitive data collected from mobile, and the Internet of Things (IoT) — radically expands the scope of the 360-degree customer profile,” said Ms. Huang.

Customer data can be used in two ways to generate monetary value:

  • Directly — sold or traded.
  • Indirectly — create new information products or services that leverage the data, although the data itself may not be sold.

A large number of monetization opportunities are created using actionable customer insights from big data, especially in the consumer packaged goods and retail sectors. Through its Retail Link trading partner portal, Walmart gives suppliers its entire sell-through data — almost in real time, and by store.

Companies create additional services based on the customer data they collect. For example, Alibaba offers targeted personal finance products to customers that are active on its digital commerce sites.

Monetizing Data Science

Blog publishing

Aspiring data scientists must have a Bachelor’s or Master’s degree in any technical domain from a reputed university. The curriculum is set for students to gain a clear and strong understanding of data science and its subsets. The bonus includes completing online certificate courses on data science from reputed professional websites such as Simplilearn, Udemy, Coursera, Linked In, and many more for gaining more data science skills.

One of the top ways to monetize data science skills is to start publishing blogs and articles on top blogging sites on the internet. There are two paths to continue to utilize data science skills— creating your own blog and writing on different websites with good payment. It will take time to monetize the blogging site. But, after reaching a certain threshold, the data scientist can start earning profit for every blog.  


A data scientist can start freelancing to monetize data science skills efficiently and effectively through the power of the internet. The outbreak of the coronavirus pandemic has created an impact on mental health, especially on the professional career path. Freelancing is one of the top ways to monetize data science skills as a data scientist in 2021.

There are multiple websites that provide sufficient and high-quality work for different professions with good payments, sometimes international payments. One has to seek a perfect client for effective data management for adding value to the CV for better purposes in the data-driven future.  

Disclosing models with APIs

A data scientist can create a machine learning model for effective data management to help the target audience in this data-driven market. It is now easier to disclose a model with API platforms that provide a good and user-friendly marketplace while monetizing APIs. There are other options like disclosing models for collaborations with start-ups and developers in the market. This is one of the best ways to earn profit from data science in 2021.  

Participations in Kaggle competitions

Active participation in Kaggle competitions, as well as global data science competitions, help data scientists to improve data science skills as well as earn good rewards. This will help to add some value to the CV of a data scientist to show communication skills, technical skills, as well as other data science skills. Recruiters consider an active Kaggle profile as an added advantage in the recruitment process for a professional data scientist.

Thus, it is one of the top ways to earn profit from data science— monetary rewards as well as recognition. A data scientist can join different data science communities to receive constant notifications regarding multiple competitions within a year.  

Creating a YouTube channel

YouTube is one of the major platforms to monetize data science skills to earn profit from data science efficiently and effectively. A data scientist can create multiple interactive videos of the data science processes for effective data management, doubts, and other interesting and unique content regarding data science.

Students who are highly interested in this field search for good quality educational videos for a better understanding. Thus, one can monetize data science skills through YouTube and create a brand and influencer network. One can link the YouTube videos with other social media profiles on LinkedIn, Facebook, and Twitter through social media marketing strategies.

Monetizing Data Assets

Many well-known use case studies on the value of data like Netflix, Amazon, and Google exist. What we see in the industry is a classic chicken-and-egg problem between the business and technology departments.  Many businesses wait for the technology teams to demonstrate the art of the possible.

Yet, the business doesn’t want to invest in data re-platforming initiatives that enable data monetization without a strong business case, leaving the business with data siloes and a lot of dark data.

We are already starting to see a number of companies separate themselves from their competition, by strategically investing in data re-platforming. In doing so they are creating long-term data assets to enable data monetization capabilities. With initiatives such as these, companies are priming their data sets for re-use.

By following a well-defined enterprise data taxonomy, different data owners within the business can contribute their own data sets and create a central repository of data assets for the business. 

With a carefully curated set of data assets, users can now select, combine, and reuse data sets to address current business case requirements and have the capability to address new business use cases that aren’t yet defined.  As a result, companies that invest in data transformation are finally starting to realize the promise and value of big data.

Data Monetization Value Chain

Though the terminology used to label the various components of the data value pipeline can vary from institution to institution, typically the data value chain is broken down into 5 key categories:

  1. Data Capture & Acquisition – This refers to the collection of raw data from both internal and external sources. The first phase of data collection involves identifying what data to collect and then establishing a process to do so (i.e. conducting a survey or retrieving automated IoT data). Decisions made here will affect the quality and usability of data throughout its life-cycle. 
  2. Data Processing & Cleansing – Bad data in equals bad insights out so, once data is collected, it must be, processes organized and cleansed. This involves cleaning data – identifying and correcting corrupt, inaccurate, or irrelevant data – as well as converting raw data into a format that is usable, integratable and machine readable. 
  3. Data Curation, Integration & Enrichment –  Data curation and integration refers to the collection of processes required to merge data from multiple sources into one, cohesive dataset. During this process, data is also enriched, meaning that contextual metadata (the data that makes larger datasets discoverable) is added or updated. 
  4. Data Analysis – Now that data has been cleansed, labeled and is primed for usage, the real fun can begin. Datasets can now be analyzed and used to uncover trends, patterns and other insights that can enhance decision making. 
  5. Data ROI or Monetization – The final step of the process is the application of data analytics processes to solve real-world problems and, in a business setting, increase revenue. This can be done by either using data analytics to optimize the efficiency internal operations and decrease overhead costs or by using data-driven insights to identify and exploit new revenue streams.  

In addition, the data value chain is more than just an outline of technical steps, achieving ROI with data requires significant cultural changes as well. Cultivating data literacy amongst non-technical users and promoting data democratization are also key parts of the success equation. 

Data Monetization Consultant

Every company is a data company. Businesses from technology to retail have an ever-increasing amount of data about their customers and their business daily operations. Despite that, only 1 in 12 companies are monetizing their data to its fullest extent.

Data monetization is the process of leveraging data to generate measurable business benefits through Internal or external data monetization techniques. That said, the path toward a successful data monetization strategy is full of challenges that limit companies’ ability to harness their data to its full extent. 

This service includes: 

  • Understanding your business and data. 
  • Listing the different business models for data monetization and the applicability of each to your business. 
  • Summarizing the different data products that can be produced from your data and the impact of that on different markets.
  • Application of your data in different markets and revenue potential on each market.

Big Data Analytics Monetization

Many B2B businesses understand that data monetization using AI and data analytics can create higher returns on investment and streamlined operations. However, despite the will and the knowledge, they are unable to maximize results.

The reason for this is simple: They’re still treating data as the tech component of their larger strategy. What they should be doing is putting data in the driver’s seat.

Let’s examine how data analysis using AI and Big Data Analytics can assist in monetizing data.

1. Upselling

While upselling may have originally been viewed as a way to sell more products, it’s now a way to sell more relevant products. With data analytics driving the decision-making, businesses can suggest products that are complementary to their customers’ purchases and that bring value to customers. Greater value for the customer means their satisfaction increases, which helps with customer retention.

In addition, the original goal of making more sales is achieved as well. When the customer sees that their needs are being predicted and addressed, they will likely appreciate the service more. This new way of sale shows that businesses can make more sales and additional revenue by optimizing their operations with a data-driven approach, without selling the data to a third party.

2. Improving Customer Experience

It’s no surprise that customers return to businesses that are easier to deal with. Delivering high-quality support is a growing pain point for many companies. Chatbots based on machine learning algorithms can help relieve some of this pain. These chatbots can handle the most common use cases, and a representative can step in for more unique demands. It can reduce query response times and maximize customer satisfaction.

Chatbots play a crucial and helpful role in solving minor problems for customers, which frees up precious time for customer reps to focus on the more complex issues. Consumers prefer to interact with companies that can respond in real time while making a purchase, much like interacting with a sales associate at a brick-and-mortar store.

Thus an AI-driven chatbot can help your customers find answers to their questions when they place an order. It gives the impression that your brand is always there to serve their needs, even during those late-night shopping binges (when all your sales reps are probably asleep!) Furthermore, AI can integrate fragmented data sources to collect all the information regarding customer experience, to create a customer-centric approach.

3. Optimizing the Time of Your Sales Representatives

Anyone with experience in sales knows that it’s a war zone. Having the highest quality data can optimize the entire process. Salespeople can benefit significantly through an AI-based data-driven business model. They can have all the key facts and figures about each product, vendor, volume, and sales at their fingertips.

Not only that, but they can also have insights into competitors’ products. Salespeople can use that knowledge to track the products they’re responsible for and make fact-based decisions. They can also optimize their time by knowing when and whom to visit, or call a vendor. This management can increase efficiency, reduce waste, and save time.

4. Streamlining Supply Chain and Logistics

Managing the supply chain, especially for large businesses, requires careful planning. Any issues in the chain can create a cascade of problems further down the chain. Even reducing lead times and procurement cycles marginally can have immense benefits in the competitive world of business.

Having data on your side can provide such an edge. AI and data analytics is a great way to analyze the chain to look for improvements. This will significantly impact how buyers conduct business with their vendors.

In practical terms, AI can alert vendors to disruption in the supply chain, recognize suppliers for compliance issues, and quickly identify fraud cases. This can enable more innovative procurement to help better decision-making and offer a real competitive advantage to businesses.

Logistics Data Monetization

Many retail and supply chain companies that aren’t maximizing the value from enriching data and missing out on opportunities to grow, optimize or manage risk. From geographic information to purchasing history, data presents retailers and supply chain providers with a powerful opportunity to extract insights about business activity, customer behavior, and other trends.

And because different kinds of data can be valuable in a wide variety of applications, even information that seems to have no immediate use to a company can generate benefits for other organizations that need it.

Using data to assess current business practices, identify new opportunities, and maximize efficiency can put serious money back in the pocket of any retailer or supply chain provider.

Here are ten ways to monetize data generated throughout the retail industry and supply chain:

1. Incorporating Predictive Analytics

One of the most useful applications of data for retailers and supply chain providers is predictive analytics. Combining and enriching data about pricing, inventory and customer behavior allows retail and supply chain companies to make intelligent forecasts through predictive analytics, follow market trends, and develop recommendation engines for future customer purchases.

By utilizing information that’s already at the fingertips of any store, fulfillment center, or manufacturer, companies can run leaner operations, offer better product selections, and anticipate demand throughout the year.

2. New Revenue Models

Keeping customers coming back for more is the dream of any retailer. Analyzing customer and supply chain data makes it possible to discover new revenue models that satisfy customers in new and innovative ways. With the rise of e-commerce subscriptions and recurring revenue programs, data can convert one-time buyers into loyal customers dedicated to brands and products.

3. Strategic Marketing

Advertising is no longer a game controlled by dominant Madison Avenue types. Digital marketing tools have empowered brands and organizations of any size to reach customers across a wide variety of platforms, eliminating expensive intermediaries to speak directly to an intended audience.

Retailers have been investing in data-driven ad tech to take advantage of commerce advertising, which responsibly uses first-party shopping data to target customers. Marketing with retail and supply chain data makes it possible to save money on ineffective advertising and reach customers directly.

4. Decrease Shrink and Fraud

Shrink has long been one of the most frustrating and expensive costs of retail business. Retail data analytics capabilities can monetize existing data by assessing patterns, trends, and anomalies to uncover potential fraud and theft.

5. Geotargeting

Reaching a customer when they’re ready to make a purchase can help close the sale. Geolocation data enables retailers to add moments of connection to in-store shopping experiences, sending product information directly to a customer’s mobile device. By implementing capabilities that can send personalized discounts or point to the right location of an item in a store, location data can bring forth new opportunities to sell products and create better retail experiences.

6. Improved Business Cooperation

Because retail companies and supply chain providers work in concert with each other to fulfill customer needs, it’s essential for partner companies to speak a shared language. Boosting the level of cooperation between manufacturers, stores, and vendors can turn data into cost savings throughout the entire supply chain.

7. Increased Customer Satisfaction

Keeping customers happy keeps them coming back. Identifying new ways of boosting customer satisfaction is a smart long-term strategy to ensure that customers can find, purchase, and receive products without pain or friction.

Because data makes it possible to understand customer needs better, retail and supply chain companies can significantly improve the customer journey by extracting valuable insights from how customers interact with a company. Data can also be taken advantage of by a comprehensive CRM system, which allows brands to build long-term relationships with new and existing customers.

8. Traffic and Density Planning

For brands with a physical retail presence, location and layout can help define a store’s success. Shopping and vendor data can be used for traffic, density, and location planning, ensuring companies build ideal locations well-situated to customer needs. Every additional data point that helps suggest a better retail experience can help create a positive retail experience and smooth logistics operation.

9. IoT Devices

From the store to the warehouse floor, improved visibility into the flow and storage of goods allows retail and supply chain companies to better track and analyze inventory. Sensors, trackers and other connected Internet-of-Things (IoT) devices can produce real-time data about an item’s location, shipment status, and availability.

Read Also: How Machine Learning Can Improve Real-time Bidding in Digital Marketing

Optimizing retail operations using IoT devices makes it possible for retailers and supply chain companies to cut costs and boost customer satisfaction.

10. Sell Data to Companies in Other Industries

Data generated by retailers and supply chain providers can provide a wide range of insights into consumer behavior and logistics. This information isn’t just valuable to the companies that produce it: new industries looking to bolster their analytic capabilities may have a use for existing data sets or information that otherwise doesn’t appear to be of immediate use. Selling relevant information — with a keen eye towards data security and privacy rights — can generate additional revenue from work already being carried out.

Importance of Data Monetization

A lot of companies are already reaping the reward that comes with data monetization. You might be wondering whether there are benefits that can be enjoyed. Below you will find the reasons why data monetization should be very important to your company.

1. It Creates Opportunities in the Market

More and more businesses are identifying the value of the data they are collecting. With adequate data volume it leverages the untapped and tapped information to develop new revenue sources.

2. Data Monetization Increases the Value of Data

Companies like Google and Facebook culminate all the activities associated with a user, thus deriving the interests, income level, buying preferences, and so on. Most companies identify partners to enhance internal data and increase the data value. 

3. Helps in Sizing of the Market

This involves leveraging data from one business unit for use by another part of the company to optimize a broader system. Also it helps an external client with the ability to make better decisions by tapping into data-driven solutions packaged by the company.

4. Data Monetization Optimizes the Use of Data and Maximizes Its Value Potential

Customer data monetization answers ‘how much is the data worth of a company’. It optimizes the usage of data and provides you with insights such as market demand, data’s shelf life, competition, potential customers etc. to attain maximum value potential.

5. Monetize Your Data to Increases Overall Productivity

You can use data to increase productivity or reduce consumption and waste. Also, it helps you to improve sales performance and minimize customer attrition. By monetizing your data, you understand your target customers and create meaningful segments slicing the data according to your target audience’s needs.

6. Creates Competitive Advantage in the Market

Companies such as Amazon, Netflix, and Disney monetize data by gaining a close understanding of their customers. With this, they can offer highly relevant products/services, delight their target audience at every touchpoint, and create a competitive advantage in the market.

7. Data Monetization Boosts Profitability

Data is intrinsically valuable, but comprehension derived from data enriches that value. This further segments customers, predict demand and churn, optimize prices, and manage cost. Further data can command higher margins when sold externally. Now what you get is overall profitability.

8. Improves Customer Experience and Strengthens Customer Loyalty

In addition, when you monetize your data, you are able to figure out the understanding of the customers. You can now use these insights into how best to improve products and services. Moving ahead, this improves the customer journey and makes the customer stick by your product.

9. Data Monetization Examples Witness a Boost in Revenue Streams

By segmenting the customer database based on gender, demography, prospects, industry, and so on into related target groups, the data owners make the most out of data. These classifications allow them to deliver highly targeted, personalized messaging, better user experience and outstanding revenues earned.

10. Helps in Strengthening Partnerships

Buying and selling of data take place in a data marketplace. If you are a data owner, you can set data-prices and consumers can choose from who they wish to buy data. It improves data sharing and collaboration between internal and external stakeholders

How Much Money can you Make Selling Data?

Data is the new currency on which today’s wealth depends on. This is how tech companies like Google and Facebook have evolved to be considered some of the world’s largest and most profitable companies. Data has become the engine that powers companies across numerous sectors. They rely on consumer data to drive profitability by influencing the rational decisions taken by the end consumer.

It is already a known fact that when consumers extensively use the services of networks such as YouTube and Instagram for free, they are providing them with personal information and data which could be considered sensitive. Tech companies leverage their proprietary data surveillance systems to stay profitable.

Although calculating the exact worth of consumers’ data is near impossible, a rough estimate tells us how much data is worth to these companies. It was reported that the email of a single internet user is worth $89 to any brand, so it makes sense to say consumers’ data is worth a lot of money.

Another great way to estimate the worth of a user data is to calculate the amount in cash equivalent after an acquisition. Microsoft, for instance, acquired LinkedIn for a $26.2 billion cash deal. At the time of the acquisition, the professional business-oriented site had a membership count of over 400 million users.

Going with this information, it is safe to say that a single user data point was valued at an estimated $65 per user in the purchase.

Following this logic, a popular social media giant, Facebook acquired WhatsApp for $19.6 billion. That means Facebook paid $39.6 for each of WhatsApp’s 500 million users. The value of user data could vary but depends heavily on the earning potential expected from each user.

The worth of user information also depends on the industry the company falls within and hence the type of data it gathers. The wider and richer the information these tech firms possess in the world of big data, the more the revenue it can realize by acting on users’ data. Therefore, the worth of a consumers’ data and the valuation of a company are intertwined.

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