Data science and data monetization are two of the most important developments in the modern technological landscape. They provide businesses, organizations, and individuals with powerful tools to unlock potential insights from their data and use them to make informed decisions that can drive growth.


Featured Article by Arek Skuza – “Why You Should Use Data Science and Data Monetization”


Data science is a field of study focused on understanding patterns and trends within existing data sets, while data monetization is the process of leveraging data to create value. Together, these data analytics tools can be used to build and maintain a competitive advantage in virtually any industry or field.

The power of these advanced analytics strategies lies in their ability to quickly analyze vast amounts of data and generate actionable insights. By utilizing data-driven strategies, businesses can make decisions that are informed by facts and evidence to act in the interest of both internal and external stakeholders. Data science also provides companies with the means to identify new opportunities for growth and expansion based on their analysis of current market trends and customer data.

The Power of Algorithms

Algorithms are not new, but the power and applications of algorithms have grown significantly in recent years. Business models today rely on them to automate repetitive business processes, improve customer experience and business performance, and personalize products for consumers. Businesses are able to make decisions faster with data-driven analytics that outperform traditional business processes in terms of speed and accuracy. Furthermore, as technology continues to evolve, companies can take advantage of the ever-increasing power of algorithms to further improve their operations and existing applications.

Companies have more data available to them than ever before. Therefore, algorithms are integral in helping them find relevant insights derived from big data. Moreover, as companies aim to drive more value from their products and services, more sophisticated methods of data monetization are being implemented to create more efficient processes for offsetting costs and improving the customer experience.

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Churn Optimization

By utilizing data monetization tools such as data-driven marketing, data mining, predictive analysis, and more, companies can upgrade their data management strategy and improve internal processes to reduce customer churn. This churn optimization can enable businesses to improve their internal processes, save costs by optimizing data flows, and leverage business data with accuracy.

Companies that utilize these tools gain competitive advantages, including cost savings, improved decision-making abilities, data-driven product design capabilities, analysis of customer-specific behaviors, and so much more.

The Advanced Data Monetization Capabilities of Artificial Intelligence (AI)

Artificial Intelligence (AI) has revolutionized data monetization strategies and is now an integral part of many digital business transformations. The data monetization capabilities of AI allow businesses to obtain strategic insights from data, enabling them to make informed decisions that can create a competitive edge.

AI enables businesses to create an individually-tailored data strategy, which allows them to evaluate data in the context of the entire customer journey, better understand customer behavior and needs, and gain efficiencies in data operations. There is no denying that AI-enabled data analysis steers organizations in the right direction for developing a profitable data strategy with greater precision and fewer obstacles.

The graphic below displays the requirements for advanced data monetization capabilities for AI. As shown, data science is an important facet of supporting AI strategies within the digital business transformation.

Requirements for Advanced Data Monetization Capabilities for AI

The Revenue Potential of Applied Data

As seen in the graph below, there is a strong relationship between applied data and revenue potential. With the proper resources, a company has the potential to shift from a data as a service company to an analytics-enabled platform as a service company.

The Revenue Potential of Applied Data

Applied data monetization is a way for companies to capitalize on their data and turn it into new revenue streams, making it a valuable business asset. By leveraging their metadata and differentiating models, businesses can create new datasets with the purpose of driving new sales opportunities. This leverage has the potential to not only access new sources of untapped revenue but also to increase existing revenue by optimizing the efficiency of certain processes related to pre-existing products.

According to a survey by McKinsey, high-performing companies are more likely to generate a higher percentage of revenue from data monetization compared to other companies. In the graph below, the y-axis represents the percentage of revenue from data monetization, while the x-axis represents the percentage of companies that fall into each y-axis category. For example, 17% of high-performing companies generate over 20% of their revenue from data monetization.

McKinsey survey_Data Monetization

Overall, it is apparent that high-performing companies achieve greater revenue streams from their own data techniques. Therefore, data analytics should be at the forefront of companies looking to become high performers.


Data science and data monetization are transforming the way businesses operate and interact with their customers. By leveraging the power of AI, businesses can automate processes and gain insights into customer behavior that can be used to create personalized experiences. Additionally, companies generate new revenue streams by selling data as a service to third parties or utilizing it to automate repetitive processes through algorithms.

Furthermore, there are big data platforms that examine the lifestyle of consumers by tracking their social media behaviors and through various other data collection techniques. For example, analyzes the conversations of consumers across digital platforms to understand their lifestyles. Then, they convert that data into actionable insights from its raw form. Finally, they create targeted marketing strategies that reveal insights companies can use to enhance their business intelligence. Platforms like Kimola can be used by businesses of all sizes looking to increase revenue.

With the right strategies and tools, any business can become more data-driven to streamline operations, maximize customer loyalty, and develop new business models. To stay ahead in an increasingly competitive world, start now by taking advantage of the powerful capabilities of techniques that monetize data.




MIT Sloan

MIT Management

About the Author:

Arek SkuzaArek Skuza is an experienced technology leader, with over 10 years experience in project management and working with technical cross-functional and cross-organizational teams. He has expertise in AI strategy design & implementation, Robotic Process Automation implementation, Machine Learning projects leading, Artificial Intelligence-powered product launch management and Go-to-Market strategies & Data Monetization strategies. Arek has consulted for major companies such as Shell Energy, Discovery Networks and IKEA, helping to monetize & leverage data to drive sales, engagement, retention & referrals.