How to Build Data Strategies for Effective Data Monetization


Table Of Contents

Almost every company uses data in one form or another to fuel its growth. The digitization of processes has made data more accessible than ever, but many companies don’t know how to use the data they have to make better decisions. They need a plan to make sure they use the data the right way, or they might miss out on important opportunities.

Companies need a robust plan to collect, process, and manage the data generated from their operations to facilitate initiatives like data analysis, transfer (internally and externally), and monetization. This is called a data strategy.

This article discusses data strategy and how it can help companies effectively use and monetize their data.

What is Data Strategy?

A data strategy is a plan that encompasses the technology, processes, governance, people, and assets surrounding an organization’s data, determining how said data will be used to further business objectives.

Companies are increasingly understanding how important taking advantage of data is to drive up their bottom line and identify new opportunities. However, data science is still new, and many companies are still trying to grapple with how to extract value from it. It’s like the old days of trying to “strike it rich” and find oil, except that these companies have all of the digital oil they need. They just can’t figure out how to use it effectively.

A recent fortune 1000 survey of global data and business leaders found little change in business innovation efforts driven by data in the organizations surveyed over the last four years; with only 59.9% of executives reporting that data-driven business innovation was a priority in 2023, the same number from the same survey that was conducted four years ago. And although investments in data are increasing (87.8% of executives surveyed said that their companies had increased investments in data in 2022, and 83.9% expected this investment to continue in 2023), the ROI for increased investment in data isn’t driving the results expected.

Data strategy helps companies plan to process, utilize and monetize their data. A well-crafted data strategy helps organizations make better decisions, improve efficiency, enhance the customer experience, gain a competitive advantage, and manage risks effectively. 

Data Strategy Framework for Data Monetization

Effective development of a data strategy for a company varies depending on the organization’s size, industry, and goals. However, companies must consider certain components while creating an enterprise data strategy, including data generation, collection, storage, and uses. 

The different steps in creating an effective enterprise data strategy framework are as follows:

Collect, Centralize, and Analyze Data

The first step in creating a data strategy is to define methodologies to collect, centralize, and analyze data. This process requires the following steps:

1. Identifying Data Requirements

identifying data requirements involves understanding what data is needed to further business goals, where that data is located, and how it can be accessed, which involves:

  • Mapping the business processes that will be required to implement various data-related initiatives, such as data monetization. This identifies the data inputs and outputs required to support each process.
  • Next,  data requirements need to be prioritized based on their importance in achieving the business goal or goals. This places the focus on the most critical data requirements and ensures that the data strategy is aligned with the organization’s needs.

After identifying the data requirements, the data sources need to be analyzed.

Analyzing Data Sources

Analyzing data sources involves understanding the quality and reliability of the data and identifying any gaps in the data that need to be filled.

The following are some of the tasks that you can use to analyze data sources:

  1. Start by identifying all of the data sources that are used within the organization. This includes internal and external sources such as databases, spreadsheets, business applications, customer feedback, market research reports, and other possible sources.
  2. After identifying data sources, categorize the data sources based on their purpose. For instance, you can set categories like operational data, financial data, and customer data. This will help you to understand the types of data that are available and how each set can be used.
  3. Next, you need to analyze the quality of the data available from each source, including data accuracy, completeness, timeliness, and relevance. Doing so helps you eliminate unusable data or augment existing data with external information to make it complete and usable.

Once data quality has been analyzed, the next step is to assess how data is integrated with the organization. This involves determining how and when people, programs, and applications access and use data. This is known as data architecture.

Developing Data Architecture

While developing data architecture,  the structure of the data needs to be defined, including how it will be stored and accessed and how it will be integrated with other systems. The process for doing this involves several steps, including:

  • Assessing tools and systems within your organization. Take stock of all the systems and tools in your organization and how they work together. Talk to stakeholders and determine the usefulness of these tools, including what works well and what doesn’t. Chances are the list of tools can be streamlined, and opportunities for better integrating these tools with each other can be identified.
  • Develop a data structure. Creating a structure for your data means considering storage, like a data lake and multi-cloud systems, and how data moves in and out of those repositories. Creating a data science pipeline is essential for mapping where data goes within an organization and how it’s interacted with. data governance policies also need to be established to control data access.
  • Define business goals. It’s important to remember the overarching goal for your organization’s data, which is to help meet business goals. Consult with stakeholders from all departments, as well as executives, and set specific business goals and consider how utilizing data will help you meet those goals.
  • Ensure consistency in how data is collected. Consistency with data collection is paramount to ensure that accurate results can be gleaned over time. For example, if a company changes how it collects website data year over year, making past and present comparisons won’t be accurate as the data collection method, and therefore the data itself, is different. It would be like comparing apples to oranges and trying to determine which is better, when they are fundamentally different fruits.

Defining the Data Analytics Approach for Your Organization

After developing the data architecture, your organization’s approach to data analytics can be defined. This process requires identifying the tools and techniques needed to analyze the data, as well as the data analytics workflows that will be used to process the data and generate insights. Data analytics algorithms and analytics workflows need to be determined, and the appropriate analytics tools need to be selected.

A data visualization plan also needs to be developed that defines how the data will be presented to stakeholders using appropriate data visualization tools.

The Increasing Size of Datasets and Processing Capacity Call for a Cloud-Based Solution

Many organizations want to capitalize on big data, but in-house resources are often not sufficient enough to store and analyze it. To effectively use big data, you need to choose a data infrastructure that can handle increasing amounts of incoming data.

Platforms like Google Cloud, Microsoft Azure, Amazon AWS, or Snowflake to store and process your data. These platforms provide the needed infrastructure for each of their customers to manage large amounts of data, with the ability to customize different aspects of the system and apply established data governance policies.

While these platforms are able to faciliate data transfer between a source and target via data marketplace functionality, effective data monetization may be hindered by two main problems:

  1. Limited display functionality affecting how data products are presented
  2. Data consumers being required to be part of the source ecosystem

Revelate provides a solution to these challenges. First, the marketplace is fully customizable, meaning that companies can choose how they want their data products to be displayed for maximum visibility. Secondly, Revelate is platform agnostic, which means that data can be extracted from any source system and transfered to any target system, provided that the target system supports the format of the data set.

Revelate also helps democratize access to data inside and outside an organization, meaning that anyone can access data provided they have the right credentials. No more getting IT teams to be the middleman to enforce data access and governance policies, Revelate does it automatically every time a data set is requested.

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Choose An Operating Model

After collecting data and setting up the required infrastructure, you need to decide on a data operating model.

There are four operating models for data monetization, which are indicated in the table below:

Operating Model Distinguishing Feature
Return On Advantage Model This model focuses on the competitive advantage that an organization can gain from its data assets and how this advantage can translate into financial returns.
Premium Service Model Based on the premise that some customers are willing to pay for access to high-quality, exclusive, or specialized data that is not available through other channels.
Differentiator Model Since data can be used to identify new business opportunities, this model helps optimize existing processes or improve customer experiences in ways that set an organization apart from its competitors.
Syndication Model Organizations can use this model to outline how to generate revenue by monetizing their data assets through partnerships with other organizations that have complementary data needs.

These models will also be explained in more detail later in the article. To use the above models effectively, data security initiatives must also be in place.

Adhere to Governance, Compliance, and Cybersecurity Best Practices

Data security is the key to a successful enterprise data strategy framework. If your data strategy has loopholes that allow unauthorized access or that can lead to a data breach, your company might incur fines and have negative impacts on reputation. Implementing data governance tools and measures to follow cybersecurity best practices and comply with data protection laws such as GDPR and CCPA is paramount.

Data governance is the set of policies, procedures, and standards that ensure the proper management of data within an organization. It is a critical component of any data strategy framework. Steps for implementing enterprise data governance in your organization are as follows:

1. Set Data Standards

To reduce silos, maintain data integrity, and promote interoperability of data, standards need to be set. Centrally managed data language needs to be established, usually with the help of data catalog tools. This includes data identifier taxonomy (like a library that uses the dewy decimal system to organize books) and data science models that determine how the different elements of your data standards work together in different situations and contexts. Data standards should be flexible enough so that they can be updated as needed to support changes with data strategy.

2. Create Data Governance Roles

Typical roles are outlined in the following table:

Data Goverance Role Responsibilities
Chief Data Officer An executive role that acts as a leader for the company’s data governance strategy, and works to achieve buy-in from the entire executive team.
Data Owners Directs how data should be used. For instance, the HR manager would be responsible for employee data, including ensuring that it’s used in accordance to regulatory and company privacy policies.
Data Stewards Handles the training and education required to enforce company data governance policies.
Data Administrators Act as a point of contact for resolving data-related issues.
Data Custodians Handles the technical aspects of data movement, including security, storage, and use.
Data Users Responsible for educating themselves on the company’s data governance, access, and use policies, as well as how to use any data governance tools effectively. Data users should also inform the data governance team of any issues pertaining to the above.

3. Map Data Governance Business Goals

Mapping the business goals with data governance should start with a focus on the most important (top level) objectives, and then using those as a base point to map out downstream objectives.

For instance, a top-level objective might be to establish better data literacy in your organization. In order to reach that goal, the roadmap would include providing effective data-related training to all employees.

4. Focus on Simplicity

It’s important to make rules and standards easy for everyone in the organization to follow, otherwise adherence will be limited. Most employees, although they may deal with data everyday as part of their jobs, will see data governance as a secondary consideration to getting the things they need done with data. While perceptions can change with education, it’s important to realize that if strict, hard-to-follow rules are implemented, they may add more time and energy to an employee’s already overflowing workload. Simple and straightforward rules that are easy to follow are less likely to be seen as a burden.

4. Determine What to Do With Unmanaged Data

Data that doesn’t fit in any specific location still needs to live somewhere. A lakehouse is one solution that could be used to store this data effectively, and ensure that security is maintained.

5. Use Metrics to Measure Progress

Specific data-related metrics that you may want to measure include:

  • Data quality, which includes accuracy and completeness
  • Data literacy rates in your organization
  • Data ownership and accountability, specifically with regard to how successful data owners have been with maintaining data reliability and following data governance rules
  • Business value of data, which refers to how effective the company’s gata governance strategy has been in meeting organizational goals

6. Use Automations

Data automations are important to utilize to reduce human error and reduce time spent on tedious manual tasks. Taking advantage of automations means saved time and resources that can be spent elsewhere.

Operating Models for Data Monetization

operating-models -data-monetization


As introduced earlier, we will be discussing the four operating models for data monetization in more detail in this section.

Return On Advantage Model

The basis for the return on advantage model is to use internal performance data, as well as external information, such as patterns and trends, to create some kind of business advantage.

For example, retailers will use consumer purchasing patterns to identity a myriad of different opportunities to create better and more personalized shopping experiences for their customers. This data can tell retailers what content to send to a customer’s email or SMS and when (e.g., a customer that always purchases at a specific store can be sent sales and promotions to their smartphone when they are in the vicinity of that store) identify potential upsell opportunities (e.g., a customer buys a pair of glasses, why not get the same style in sunglasses) and much more.

 Premium Service Model

The premium service model typically pertains more to SaaS, where the value proposition and the value exchange formula are optimized. Essentially, the service is able to be accessed by the customer in exchange for a monthly or annual fee. This can pertain to a wide range of industries, from telecommunications where people pay a monthly fee for voice and data access on their mobile phones, to fitness monitoring devices that offer enhanced health data access to consumers for a fee.

Differentiator Model

The differentiator monetization model might seem counterintuitive at first glance, but it can be a a powerful model for building customer loyalty and brand recognition. Basically, the product or service is provided to the customer for free, in the hopes that it will positively impact the company. A good example of the differentiator model in action is influencer marketing. Using popular influencers that are well-known to a target audience to promote a product or service is known to have significant positive effects for a business in terms of recognition and brand loyalty.

Syndication Model

With a syndication model, transformed data is delivered to third parties so they can use it for various purposes, including analytics, research, planning, or product or service development. The data consumer signs up to receive syndicated data (e.g., APIs, tables, reports) usually via a marketplace like Revelate. The data owner generates income from selling this data to multiple data consumers. Most companies utilize this data monetization model.

How Does a Cloud Data Strategy Affect Data Monetization?

These days, organizations store their data in multiple cloud-based locations. Organizations use cloud-based systems because they are flexible and scalable—both things that modern organizations need to operate effectively.

A cloud data strategy can have a significant impact on data monetization. It provides the necessary infrastructure and tools to manage and process large volumes of data.

There are several ways in which a cloud data strategy affects data monetization, which are discussed in the table below:

Business Aspect Impact of Cloud Data Strategy
Scalability Cloud infrastructure can provide scalability to manage and store large volumes of data. This will allow your company to collect and store more data, which can be used to generate more revenue through data monetization.
Data processing The bandwidth and processing power needed to extract insights from large amounts of data is almost impossible to provide in-house. Because cloud-based environments are infinitely scalable, they can be utilized to handle large data processing requirements effectively.
Agility Cloud data infrastructure can provide agility by allowing your company to quickly scale up or down its data processing capabilities based on changing business needs. This can help you to respond quickly to market changes and take advantage of new data monetization opportunities.
Collaboration Online marketplace solutions like Revelate  can provide collaboration opportunities  that allow multiple teams to access and work on data sets. This can help organizations to leverage the expertise of multiple teams to generate new insights and develop new data products and services.
Security and compliance Managing the security of multi-cloud and hybrid-clod systems can be a bit more complex, as they all have their own built-in security and access. A security management platform like Immuta (which is built into Revelate) assists with this issue by providing a centralized platform where global data governance and security policies can be applied to all systems and managed accordingly.

Tools To Upgrade Your Monetization Data Strategy Technology

There are various tools that you can use to facilitate data monetization. You can use data monetization and fulfillment tools provided by different cloud providers to monetize your data.

  • A data marketplace solution like Revelate is optimal for data monetization. Internal and external interfaces can be provided so that employees can easily access data sets to extract insights from, while external customers and stakeholders can access data sets to purchase.
  • Data discovery tools help organizations locate and anlayze their internal data, even when it’s scattered through multi-cloud or hybrid cloud systems, so that it can be utilized for business objectives.
  • Data catalog tools assist in sorting, categorizing, and standardizing data assets so that they can be easily found when needed. Effective data cataloging is an essential part of an organization’s data governance strategy.
Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started




With big data becoming more prevalent, companies have access to vast amounts of information that can be leveraged for financial gain. By implementing an enterprise data strategy and choosing the right data monetization model, companies can turn data into valuable assets. There are various data monetization models to choose from, including direct data sales, data marketplaces, and data partnerships. Each model has its advantages and disadvantages, and it’s essential to choose the one that best aligns with your business goals.

To create a successful data monetization strategy, it’s crucial to follow the right steps, such as identifying the right data product, selecting the appropriate data monetization tools, and establishing the right data monetization strategy.

Book a free trial with Revelate and discover how our platform can help you optimize your data monetization efforts.