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From taking advantage of a potential new revenue stream through data monetization to simply providing valuable data to more people, there is a wide range of reasons why organizations should work to determine their data value. Figuring out the value that your organization’s data provides is the first and most important step to monetize data effectively.
Sure, you could slap a random price tag on a dataset and hope that it sells, but it probably won’t be very fruitful. Value might be more of an abstract concept, but through research and thoughtful processes, it is possible to value data effectively.
Determining the best data pricing involves the identification of use cases and then applying different pivot points for determining the overall data value of a dataset.
In this article, we explore the different options that businesses have for assigning value to their data so that data can be monetized effectively using a data marketplace platform.
How to Price Data for Your Business?
Pricing data isn’t as straightforward as pricing a tangible product, like, let’s say, a piece of fruit or a pair of headphones. Different data consumers will have different preconceived ideas about the value of a dataset that your organization is trying to sell.
Consider this example:
- Customer A approaches your company because they know that you likely have data that would prove very useful for their plan to launch a new product. Let’s say that they approach you and offer you $5,000 for a specific dataset.
- Customer B is also interested in the dataset, but it isn’t as critical to them as Customer A. They’re willing to pay only $1,000 to access the data.
- Customer C only needs a small portion of the data, so they make an offer of $500 with the expectation that you’ll only provide the portion of the data that they are asking for, rather than the entire dataset.
Logically, it would make sense to accept all of these offers since you’re always going to retain ownership of the original dataset, so you can sell it as many times as you’d like, unlike a tangible product.
But that approach doesn’t do much for determining data value, and it’s easy to imagine the kinds of problems that will likely arise: what happens when a customer approaches you and asks you how much you’d like to sell the dataset for? Do you base your price on what Customer A offered or Customer B? What if a customer wants access to a certain portion of the dataset like Customer C? How do you determine the price of the portion of data?
To answer these questions and avoid awkward situations when data consumers question you on price, it’s important to understand the value of your data and use that as the baseline in your pricing strategy.
Key Points in Determining Data Value
At a high level, the value of data can be determined by keeping in mind these key points:
- One of the most important factors in measuring data value is quantifying its contribution to a business’s bottom line. This could refer to a specific business goal or objective, or its relevance in the success of a project.
- Data value can be determined by it’s level of risk. A higher level of risk usually means that the data is more valuable.
- What the data can be used for helps determine its value. If a dataset has a high number of use cases, then chances are it will be more valuable. However, that doesn’t necessarily mean that a niche dataset isn’t valuable; if it’s hard to find and is essential to a specific kind of business, then the value of that data will also be high.
What Makes Data Different From a Tangible Product?
To understand why data is different from a tangible product and, therefore, why the process for valuing it is different, consider the following points:
- Data can be shared an endless number of times without losing its value. Unlike physical products, where the more are produced, the less valuable they may become due to factors like market saturation, data can be shared without compromising its value.
- Data is perishable. If the data is time-limited, then the value of it decreases significantly over time.
- Data value increases when combined with other data. When external data is used to augment internal data, the overall value of the dataset increases. However, the added data must be useful; more data doesn’t necessarily mean better.
- Data security is extremely important. While physical products can be locked in a warehouse or store, data products need robust cybersecurity and enterprise data governance measures to keep it it protected.
Valuing Data as an Asset Through Building a Framework
A company’s data could be its most valuable asset. But determining that value requires a staged approach that considers the entire story surrounding a dataset:
1. Identify and Inventory Current Data Assets
The first step in determining data value is to understand your available data. This involves completing an inventory of data assets and evaluating how that data is currently being used. This involves:
- Understanding where data resides within an organization. Businesses often use various systems, applications, and programs, meaning that data is extremely distributed. Data discovery tools are essential for finding and making sense of all available data sources.
- Updating and maintaining systems that store data. Data that resides in archaic systems could be a security risk and may also be difficult to access. It’s important that modern systems are used to store data that hasn’t yet been used. Many organizations use systems like data warehouses or data lakes for this purpose, or the more recent lakehouse.
- Determining the data’s sensitivity. The more sensitive data is, the more valuable it may be, but it also needs more security to ensure its protection. Governance for sensitive data means understanding the risks behind it and implementing measures that align with how serious those risks are. For instance, security and governance for social security numbers should be high because if this information is leaked, it would significantly negatively affect the people those numbers represent.
2. Identify Data Attributes
Once data has been identified, it needs to be Categorized and cataloged. This is for ease of access and searchability. Data catalog tools assist with this by allowing you to add metadata to a dataset, which describes what the data is, what it’s about, use cases, and any other important information that helps users understand it. A data catalog also allows tagging, which works similarly to hashtags, to make finding specific types of datasets even easier.
3. Identify Current Data Use Cases
Determining data value includes outlining the use cases for different datasets. These use cases are outlined below:
| Use Case + Data Value Methodology | Definition | Example |
Internal
|
Data that is used for direct benefit to the company, such as improving operations or developing new products or services, or optimizing workflows. | By analyzing raw materials usage by various plants, a ready-mix concrete company is able to optimize materials usage and increase product yield, resulting in more revenue and increased sustainability and giving them an edge over competitors by producing a high-quality product with more efficiency. |
Commercial use of existing data
|
B2B data transactions result in a win-win for both parties: the data provider gains revenue, and the data consumer applies the data to improve their business. | By packaging proprietary customer insights, a retail organization is able to create a steady revenue stream by providing these insights to other organizations via a subscription. The receiving organizations then use these insights to conduct better marketing and sales initiatives. |
4. Identify Alternative or Future Data Use Cases
Organizations that are collecting data meant for a specific use case can sometimes find out that the data can be used for another purpose or multiple other purposes. This creates the opportunity to sell that data externally to generate a new revenue stream that wasn’t originally considered.
For example, a night-time peritoneal dialysis machine manufacturer collects and manages patient data to ensure the machine works as intended and provides the data to doctors and medical facilities. However, they’ve identified that this data is also important to developing new and innovative dialysis treatments, as medical research organizations are interested in potentially creating a portable version of the machine. To do so, they must better understand the existing treatment’s efficacy.
Defensive Use Case
In some cases, it may be beneficial for companies to gather large volumes of data in an attempt to gain an edge over their competitors. This could be through allowing them to enter new markets quicker or provide better products or services in an existing market.
For example, a wholesale fabric company maintains a database of information about a particular region where it doesn’t currently sell its products, but industry analysts suggest that this region may be a lucrative market to expand into at some point in the future. Of course, organizations that are gathering data for this purpose need to consider the costs associated with maintaining it, and determine whether the data is valuable enough to justify these ongoing costs.
Questions to Ask When Pricing Data
You can ask several questions to determine data pricing. The answers to these questions will help you determine the right data value for each of your organization’s datasets that are set to be sold or used internally.
1. Is the Data Unique?
One of the most important factors in determining data value is the uniqueness of the data. If similar data is widely available through different sources, such as data marketplace platforms, then chances are the demand for it won’t be as high and, therefore, won’t be as lucrative to sell as another dataset that provides unique insight.
That said, a dataset may include widely available information but augments it with unique information. It’s important to take the time to consider the full value of the dataset and highlight key points that make it stand out from other offerings in its metadata.
2. Is the Data Clean, Current, Trustworthy, and Reliable?
Another important factor in determining data value is to look at its quality. High-quality data is sought after because it can be trusted, and it’s complete, current, and reliable. For an organization to ensure that its data is of high quality, a comprehensive data governance process must be in place, and every stakeholder within the organization that deals with data must prioritize the integrity of said data.
This is why many organizations are moving to a data mesh approach. With data mesh, data management is distributed across the organization, with the relevant business units or domains responsible for the data that they produce or use. For example, HR would be responsible for employee data, while accounting would be responsible for financial data. This means that the people who know the data best can determine its use cases, level of access, level of security it needs, and more. While data mesh is a decentralized approach to data management, it should be supported by a centralized data governance team.
3. How Will Access Be Controlled?
Whether your organization uses a data mesh approach or not, ease of access still needs to be considered while at the same time ensuring that data governance and security is maintained. Organizational data is often distributed through multiple systems—applications, cloud-based storage, and on-premises storage (e.g., individual employee computers, servers, etc.). Democratizing access to data, regardless of where it is stored, is a challenge.
With Revelate, however, you can have the best of several worlds: democratized data access, robust security, and self-service, automated data fulfillment. Through a fully customizable online storefront, data consumers can access the datasets they need on-demand without the need for IT or data stakeholders to get involved. Access levels are applied automatically, so only the right people can access the right data at the right time.
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!
4. Is the Data Prepared to Be Used in BI and Analytics Systems?
It needs to be prepared for data to be usable in BI and analytics systems. Preparation can be done manually or through automated processes. Revelate prepares data automatically in the format that the customer requests so that it can be used by the source system immediately.
5. How Will the Data Be Sold?
One of the most popular ways organizations sell data is through a data marketplace. Businesses choose data marketplaces because they handle all of the security, licensing, and access considerations while giving the business access to an established client base.
But data marketplaces have limitations that organizations have to contend with, including limited display flexibility and an often convoluted customer journey (consumers have trouble finding data, and if they do, the limited display often causes them to ask for samples to understand the data better), and high competition with listings (older data products get buried in search results, and competitors with similar products can’t differentiate themselves in search).
While it’s still a good idea to list data products on multiple marketplaces, having your own custom marketplace where data consumers you’ve built trust with can easily find your data products is also a good idea. Revelate provides a data marketplace solution that does just that.
Pivot Points for Valuing Data
As part of assigning data value, an organization must determine how the data impacts the business from a growth, returns, and risk perspective.
Growth—Analyzing how data contributes to growth requires understanding how your data contributes to revenue generation and whether there is more opportunity for existing data assets, whether it be through augmentation with other data sources or examining potential new use cases.
Returns—The cost of storing, maintaining, and augmenting data must be considered to determine if the efforts involved in making the data usable for sale make sense with the amount of revenue generated from it.
Risk—Different types of data come with different risks, so it needs to be determined whether the risks with a certain dataset outweigh the associated return.
Data Valuation Methodologies
There are a variety of ways that organizations can set data value. These methods are called data valuation methodologies and are described in more detail in the table below:
| Data Valuation Method | Description |
| Cost value | Based on the return valuation, it measures the cost of storing and maintaining the data and the impact on cash flow if it is compromised. |
| Market value | Determines the current value of a dataset based on what others pay for a similar or comparable dataset in a target market. |
| Economic value | Based on two key methods:
|
| Stakeholder value | Measures the economic value of data based on the value it provides for specific groups, such as stakeholders, employees, customers, and more. |
How Do I Maximize The Value of My Data?
Data value can be maximized through a variety of methods, including the following:
1. Ensure Data is Clean
One of the most important factors in maximizing data value is to ensure that the data is complete, free of errors, incorrect information, and corruption and doesn’t have any duplicate information. Even with the cloud offering more data storage space, nobody wants to pay to house datasets that contain useless information. Taking the time to dedupe, fill in gaps in information, and eliminate corrupted data is essential for maximizing the value of your dataset.
2. Incorporate Data from External Sources
Augmenting internal data with data from external sources can also increase data value. For example, Dun & Bradstreet can fill in missing data from a corporation’s customer accounts, including related companies. This allows them to create customer information datasets that are more granular and complete than before, thus increasing the value of that dataset.
3. Incorporate ML and AI to Turn Data into Insights
Technologies like ML and AI offer a wide range of functionalities with regard to analyzing and gleaning insights from data. Probably one of the most important functions of AI and ML is the ability to glean insights from large amounts of data. Speed to insight is incredibly important for businesses that need to make quick, effective decisions to drive their business forward in highly competitive markets, and AI and ML can assist with providing those insights quickly so that these decisions can be made confidently.
4. Target the Right Audience
Another essential component of valuing data as an asset is ensuring that datasets reach their relevant audiences. This can be done in a number of ways, including setting up a data exchange with a network of organizations and individuals or utilizing a variety of data marketplaces that already have a client base consisting of the audience you’re looking for.
Conclusion
Determining the value of your data can be a tricky process. After all, it’s not a tangible product where the value can be assessed physically, so other methods of finding value must be used.
At the end of the day, valuing data as an asset involves the following considerations:
- How much it costs to store, maintain, and augment, and whether these costs are worth it in terms of how much money the business can make from selling the data
- How unique the data is, and whether that uniqueness (or lack thereof) significantly affects its value in terms of demand
- The risk the data poses (it’s sensitivity or confidentiality) and the robustness of security that is needed to protect the data but also ensure that the people who need it can gain access to it (licensing, vetting consumers, etc.)
- How long it will take for the data to lose its value, whether that be because the insights it provides are time sensitive, or for another reason
Once a data asset has a value and a price assigned to it, it needs to get in front of the right audience to be sold. Many businesses utilize a variety of data marketplaces for this because they have a low barrier to entry and an established client base. However, the limitations of most data marketplaces mean that it’s a good idea to establish your own platform for selling your data, especially when you build a reliable customer base.
Revelate is a robust data marketplace solution that offers self-service data fulfillment for different audiences (e.g., an internal audience will see a different interface compared to an external audience), centralized security, access, and governance, and a fully customizable storefront so you can display your data products in the best way.
Learn more about the Revelate platform by Booking a Demo today.
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!
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