An Introduction to Data Valuation and Its Importance

Today, 90% of business values comprise intangible assets, including intellectual property, brand reputation, corporate data, and customer metrics. That makes understanding the value of your business’s data and maximizing it is key to boosting profitability and overall worth.

As more companies turn to data monetization, keeping track of data assets, managing them, and identifying what is truly valuable is essential. Data valuation’s main objective is to benefit a business by increasing the total enterprise value of an organization by recognizing the data as an asset. Additionally, data valuation results in proper data organization, as you must organize data in order to determine different metrics needed to properly value it.

This guide explores data valuation’s significance and key functionalities in dealing with your business assets. It also discusses how you can build an efficient and comprehensive data valuation framework to get better revenue and profitability.

What is Data Valuation?

Data valuation encompasses determining the value of data that is stored, collected, analyzed, and exchanged by companies. A big part of determining the value of data involves properly organizing that data. This helps you identify the value a dataset provides and makes it easier for you to monetize data for your business.

Data should be managed in the best way possible because it helps businesses establish processes and make well-informed decisions. Good, manageable data must be able to be:

• Archived and moved: you should be able to sort and move data in order to easily organize it
• Created: you need to be able to create new data sets as you gather additional information and insights from cliens, competitors, and other information sources
• Overseen and updated: you need to be able to update old and obsolete data so that it continues to provide value
• Validated and controlled: you need to be able to check the sources of your data to ensure its accuracy

Through data valuation, your business can quantify a data asset’s value and costs. At the same time, valuation allows you to understand proper data management and seek ways to improve it. It also helps you look for innovation opportunities to promote a better data-oriented culture in your business.

Understanding data valuation means you master the proper way to define data assets. These should be:

1. Identifiable and distinct: including database records, tables, and files
2. Commit to prospective and economic benefits: should be applied significantly in real-life situations
3. Under your organization’s control: your responsibility to use your data while complying with the current laws and system
4. Understanding of market demand: knowing who would want your data and how unique it is in comparison to what’s already available in the marketplace.

Data valuation helps your business learn where you can focus limited resources. This will ensure your company gets the best return on investment from your data assets. You must also understand that data valuation is more than knowing your data asset values’ complex numbers. It involves comprehension and deeper understanding to leverage advantages for your business.

Why Does Valuation of Data Matter?

Businesses are one with the fact that data is essential in the industry. In fact, a report revealed that more than 80% of businesses reported an increase in their overall revenue after learning to monitor and value real-time data.

Data valuation is important because it lets your company identify precisely how much your data is worth. And when you see the true worth of your business’s data, it becomes easier to collect, protect, store, and distribute these pieces of information. You must also remember that data valuation should always be coupled with an efficient data analytics strategy.

Data valuation also lets you concentrate on departments or areas that will benefit your business. It helps you zero in solely on the information you need. Moreover, data valuation enables you to create more informed decisions that can help drive better data analytics strategies. data valuation also provides a monetary value to your data. The economic value helps you compare your data with other relevant assets in your business.

Determining your valuable data assets can give you more choices in consuming your available resources. Always remember that having options is a great business and data strategy.

When you know how to add value to your data, you improve your company’s overall data culture. Instead of pushing the people to believe that your data has value, you can simply show them the monetary values obtained.

When your data has an established monetary value, it can also result in boosted morale of your company’s data team.

Valuation Data Methodologies to Incorporate

Data assets serve as the engine for businesses to grow and become more valuable and are the foundation of data science within any organization. This is the same reason why data valuation should have a comprehensive framework to maximize your company’s data potential.

When proper data valuation methodologies are incorporated into your business model, you increase the value provided to your shareholders, which can optimize your organization’s future success. 

The Market Approach

Your company’s current data value is vital for the market approach. Your team tracks relevant information based on how much your clients pay for data assets in the active market.

Calculations may be easy, especially for large data proportions. With the market approach, you get a clear idea of how companies are responding to your data, what they’re paying for it, and how it’s being exchanged. This gives you clues as to how much competitive advantage your data provides, thus helping identify how much value it brings to your customers.

Multi-period Excess Earnings Method (MPEEM)

Measuring the intangible assets’ fair value is the primary purpose of MPEEM.  Using this approach entails isolating all the net earnings relevant to the asset you’re currently measuring. It’s the method’s fundamental principle.

The fair value of any intangible asset refers to the current value of your after-tax cash flows. You can combine these intangible assets with another set of intangible and tangible assets. Doing this helps with increasing your company’s income.

MPEEM creates two main key assumptions:

• Projected cash flows from your asset’s useful life
• Consideration of CAC or contributory asset charges, tax amortization benefits, and discount rate

Relief from Royalty Method

The following intangible assets are the main focus of the Relief from Royalty Method:

1. Trade names
2. Patents
3. Proprietary technologies

This approach involves measuring these assets’ fair value to generate ways your company can license them from a third-party provider, such as through a data exchange platform. Moreover, the data asset’s fair value represents the license fees avoided, like the royalty savings.

Companies can calculate their royalty savings by assigning their assets reasonable royalty rates. This method allows you to apply the same royalty amount to your yearly related sales. It’s also the approach of choice in evaluating a business’s trade name.

With and Without Method

The With and Without Method involves creating rough estimates of the data assets’ value. Companies achieve this by identifying the impact of specific cash flows and quantifying them to assess whether that data is necessary or can be removed or replaced.

Assets and entities involved in this method involve operating expenses, cash flows, and projected revenues. They undergo calculations in “with” and “without” data scenarios, allowing companies to see the difference in the evolution of cash flows in both.

The Cost Approach

In layman’s terms, the cost approach involves measuring the following entities:

1. Data storage and production
2. Data replacement
3. The amount of impact made to cash flows because of lost data

The cost approach also consists of identifying fundamental laws and principles to help more businesses understand the importance of data valuation. It works similarly to the glue reply valuation technique. 

Like the market approach, the cost approach is easy and can be accomplished within a short amount of time. It’s also reliable for obtaining reputable information regarding a cert data asset’s value.

The Stakeholder Value Approach

This approach focuses on what the stakeholder needs. It goes straight into the value’s source, which involves getting accurate measurements of each stakeholder’s economic value. Aside from stakeholders, the employees, customers, communities, suppliers, and the environment are also significant.

Compared to the other methods, this relatively more modern approach aligns with capitalism’s gradual shareholder-to-stakeholder shift. The Stakeholder Value Approach looks at the situation from a larger perspective, allowing a better understanding of each stakeholder’s value and significance.

The Economic Value Approach

Two essential methods comprise the Economic Approach: Utility or Income Valuation and Use Case Valuation. Here’s a quick rundown of what each of these are.

Utility Valuation

This method tracks the effects of specific data on a business’s overall goals and objectives. It allows data to determine how much value is added and how it boosts certain use cases or business functions.

One drawback of utility valuation is its difficulty in how it measures. The measurements produced are often too broad, including distinguishing the amount of value the data added.

Use Case Valuation

The Use Case Valuation involves two techniques. The first is the Business Model Maturity Index proposed by the Internet of Water. It calculates data values through business use cases and estimates each case’s value.

The other technique is Decision-based Valuation which works similarly but with more functionalities. This method also models a data collection’s accuracy and frequency to identify the purpose of certain data assets.

How to Build a Comprehensive Data Valuation Framework

A comprehensive data valuation framework, as outlined in the book Infonomics, is essential if you want to understand your data assets’ value. The framework helps make better decisions by focusing on relevant data and investing your resources in them. 

Additionally, data valuation frameworks answer two main questions:

1. How much precisely is my data worth?
2. Are there ways I can increase my data’s overall value?

With several frameworks you’ll see online, the following are the most effective and efficient frameworks you can add to your current data valuation strategy.

Identify/Inventory Current Data Assets

The first step to data valuation is determining your starting point. It should involve determining your current data assets and providing them with attributes for better identification. Moreover, inventorying your existing data assets makes it easier for you to know how to utilize them in your business.

Identify Data Attributes

The following are attributes you can use to describe the state of your current data assets:

• Data Quality: recency, accuracy, relevance, and type
• Source: collection method, data governance, privacy implications
• Targetability or Selectability: for certain data segments
• ROI or Use Case: Substantiated use case
• Universe or Breadth: Audience coverage
• Exclusiveness and Uniqueness: the probability of having similar data
• Market Demand: Willingness and market to pay

Identifying attributes and assigning them to your data assets allow maximizing the impact of the data on your company’s risk, growth, and profitability.

Identify Current Data Use Cases

After identifying your current data assets and assigning attributes, uncovering use cases is the next step. These use cases can range from data’s defensive and alternative uses to new commercial applications.

You can use four kinds of use cases to segregate your data assets.

Use Case 1: Internal

Internal data use cases involve using data to allow companies to set themselves apart from their competitors. This enables businesses to achieve a first-move advantage, becoming disruptors and leaders across business ecosystems.

Let’s take a life insurance company, for example. When the business performs a weekly analysis of mortality claim data, it can collate information obtained for its sister company’s internal use. The company can comprise a wealth manager, leading to better pricing and sales-led algorithm profitability.

In this internal use case, an effective valuation methodology is a with-and-without approach. This allows businesses to understand better how data can impact internal use cases to improve the business’s value. Moreover, despite requiring major discretion, the with-and-without approach can help establish the possible value range of data assets.

Use Case 2: Existing Data’s Commercial Usage

Commercial usage of existing data enables companies to see the value of various B2B transactions from the data collected. For instance, retail companies can analyze and package data from the purchases made by their proprietary customers. Doing this allows your business to obtain the information you need and supply it to a third-party provider.

The standard methodologies used in this use case are the with-and-without and the relief-from-royalty methods. These forward-looking techniques allow companies to zero in on relevant data about the following:

• Profitability
• Growth
• Risk

Use Case 3: External or Alternative

External use case is best used when organizations discover that their data can be used for multiple purposes. This allows them to establish a parallel business model where the data set can be sold externally.

The best application of this use case is in agriculture yield. Farmers realize the value of their farm-equipment manufacturers for their personal use and in different geographies. This makes lenders interested in obtaining crop data from the farmers.

The best valuation methodology used is the relief-from-royalty approach. This method allows businesses to add new revenue streams for better crop data analysis.

Use Case 4: Defensive

Defensive use cases involve companies leveraging large data volumes to enable fast entry to more markets in the same industry. This gives them an edge over their competitors. A defensive use case can also mean providing better services and products to your target personas to scale quickly and become better than your competitors.

The best valuation methodology for a defensive use case is the cost-value approach. It’s the best method for companies to use their internal data properly to increase their overall data asset values.

Explore Alternative/Future Data Use Cases

After identifying all possible current use cases, you can proceed with the succeeding steps for value creation. Exploring future data use cases is a significant step to enhance your company’s value and your stakeholders.

The staged approach below can help you accomplish a more efficient data valuation process:

Pivotal Points to Understand Data Asset Valuation

To understand what data asset valuation means, you must look into three main pivotal points. These aspects are essential to ensure your data assets are significantly used for your business. The three most important pivotal points include growth, returns, and risk.

Growth

Utilizing growth allows you to generate ways how to improve your business. It encompasses your company’s revenue, profitability, and value creation for each department and stakeholder. You must be able to answer the following questions when considering your company’s growth:

1. Is your data being utilized to generate revenue?
2. What are potential alternative use cases for your data assets?
3. Can you find similarities with other data assets within your organization?

Returns

A data asset’s return is an important measurement of an organization’s efficiency in generating profits from its data inventory. Visual data representations are essential for companies to develop emerging techniques to help them understand large raw data volumes.

When there are proper representations of data, better business decisions can be made, including the following:

• Revenue lines that can be possibly leveraged
• Costs for potential elimination
• Division for closing

The above aspects help create more data value, leading to better sales and return, especially when it’s time to see the company. Your business should answer the following relevant questions when dealing with returns:

1. What is the cost to house and maintain data?
2. What are the system and organizational costs associated with your data assets?
3. What are potential earnings you can achieve from data assets?
4. Will there be returns on internal data usage?

Risks

The last pivotal point points to valuation risks that can damage or negatively impact your data assets. Valuation risk is the fear of loss because of the significant difference between the actual price of a product or service and the recorded price on the company’s balance sheet.

Some factors contribute to increasing valuation risk, which include:

• Using data with no available market information
• Market instability
• Poor data verification

When dealing with data valuation risks, it’s essential to consider the following questions. Answering these can give you better insights about your data assets, diminishing risks that can potentially affect your revenue and profitability:

1. What are the privacy and data security impacts to the organization (e.g., data generation, storage, access, and dissemination)?
2. Do the risks outweigh the returns associated with specific data types?
3. Can data reduce the organization’s risk by proactively providing insights or creating “defensive value”?
4. Can we identify market perceptions of the organization, including data held and retained?

Conclusion

Data valuation is more than getting increased monetary figures for your data. While it’s an outcome every company aspires to achieve, there is much more to valuing your data assets.

Data valuation provides a better understanding of your company’s data, allowing you to use them in the best way possible. At the same time, it helps ensure accurate planning and forecasting of future and alternative use cases.

As businesses rely on data-driven strategies, the need for proper data management, monetization, and valuation also increases. All these aspects are important in business structure reinvention to facilitate more profitable models.

In order for your data to be truly valuable, you need to be able to access it. Revelate is a  data sharing platform and data marketplace that allows companies to buy, sell, and moneitze their assets. Book a demo to learn more about it and how it can help your business.

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