data-monetization

What Is Data Monetization? Data Monetization In A Nutshell

Data monetization describes the process of a business using data to obtain an economic benefit. In essence, data monetization is the process of utilizing data to increase revenue. According to management firm McKinsey & Company, an increasing share of the most successful companies in the world incorporate data and analytics to fuel their growth.

Understanding data monetization

Data is becoming increasingly ubiquitous as the widespread adoption of big data systems and Internet of Things (IoT) devices allow consumers and businesses alike to collect data on anything and everything. In fact, it is estimated that the world creates around 2.5 million terabytes of data every day, with more than 90% created in the past two years.

Despite the relative ease with which data can be collected and analyzed, the practice of monetizing data remains relatively uncommon. Research out of Germany discovered that less than one in five companies had established data monetization initiatives and a mere 0.5% use data in decision making. Instead, most companies spend vast amounts of time and money storing their data instead of determining how they can make money from it.

Data has undebatable intrinsic value, but the most value is derived when the business demonstrates the ability to derive insights from that data and monetize it accordingly.

How is data monetized?

Different data monetization methods will be appropriate to different companies according to their particular business strategies, growth stage, or industry.

Here are a few ways data can be monetized:

  1. Data as a service – the most simple method where data is sold to customers in a raw, anonymous, or aggregate form. The customer is responsible for analyzing the data to facilitate a financial gain.
  2. Analytics-enabled platform as a service – these are platforms sold to customers that provide scalable and versatile data analytics in real-time. They can be cloud-based or installed on-premise and can incorporate a wide array of data formats.
  3. Insight as a service – where internal and external data sources are combined and analyzed to generate insights. This method tends to be confined to specific contexts and datasets. For example, John Deere combined external soil and weather data with internal crop timing and fertilizer usage data to create an intelligent farming system that is sold to farmers

How can businesses successfully incorporate data monetization?

To understand how a business can incorporate data monetization, consider these pointers:

Understand the role and value of data 

In theory, data should facilitate business performance, reduce risk, and prove compliance. However, this can only occur when the business understands the relevance of its data and how valuable it is. Some companies fail to realize this as they do not consider data to be an asset.

Data monetization must be embedded into strategy 

Business strategy should always be underpinned by data management initiatives and vice versa. It is important management understands how data is connected to strategy before it implements structures to monetize it. 

This requires the company to assemble a cross-functional, multi-disciplinary team with members from sales, marketing, operations, and data management.

Communicating the value of data to facilitate growth

As the studies mentioned in previous sections have demonstrated, data monetisation remains a mystery in some organizations. Even when the practice is in place, employees may not understand the underlying reasons for its success.

Communicating the value of data monetisation to internal and external stakeholders will become paramount in ensuring market competitiveness, among other things.

Key takeaways:

  • Data monetization describes the process of a business using data to obtain an economic benefit. Despite the relative ease with which data can now be collected, the practice remains relatively uncommon.
  • Data monetization can be monetized in a few different ways. These include data as a service, analytics-enabled platform as a service, and insight as a service.
  • To ensure businesses make the most of their data, it is important they first understand its role and value. Data and business strategy must also support each other and the value of data should be understood by internal and external stakeholders.

Main Free Guides:

Connected Analysis Frameworks

Failure Mode And Effects Analysis

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A failure mode and effects analysis (FMEA) is a structured approach to identifying design failures in a product or process. Developed in the 1950s, the failure mode and effects analysis is one the earliest methodologies of its kind. It enables organizations to anticipate a range of potential failures during the design stage.

Agile Business Analysis

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Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.

Business Valuation

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Business valuations involve a formal analysis of the key operational aspects of a business. A business valuation is an analysis used to determine the economic value of a business or company unit. It’s important to note that valuations are one part science and one part art. Analysts use professional judgment to consider the financial performance of a business with respect to local, national, or global economic conditions. They will also consider the total value of assets and liabilities, in addition to patented or proprietary technology.

Paired Comparison Analysis

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A paired comparison analysis is used to rate or rank options where evaluation criteria are subjective by nature. The analysis is particularly useful when there is a lack of clear priorities or objective data to base decisions on. A paired comparison analysis evaluates a range of options by comparing them against each other.

Monte Carlo Analysis

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The Monte Carlo analysis is a quantitative risk management technique. The Monte Carlo analysis was developed by nuclear scientist Stanislaw Ulam in 1940 as work progressed on the atom bomb. The analysis first considers the impact of certain risks on project management such as time or budgetary constraints. Then, a computerized mathematical output gives businesses a range of possible outcomes and their probability of occurrence.

Cost-Benefit Analysis

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A cost-benefit analysis is a process a business can use to analyze decisions according to the costs associated with making that decision. For a cost analysis to be effective it’s important to articulate the project in the simplest terms possible, identify the costs, determine the benefits of project implementation, assess the alternatives.

Financial Modeling

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Financial modeling involves the analysis of accounting, finance, and business data to predict future financial performance. Financial modeling is often used in valuation, which consists of estimating the value in dollar terms of a company based on several parameters. Some of the most common financial models comprise discounted cash flows, the M&A model, and the CCA model.
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