Strategies to Generate Revenue from Your Business's Data Assets
In today's digital age, companies accumulate vast amounts of data through increased online activities. However, not all businesses are fully capitalizing on this valuable resource. Two recent articles offer insights on how non-tech firms can leverage data as a revenue source.
In the article **"4 Steps to Start Monetizing Your Company's Data"** published by Ulrik Stig Hansen and Eric Landau in the September 2022 issue of the Harvard Business Review (HBR), the authors provide a systematic approach for non-tech companies to monetize their data.
## Identifying Valuable Data Assets
The first step is to inventory and understand the data your company collects and generates. Determine which data sets have potential value beyond internal use and could serve external customers or partners.
## Assessing Market Potential and Use Cases
Next, evaluate how your data can meet specific market needs. Identify industries or customers who could benefit from your data, and develop potential use cases or solutions that leverage your data’s insights.
## Developing a Data Monetization Model
Choose the right business model for monetizing your data — such as selling raw data, offering analytics as a service, or embedding data insights into products. Consider pricing, delivery channels, and required technology infrastructure.
## Building Capabilities and Managing Risk
Establish the necessary organizational capabilities, including data governance, compliance, and security. Manage risks related to privacy, intellectual property, and customer trust to ensure sustainable data monetization.
These steps provide a roadmap for non-tech firms to transform latent information into valuable business assets.
It's important to note that companies should never discard data they collect or acquire, as it may have potential for future monetization. For many business problems, existing solutions are available, making buying and customizing a better option than building from scratch. Culture is the greatest barrier to data success in many companies, preventing them from becoming truly data-driven.
The democratization of machine learning models allows non-tech companies to invest in and benefit from cutting-edge digital technologies. Understanding the data a company already possesses is the first step for non-tech companies. Despite investments in data, very few companies treat data as a business asset.
Tools that are core to a business can be considered for full in-house development, while non-core areas should examine existing solutions and customize them. Replicating how new-age companies derive value from data can help leaders understand data usage in business and monetize it. Observing and analyzing data usage trends in other companies can help improve processes and understand what to focus on with the data.
In the digital economy, companies must become more data-driven to compete effectively. AI, machine learning, and other cutting-edge digital technologies are increasingly accessible to non-tech companies. Sponsoring events with startups can help find innovative ways to use digital technologies like AI and machine learning.
The FAANG companies have collected vast amounts of data, using it as their primary source for products and value. Building a large data repository can provide a company with a competitive edge, acting as a "data moat." The biggest challenge for startups and new companies today is the availability of information, not capital.
In conclusion, the data boom is here, and non-tech firms can capitalize on it by following a systematic approach to monetizing their data. By understanding their data assets, assessing market potential, developing a monetization model, and building capabilities, companies can transform their data into valuable business assets.
The first sentence: "Companies can capitalize on the data boom by utilizing machine learning and data-and-cloud-computing technology to systematically monetize their data, as outlined in the article '4 Steps to Start Monetizing Your Company's Data'."
The second sentence: "Investing in the democratization of machine learning models can help non-tech companies leverage digital technologies, enabling them to become more data-driven and compete effectively in the digital economy."
