Infonomics: Monetize, manage, and utilize data as an asset

Infonomics is the theory, study, and discipline of attributing economic significance to information and data. It strives to apply economic and asset management principles to the valuation, handling, and deployment of information assets.

Infonomics is about realizing the value customers find in your data and how to implement data management practices to best help customers navigate your products and services. This “monetization” helps organizations achieve a variety of goals from customer retention to boosting sales.

This blog will discuss the basics of Infonomics, provide a real-world use case, share the typical journey of data monetization, and share best practices to gain value from data.

The three Ms of infonomics

The main principles of infonomics can be broken down into three parts: Monetize, manage, and measure. These steps help data professionals make the most of the data, monetarily and to achieve business goals.

Part I: Monetize

Explores ways to identify data with unique opportunities to be monetized and justify business cases for further deploying your organization’s information. Measuring the value of data is a process, not a one-time thing! Different organizations may have different datasets that provide more value than others. What’s important is identifying the data that drives decision-making and promotes achieving organizational goals.

Part II: Manage

Tackles the challenges and best practices for managing all forms of data as an asset. This step involves applying project management practices and principles to your organization’s existing data management techniques to see real-world benefits. The more you organize and categorize your data, the more benefits you can identify for your organization.

Part III: Measure

Discusses the real and perceived roadblocks to measuring information as an asset by providing specific models to help organizations quantify the aspects and impact of information assets. What is stopping your organization from achieving goals? What will change once you remove these roadblocks? Who is responsible for helping the organization move forward with its data management practices? Transparency, teamwork, and interconnectedness all help organizations monetize their data and measure its success. Organizational roadblocks to data monetization can include:

• Mental blocks
• Insufficient data quality
• Lacking an internal culture of R&D
• Lacking organizational experience

Real-world data monetization example

In 2005, Walmart discovered the benefit of understanding, managing, and monetizing customer data. After the release of the popular TV show House, search results on Walmart.com were skyrocketing for the keyword. Customers were looking to purchase box sets of the previous season after the show’s season 2 premiere. However, these customers were disappointed to find their search results for “House” came back with anything but the TV show box set – Results included everything from dog houses to the Barbie Dream House. After some research, Walmart realized they needed to integrate trending data into their search engine to make sure people were landing on the product they were searching for to boost sales.

Infonomics: It’s about the journey and the destination!

Transforming raw data into actionable insights and solutions lies at the heart of effective decision-making for many organizations in today’s data-driven world. Vast and unstructured raw data holds the potential to unlock valuable information that can drive organizational project management and help companies achieve key goals. However, this transformation requires a systematic and iterative process that involves data collection, preprocessing, analysis, and interpretation.

To fully benefit from infonomics, organizations must transform raw data into actionable insights – An often daunting task. The journey can involve many steps, including:

• Raw published data
• Raw data access or query
• Raw data analytical workspace
• Integrated and enriched data
• Pre-aggregated data
• Analytics and insights
• Analytics and reports
• Customer data products
• Integrated data solutions

Transforming raw data into actionable insights and solutions looks different for every organization – How you use the data to achieve goals is up to you! However, this transformation should not be approached as a one-stop-shop: Data transformation and understanding takes time and research into your customer base.

Adding value to data

There are three main types of value that data can bring to an organization: Intrinsic, business, and performance.

• Intrinsic value – Data’s correctness and completeness
  Data quality serves as the foundation for insightful analysis, enabling organizations to draw meaningful conclusions and make informed choices. High data quality is essential for proper data-driven decision-making: It signifies accuracy, completeness, and consistency, ensuring that the information used is reliable and trustworthy. Therefore, maintaining high data quality is essential for unlocking the full potential of data’s transformative power.
• Business value – Data’s effect on achieving certain goals and objectives
  Business value revolves around leveraging data to improve decision-making, optimize processes, and enhance customer experiences. By analyzing customer behavior, market trends, and operational metrics, businesses can fine-tune their strategies to achieve goals. Business value transforms data from a passive asset into a proactive tool, driving growth and competitiveness in an ever-evolving marketplace.
• Performance value – Data’s effect on achieving and exceeding key performance indicators (KPIs)
  By tracking KPIs and operational metrics, data enables organizations to identify bottlenecks, inefficiencies, and areas of improvement. This value type is particularly relevant in industries like manufacturing, logistics, and supply chain management, where optimizing processes and minimizing downtime are critical. Real-time data analysis empowers organizations to make timely adjustments, thereby boosting productivity, reducing costs, and ensuring smoother operations.

Each of these value types plays a unique role in shaping an organization’s growth, decision-making processes, and overall success. Recognizing and capitalizing on these value types can lead to data-driven success, positioning organizations to thrive in today’s data-centric landscape.

Conclusion

Monetizing data has emerged as a transformative strategy for organizations to achieve their goals: By effectively harnessing the vast amounts of information they accumulate, companies can uncover valuable insights, trends, and patterns that drive innovation, enhance decision-making, and optimize operations.

Overall, the strategic monetization of data presents an unprecedented opportunity for businesses to bolster their competitiveness, foster growth, and realize their overarching objectives.
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