[White paper] How to determine the economic value of your data

The importance of data has changed over the years. Initially, data was just the “exhaust” from an organization’s On-Line Transactional Process (OLTP) systems. However as the volume, variety and velocity of the data grew over the past few years, the economic value of data has been transformed by the big data phenomena [1] that has enabled organizations to capture a broader, more granular and more real-time range of customer, product, operational and market interactions. Today, business leaders see data as a monetization opportunity, and their organizations are embracing data and analytics as the intellectual capital of the modern organization.

More and more companies are also contemplating the organizational and business challenges of accounting for data as a “corporate asset”. Data as an asset exhibits unusual characteristics when compared to other balance sheet assets (where asset is defined as property owned by a person or company, regarded as having value and available to meet debts, commitments, or legacies). Most assets depreciate with usage, however data appreciates or gains more value with usage; that is, the more the organization uses the data across more use cases, the more valuable, complete and accurate the data becomes. These same characteristics apply to analytics, where analytics is basically “data” that has been refined or “curated” into customer, product or operational insights.

This research paper will explore data as a form of currency. Most currencies are constrained to a one-to-one transactional relationship. For example, the economic value of a dollar is considered to be finite – the dollar can only be used to buy one item or service at a time. Same with the finite nature of a person as a person can only do one job at a time. However, the economic value of data is not constrained by transactional limitations. Data as a currency exhibits a network (or multiplier) effect, where the same data can be used simultaneously across multiple business use cases thereby increasing its financial and economic value to the organization.

However, there are severe limitations in valuing data in the traditional balance sheet framework. It is important that firms identify a way to account for their data. Organization’s also need a framework to address the “Rubik’s Cube” intellectual capital [2] challenge regarding how to identify, align and prioritize the organization’s data and analytic investments. To address this challenge, this research paper will put forth the following:

• A framework to facilitate the capture, refinement and sharing of the organization’s data and analytic assets, and
• A process to help organizations prioritize where to invest their precious data and analytic resources.

It is our hope that this research paper will foster new ways for organizations to re-think how they value their data and analytics from an economic and financial perspective. The concepts covered in this research paper will provide a common vocabulary and approach that enables business leadership to collaborate with the IT and Data Science organizations on identifying and prioritizing the organization’s investments in data and analytics; to create a common collaborative value creation platform.

This is an excerpt from “Applying Economic Concepts To Big Data To Determine The Financial Value Of The Organization’s Data And Analytics, And Understanding The Ramifications On The Organizations’ Financial Statements And IT Operations And Business Strategies”white paper sponsored by Hitachi Vantara. For more resources on our website, check this link.