The importance of data has changed. As the volume, variety and velocity of the data grew over the past few years, the data has been transformed to provide organizations 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.
The Internet of Things is accelerating this drive towards “data monetization.” However organizations are quickly learning that you don’t necessary monetize the data as much as you monetize the customer, product, and operational insights derived from the data to create new revenue opportunities: new products, new services, new channels, new markets and new partnerships (see Figure 1).
But to help organizations realize these new monetization opportunities, organizations need to transform the way that they create and even account for data and analytics. To support that organizational transformation, I recently completed a research paper with Professor Mouwafac Sidaoui, Department Chair and Associate Professor of Business Analytics and Information Systems at the University of San Francisco, titled “Applying Economic Concepts To Big Data To Determine The Financial Value Of The Organization’s Data And Analytics Research Paper”. The research paper seeks to integrate leading academic thinking with practical real-world consulting experience to advance the cause for organizations to embrace data and analytics as unique corporate assets.
More and more companies are 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. 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.
Taking the idea of data as an asset one step further, what if organizations viewed data as a form of currency? While most currencies are constrained to a one-to-one transactional relationship, data and analytics do not suffer from that limitation. 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 not only account for their data, but to maximize the economic value of it across the organization. To accomplish this, we created what we like to call the collaborative value creation platform. This framework seeks to:
- Identify and prioritize the highest potential business use cases—using a prioritization matrix can facilitate the discussion between business and IT stakeholders in identifying the right use cases upon which to focus the organization’s analytic resources.
- Build analytic profiles to facilitate analytic capture and re-use—analytics profiles are structures that standardize the collection and re-application of analytics across multiple uses cases.
- Identify and prioritize data sources loaded into a data lake—the data lake supports the data science processes of refining the data into actionable analytics to create financial or economic value.
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 seek to 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.
The complete paper can be viewed here: USF The Economics of Data and Analytics 7.0
