Most of the modern companies have realized that data assets are the engine driving the growth and value of organizations. However I argue, that only a comprehensive data valuation framework will help for discovering and realizing the true value and potential of data. Without a framework, there is a risk of missing out e.g. competitive advantages and stakeholder value that data assets could generate.
As with other assets, it is important to understand what affects to the value of data. Data can be e.g. vital to company´s business operations, already commercialized or even - when there´s no data available, there´s no business!
Start by answering some fundamental questions
Comprehensive data valuation is a multi-step process and every company is unique, but answering these following questions may help you in the beginning:
What data assets do we have?
How and where we are currently using them?
What data assets help e.g. to grow our business, improve productivity, decrease risks of business and enable better forecasting?
Are there synergies with other assets in the company?
Are there already 3rd party commercial revenues or potential earnings associated with the data? Or only internal use?
The most relevant attributes from a value perspective
The first steps in process are gathering knowledge of company´s starting point and executing an inventory of current data assets and determining how the company is currently utilizing data, or is it being used at all. When looking at each data asset in more detail, it is important to look at its attributes and how they affect to the value of data.
The most relevant attributes in terms of data valuation are:
Exclusivity
Timeliness,
Accuracy
Completeness
Consistency
User restrictions, liabilities and risks
Interoperability and compatibility
After creating a inventory of existing data assets, you are able to start valuing data in more detail. Then it is important to look at through valuation lenses current and future data-based use cases.
Potential use cases can be internal and provide a competitive advantage or save money. Completely new data might need to be collected or acquired as a part of the process and combined to the existing ones.
Annual data financial statement
Overall, especially when usage of data is business critical, data asset management strategy should be implemented. Managing data assets can be at least just as important like managing e.g. material assets. When the value of data is significant, an annual data financial statement should be made. In addition to the financial value of the data, the data financial statement also implements the principle of accountability. According to this principle, the organization itself shows that it complies with the law, good data processing practices and management practices.
There are various of theoretical data valuation methods available, but they may not suit for your needs. Therefore, at least I think, it might be better in the beginning to create own practical data valuation framework and focus on increasing company´s data utilization potential.