Not all data is created equal.
When companies make their tentative first steps on the road to direct data monetization (that is, selling non-personal data they own), they have to start by understanding the value of their data. They need to assess what types of data they are collecting or generating in the course of business, and whether these could potentially be a new driver of revenue.
It’s often said that data is an important and valuable asset in any organization, but there’s a reason why it never appears on a balance sheet. Data valuation is a complex and challenging exercise. But knowing what their data is worth can help companies explore monetization, allocate resources and properly structure their technology infrastructure.
In addition, they have to understand the market’s perception of value. Some firms might be overestimating the monetary value of their data, while others are unaware that their data is valuable, often to people in sectors and industries they may not have even thought of. A Forbes contributor compared the latter to the instance when companies realize they’re sitting on patents they don’t really need, but actually have value to someone else.
So what are the fundamental characteristics of high-quality data that organizations need to consider when trying to measure its value? Or more simply put – what makes data valuable and how much is your data worth?
1. Does it tell a story?
Does the data tell you something about the economy or market trends? Does it track which brands are growing or which products are in high demand? The better your data reflects real world behavior, the higher its value.
2. Is it unique?
Generally, the more exclusive the dataset, the more lucrative it is. Do you have data that nobody else has, or is it already widely available from other sources?
3. Is it anonymized and compliant?
If you are planning to share raw data, it needs to be stripped of all personally identifiable information (PII) to protect the privacy of individual customers. This is critical in order to monetize data responsibly, as data privacy is not optional but essential.
4. Is it timely?
Is your dataset updated on a weekly, monthly or a near real-time basis? The latter is most desired, especially for economists and institutional investors that are looking for faster insights to stay ahead of the market.
5. Is it specific?
The more granular and detailed the data, the more valuable it is. (Though to reiterate, personal details should definitely be excluded!) For example, data showing a million smartphones were sold last week is valuable. But its value grows significantly if it also indicates how many of those smartphones were iPhone X or Samsung Galaxy, etc.
6. Is it complete?
Do you have data for every day, without any gaps? Missing data could be as bad as inaccurate data, as it provides only a partial view of the real trends.
7. Is it reliable and consistent?
If there are multiple servers where data is collected or stored, do they all add up properly? Or do they contradict with one another? Are there potential duplicates or other data errors?
8. Do you have archives of historical data?
The further back your data goes, the better. Historical information is used in all kinds of analytics. In most use cases, two or more years of data are important to see how trends are changing over time.