In contrast with other essential elements of human life like death and taxes, the history of insurance has been very short. However, in terms of evolution, the concept of insurance has been constantly changing and continuously embracing new domains. Insurance of properties, life, health, beauty, athletic talent and limbs are very trivial now. Data insurance, which has been limited once to multi-billion dollar corporates and that too for limited scenarios, is now taking center stage.
The drivers for data insurance existed for quite some time, but they haven’t proliferated into human life and organizational practices as it happens now. The key drivers pushing the trend towards data insurance are the protections we need against data loss, data compromise and data misuse.
Organizations, as they evolve in their presence over web, social networks and mobile applications, are capturing more and more data. The rest of the discussion in this article focuses on two categories of this data.
- Acquired data: All the customer information, employee information and any other user information collected directly or indirectly from the users constitutes this acquired data. By nature, this class of data is highly likely to have sensitive information that includes personally identifiable information (PII), credit card information, etc.
- Generated data: All the housekeeping, analytics and user behavior data in an organization falls into this category. This data is very vital in deliveringÂ better user experience to both end users and internal teams. This data is mostly generated by an organization’s web/mobile applications that interface with end users and may be augmented with data inferred from other user interactions like support calls and email exchanges.
Any compromise on acquired data leads to a very big exposure – loss of face, legal tangles and/or customer loyalty issues. The data compromises detected at companies like Target and Home Depot are leading to customer unrest, loss of loyalty and severe financial implications from legal consequences.
Any compromise of generated data makes an organization limp (often heavily) in their business process. Generated data compromise mostly leads to inefficiencies and exposure of the secret sauces to competition.
The impact of a compromise on generatedÂ can’t be taken any lightly when compared to the impact of acquired data compromise. The generated data may also include intellectual property related items that could hurt a company in the long run when that data is compromised.
Digital (or digitized) data captured by humans also is increasing in its prominence,Â value and the risk of compromise. Whether it is personal pictures of celebrities or tax data of individuals, the risk associated with any compromise of this data is increasing over time. As the data access avenues are increasing (e.g. health data accessed via a wearable device), the potential for compromise of personal data is also increasing.
Given all this increased focus on data and its risks, we see a bigger shift towards insuring the data by corporations and individuals. Data Insurance is taking new paths that are less traveled by insurance companies in the past. Data Insurance packages now contain and cover a wide variety of data sets.
Just like humans undergo a set of prerequisite tests before taking a new health insurance package, data sets might undergo certain audits that cover the access controls and security risks associated with this data. We may also see a trend towards re-audits during renewals of data insurance to re-validate the access controls and risks.
The key factor in Data Insurance is determining the value of data. Human life insurance packages usually cover sums like 5x annual income. Vehicle insurances usually cover up to the Bluebook value of a vehicle. Coming up with valuation for data is not that straight forward though. The valuation process might differ greatly between acquired data and generated data. Unlike constant depreciation of a vehicle’s Bluebook value, the value of data may either decrease (data that becomes stale over time) or increase (with volumes or with increased sensitivity of same data) over time. Data Insurance companies and the insured organizations/individuals will often be re-evaluating the value of data to optimize costs and minimize the impact of exposure.
In summary, here are some of the primary factors by which data insurance evolves:
- Categorization of data
- Valuation of data
- Data audits
As data insurance hits mainstream, all these factors experience market growth and some sort of standardization beyond what we have today.