Is your data an asset or liability?

Is your data an asset or liability?

Three strategies to tip the equation in your favour

Businesses are placing data at the core of their strategy

In our previous post (part I), we spoke about the explosion of commodified data, which is now ‘more valuable than oil.’ In short, we explored how data is easier than ever to gather and store, and provides insights that are of huge commercial impact, going so far as to redefine entire industries (e.g. Netflix, Uber).

It’s hardly surprising that most businesses now realise the immense value that data can create, but many fall into a dangerous cycle of assuming that more data always equals more value. This mindset overlooks not only the hidden costs of data collection (which can deliver an unexpected sting), but also the mammoth harm from a data breach. Who knows how many companies are storing your personal information without a clear purpose or your knowledge?

When is data an asset?

Data is an asset when it generates value, which can be achieved in many ways. It can be used for operational metrics, KPIs and dashboards, and to provide critical insights into business performance. It is also used for one-off analysis to deliver ad-hoc decision making.

“Knowledge has become the key economic resource and the dominant, if not the only, source of competitive advantage.” Peter F. Drucker

These uses can result in a big competitive advantage. This occurs when data enables the organisation to move faster than competitors, through embedding the information within organisational processes, or through generating unique and defensible insights which better align the business with customer needs.

For example, Netflix achieved over 90% retention rate through personalising recommendations, tailoring adverts and producing their own content to meet the specific viewing trends amongst their subscribers.

The scale of the competitive advantage is determined by multiple factors, including: the uniqueness of insights, and the extent to which insight-driven actions deviate from the baseline; how easily replicable the data is; how long the data remains relevant; and, how fast competitors can replicate the actions once the success is visible in the market. More valuable data will create a larger moat, resulting in a more defensible and advantageous position.

When does data become a liability?

Like many other things, data becomes a liability when the costs outweigh the benefits. Sounds pretty simple, right? In theory yes, in practice less so.

The first issue with this equation is that the costs of data are often difficult to quantify upfront. There are many hidden costs throughout the data lifecycle. For example, if the data gathered is of low quality, the costs of ‘cleaning’ it can be surprisingly high while weakened confidence can undermine any insights. Additionally, storage costs (although cheaper than they used to be) can quickly accumulate, reaching millions of dollars as data volumes swell.

Additionally, data breaches represent the largest potential cost, yet they are rarely accounted for due to their unpredictable nature. IBM estimates the average cost of a breach was $4.4m in 2022, but over twice as much for those in the US ($9m) or healthcare ($10m). These are only the averages. Penalties can reach hundreds of millions of dollars; GDPR permits up to 4% of turnover, while penalties in Australia could be up to 30% of turnover under new legislation. This is before accounting for the reputational harm and loss of trust. Medibank breach costs are estimated to be as high as a billion dollars, as regulators, victims and lawyers seek reparations for the damages.

90% of unstructured data is never analysed — IDC

The simple truth is, in their hopes of using information to gain an edge over competitors, businesses often gather too much data. According to the IDC, an astonishing ~90% of unstructured data is never even analysed. The accumulation of this data serves no purpose other than to amplify the hidden costs and the likelihood of a breach. Information can appear to be free at the point of acquiring it as the marginal cost of each extra data point is so low. As a consequence, businesses fall into the trap of gathering and retaining all available data without fully considering the total cost of ownership.

To add to the complexity, the value of data typically erodes over time — it depreciates like most assets (although much more quickly) as it loses relevance. As time passes and the value diminishes then the data will become more of a liability than an asset.

So what should businesses do?

In short, businesses need to maximise the value from data whilst minimising the cost. While there are many strategies a business can pursue, the implementation of privacy-by-design principles, data protection measures, and treating data as a product will materially benefit their value equation.

  1. Apply data minimization principles. The golden rule is to only collect what you need, and don’t keep it for longer than you need it. A clear cost/benefit analysis should be conducted before collecting the data, and processes established to delete this data when it’s no longer generating value. In cases where data is partially useful, data masking should be used to reduce personally identifiable information.
  2. Invest in data security. Any data collected should remain secure and customers should have confidence that their information is safe. Companies need to minimise the likelihood and potential damage of a breach. Tools such as data encryption, multi-factor authentication, and employee training in data management and risk prevention should be embedded in the DNA of an organisation.
  3. Treat data as a product. Data often lacks a specific owner with defined responsibilities, resulting in low levels of accountability for data quality, maintenance, integrations and meeting business needs. The result is data with low accuracy, often sprawling throughout an organisation but with limited confidence from data users. By treating it as a product, with investment in data teams (e.g. appointing a CDO) and tools, will increase the likelihood that data is reliable, efficient, integrated and is aligned to business requirements.

If you would like to learn more about what we are building at Onqlave to help protect sensitive data, follow our updates via LinkedIn, sign up to our newsletter or feel free to get in touch with any of our team.