Data is an asset, not a commodity.
Some people talk about data as the new oil, but this is too simplistic. Oil is a commodity – to be bought and sold. Data is an asset, an asset that grows in value through use.
A single person’s data is not very valuable. Combining the data generated by thousands of people is a completely different story. Coupling that with data generated in different situations, combining datasets, creates new insights and value for different actors and stakeholders.
If data is so valuable, then why do so few firms value it? Some seek to work out a price for their data. They try to understand what the market will pay for it. But the value in data does not always lie in its sale. Take Amazon and Alibaba, for example.
Both firms are seeking to optimise a marketplace; to connect customers with a demand, to organisations that can supply. Individual consumers provide data on what they want and need. Amazon and Alibaba use this to match the consumers to providers with the right products and services.
They also aggregate the data to provide insights into market trends and shopping patterns. They don’t sell data, at least not as their primary service, but they do use it extensively to optimise their processes.
The value of data to Amazon and Alibaba is not captured in a pricing approach. Yes, their data may be valuable to third parties, but it is more valuable to the firms themselves as they seek to optimise their operation. Indeed, without data, they could not continue to operate.
So, we can’t think of data value as simply the price others are willing to pay. We have to think more widely and in doing so we have to create methodologies for data valuation. This distinction, from data value to data valuation, is critically important.
Data value is a property. Your data has a certain value and you need to understand what this is in order to make appropriate investment decisions to support your data.
To understand the value of your data you need a methodology for data valuation. You need a way of working out what the actual value of your data is.
Our mathematicians and data scientists have developed unique, in-depth approaches to data valuation.
We have identified five different data valuation methodologies: Market-driven, Dataset-driven, Initiative-driven, Stakeholder-driven, and Pricing-driven. We’ll explore each of these in subsequent blogs, but before we do that let’s investigate the distinction between data value and data valuation.
One way to think about this is to ask the question, why would I want to put a value on my data? Think of data as an asset; organisations deploy assets to create value for different stakeholders. They also invest in assets to make them fit for purpose and, at any point in time, they have to consider which assets are worth investing in.
You can think of this as the data value/data valuation cycle. You have to assess and understand what data you have (data assessment). You have to put a value on this data (data valuation) so your people recognise the value of data, treat it with respect inside your organisation and work out how to make it more valuable.
From this, you then have to invest (data investment) to make sure your data is fit for purpose. You have to ensure you have good governance in place, an appropriate data strategy, standards, systems and procedures to ensure you achieve good data quality.
Once you have good data you can start to use it (data utilisation). This is centred around identifying how you can use data to create value for you and your stakeholders.
This may be through better operations. It may be through more efficient delivery of products and services. It may be by using the data to generate new and meaningful insights that are, in themselves, valuable. Then you can create data value – by acting upon these insights.
Finally, you have to review what you have learnt (data reflection), asking yourself, what have we learned from applying our data? How could we do this better in the future? Are there new and different datasets we need to access?
This cycle is endless – you oscillate between the data valuation and data value phases. Making sure that you understand the value of data is crucial to making the correct investment decisions.
Although this is only one step, it is essential for the path to data maturity. A path that turns a systemic disadvantage into a competitive advantage, leads to outperforming your competition, and creates long-term value for your stakeholders for years to come.