Data’s value: How and why should we measure it?
Excellent post by the ODI’s Ben Snaith, in collaboration with Peter Wells and Anna Scott, taking a deep dive on how data can be valued, governed and regulated
. Snaith argues that “trust in data itself has been undermined by recent global events” and, as a consequence, innovation will stifle and consumers will suffer until there is more progress in valuing data.
The six most valuable companies
in the world rely on data, and these “AI lock-in-loops” and “data networks” are creating new market barriers to entry. What’s more, newer approaches like deep learning and neural networks continue to improve as more data is added. Snaith points out the view that “data marketplaces will be key to addressing this challenge”.
Society is just at the start of realising the full potential of data to society and to the economy – driving this will be vastly increasing volumes of data being collected, stored and used. It is, therefore, increasingly important to find a way to measure data’s full value, as it is clear that our current methods are insufficient.
For this change to happen, we need to work collaboratively and push for engagement between people with different expertise – this is an issue that needs to be addressed by policymakers, economists, technologists, business and wider civil society.
Why the appeal of second party data is growing.
Great Marketing Week coverage on the trend of organisations seeking second party data partnerships
, either directly or via a platform. First party data is the data collected by a company about their customers (which remains king), whereas second party data is another company’s first party data.
This is to replace third party data, which is often purchased from a broker, with that “owned by trusted partners” as the “safer, more reliable option”:
Being able to buy audiences from other platforms that have been segmented based upon behaviours, interest and preferences is a powerful indicator of likely future behaviour. This is a safer place for brands to play. It offers not just reach but context as well. Brands will see a better return.
Doing good data science.
Mike Loukides, Hilary Mason and DJ Patil have come together to answer how we can put ethical principles into practice
. In particular, they argue that we need to nurture corporate cultures which encapsulate discussions about fairness, the proper use of data and the potential harm that can be done through inappropriate use. In summary:
Users want to engage with companies and organizations they can trust not to take unfair advantage of them. Users want to deal with companies that will treat them and their data responsibly, not just as potential profit or engagement to be maximised. Those companies will be the ones that create space for ethics within their organizations.
We, the data scientists, data engineers, AI and ML developers, and other data professionals, have to demand change. We can’t leave it to people that “do” ethics. We can’t expect management to hire trained ethicists and assign them to our teams. We need to live ethical values, not just talk about them. We need to think carefully about the consequences of our work. We must create space for ethics within our organizations. Cultural change may take time, but it will happen—if we are that change. That’s what it means to do good data science.