How widely companies share data.
Katharine Schwab discusses
PayPal’s recent spreadsheet
which outlines all the third-parties with whom they share customer data. 🔮 Designer and researcher Rebecca Ricks neatly visualised
this information in a new tree diagram — recommend exploring it to very clearly see how many companies PayPal are sending their customer’s data to (including name, DOB and photos):
It would be a great resource for consumers if all companies that collect and share their customers’ personal data were up-front about how they were sharing it–and better yet, if they presented it through this kind of visualization, which makes it much easier to comprehend the overwhelming scale of the list and dig into particular categories.
A recent SAP Hubris survey
found that 66% of US consumers expect companies
to “be transparent
about how the data are being used with partners”. They are happy to share information, but in return expect for understandable and honest communication around how their data is being used, where it is being moved to and who is accessing it
Threats galore for centralised data.
🙀 Interesting article debating
the “safety of people’s privacy” when data is pooled
in a central location, like in India’s leaky Aadhaar system:
There are huge legal implications regarding the privacy of the data. Because the data is now available in a long trail linking a person’s every habit, the government has total access to his entire record, including health, sexual preference, etc. The consequences of government agencies having access to all information on a person are not measurable, particularly when it comes to mala fide action. The protection would, however, come under Data Protection laws that will have to come into force so that the individual does not suffer. The scary part is not the legislation but the follow-up by courts and law enforcing authorities. How effective would that be once a person’s digital footprint has been exposed?
3 ideas for 2018 to stay ahead of competitors
. Kasia Moreno, editorial director at Forbes Insights, discusses the top themes for this year
and how companies can use them to as differentiators. Moreno emphasises how “this is going to be the year of data
” — due to the GDPR coming into action, the growth of smart data sharing and companies creating business value from customer data. 💯 Importantly:
Companies have been focused on changing their business models from selling products to selling services, based on data. For example, a maker of a product, if it has data about the use of that product from its customers, can advise on maintenance, thus cutting down on downtime. Since the maker has historical product performance information about both the maker and the client, the trove of knowledge to share and use to improve business is quite vast. This means the ability to add to ongoing services. The trick is to figure out which data and which services the client will find useful.
But this customer most probably also shares and obtains data from other companies, and some of it may be even more useful than the data from the product maker. What’s more, this useful data can often come from a relative startup in the industry, which does not have historical data at all, but finds one useful piece of data — sometimes publicly available — to mesh with the client data and provide the most useful services. I call such data phantom data. It emerges unexpectedly, out of the blue — even though it was there all along, only impossible for many to see — and instantly seems both obvious and visionary. Companies must be on a constant hunt for such phantom data.
Is your company’s data actually valuable in the AI era?
🤔 Writing for Harvard Business Review, Ajay Agrawal, Joshua Gans and Avi Goldfarb argue that data is helpful to build prediction machines
— but not operate them:
The data you have now is training data. You use that data as input to train an algorithm. And you use that algorithm to generate predictions to inform actions.
So, yes, that does mean your data is valuable. But it does not mean your business can survive the storm. Once your data is used to train a prediction machine, it is devalued. It is not useful anymore for that sort of prediction. And there are only so many predictions your data will be useful for.