Data sharing is as old as language. So why now are we so concerned about it?
Data Sharing Is Not New
Back in the 50’s, 60’s and 70’s the marketing juggernaut that was Reader’s Digest used to send out surveys several times a year to its customers. The surveys asked about salary range, number of rooms in your house, how many cars, how many TVs, children, pets. People would not only check off the appropriate boxes but they would add comments to their answers. (Dear Readers Digest: we have 2 color TVs, an RCA in our bedroom & a color 25-inch RCA is in the living room). At the time Reader’s Digest was right up there in trustworthiness and Americana with Mom and apple pie.
Why do I mention this? Because people have been sharing data forever. And they are willing to share ever more personal information with those they trust. As a result of these surveys, Reader’s Digest had a zip code profile (average salary, average mortgage, credit card debt, etc.) that the government repeatedly pursued. They never got their hands on it. It was chock-a-block with an incredible wealth of information. I know, I was party to it and I used to read the survey cards, shaking my head at the treasure trove of information in the comments.
Reader’s Digest’s zip code file would pale in contrast to some of today’s big data efforts. It could, however, stand on its own for the sheer depth of information people willingly provided. People shared data because they trusted who was asking them and they believed they were telling trusted friends when they opened up. And Reader’s Digest worked very hard to establish, maintain and retain that trust. The same cannot be said for Big Tech. People like the conveniences offered but in survey after survey Big Tech lags behind many other industries in terms of trust.
We share willingly with those we trust
Part of the reason for this is the depth of private data companies seek and the invisible -and obtuse- methods they employ to obtain it. They ask for critical information to access to your data (financial, medical, personal) so that they can provide you a service or analysis that will solve a problem you think you might have. As you grant that access, they are additionally measuring the way you interact with their apps to understand your age, sexual bias, race, response time, agility (mental and physical), likes, dislikes, bio patterns, etc.
In many respects it’s a Faustian bargain, we win short term, tech wins long term. There is a need -now more pressing than ever- for total transparency from tech companies. To gain our trust we need to know:
- what data do they collect
- how do they use the data
- what data do they sell
- what data do they trade
- to whom do they trade and sell data
- for what purpose do they sell, buy and trade data
These questions lie at the heart of data transparency. And since our focus here is on financial data, we will focus the remaining discussion on data transparency within financial services: why we need it, what financial institutions, regulators and fintechs are doing about it and how you can help.