The very mention of any of these three terms can make the layperson cringe. Or run to a favorite search engine for clarification. That dusty old Oxford English Dictionary on the bookshelf will be of no help.
Even a voracious reader of financial and technical news may scratch their head when asked to explain what each means. They might think they get it, but can they articulate it?
Let’s first look at FinTech. That is a problematic place to start, as one source describes FinTech as a “portmanteau” of some other things. We have hardly even begun, yet we already have to rush to the Oxford English Dictionary.
For the record, portmanteau means combining two terms into one. Why couldn’t they just say that? It can also mean a large suitcase, but probably not in the context of FinTech.
FinTech, or fintech if you do not want to make it look fancy, is a word that emerged in common language around the time of the 2007 financial collapse. Investors then put their money into financial technology instead of dicey traditional financial institutions. Investors like to talk fast, so they crammed financial technology into one word.
The term began as a phrase related to back office technology at banks and the like. FinTech has evolved and expanded to address a broad range of technological innovation in the financial sector. That covers a lot of stuff, even funky forms of money like bitcoin. We dare not address bitcoin here because we have more than enough to do.
This takes us to Big Data, two very recognizable words. Fortunately no one felt it necessary to remove the space in between and smash them together to create an annoying portmanteau.
One could reasonably assume Big Data to mean data, just a whole lot of it. One would not be far wrong, but we can not leave it at that. What it really means is kinda like this. So many more people collect so much more data so much faster than ever before we do not know what to do with it all. Since we can’t figure out where to put it, let’s at least create a new term for it. That way we can feel like we accomplished something.
Now that someone named it, we must figure out what to do with Big Data. That means some machine more sophisticated than your old desktop PC with floppy disks. We need far more advanced technology to collect data, process it, analyze it and transfer it.
While we are at the task of transferring Big Data, we must decide whether or not we want to share it with others or just move it from one place to another. This is occasionally a touchy subject, especially when something gets shared when it was only supposed to be moved. Kinda like a leaky oil tanker.
Working with a whole bunch of data takes us to our third and final uncomfortable word, analytics. Analytics also became a fairly commonly used term sometime around 2007. It should be obvious, but we needed a noun better than analysis to describe all of the analyzing people were doing with all that data.
Analytics also covers a lot of ground, but we will try to keep it simple. We must first figure out what we need to answer a certain question. We then determine what data we need to get to an answer. Someone must collect and process that data, then figure out what it means when the data gets crunched. Analytics is pretty much on the front and back end of that process.
One of many things analytics is good for is to remind us of old sayings. We have all heard you can manipulate data to come up with any result you want. We can counter that with another saying, garbage in, garbage out. An expert in analytics will quickly detect if someone put garbage in, and that will destroy the reputation of whoever put it there.
If we really wanted to get into the weeds we could talk about the differences between predictive, prescriptive, descriptive and cognitive analytics. We will refrain from doing that, not only because we are reaching maximum word count, but also because we have no idea what those different types of analytics are.
Yet we do know this. FinTech, Big Data and analytics are increasingly important to do a lot of things a lot better than we have ever been able to do them before. Whereas we used to have to somewhat guess, we can now know and predict things with far greater certainty. Whereas tricksters used to be able to fudge, it is exponentially harder for bad actors to get away with such trickery.
The culmination of all this leads to artificial intelligence. We are waiting for our desktop PC with floppy disks to write that article without us having to do any work. It is a good thing we are patient.