>>How important is this increasing amount of data this better tools for data analytics in FinTech markets? >>Now, a lot of those data are actually useful for analyst Now, it's really to look at the pendulum Do you prefer less data or more data? A lot people probably say that more data >>More data >>Yeah. At least, I have the choice I have more information to confer to clean it, to filter out the irrelevant part, or the noise part At least, I have a pool, instead of have nothing or have a just only one or two source of data then it's no way for me to know the true picture of it So, I think people, in general still prefer more data Also, ones with more data then more people will able to be build up a lot of analytic engines to analyze to clean the data, to look at the quality of the data >>But I have heard it said at least in the context of regulators that regulators are used to thinking we need more data we need more data, we need more data And it overwhelmed with too much data today So, what do they need when we get so much data so much big data so many sources of data What do we need now? >>Yeah. Okay. I think the next thing is data cleaning It's really a tedious process, and very often a lot of the managers will say that "Yeah, we do a lot of big data analytics, etc." They overlook the importance of cleaning the data >>That can cost more than the analytics itself >>It could be. Yeah. I use porting example for example, on the exchange rate Now, in some countries if the government decided to dis-encourage high frequency trading >>Right >>So, instead of releasing tech data they could release second data Every second they release within the second there are let's say 100 transactions without any time series sequence So, it's up to you to figure it out Okay. Or they purposely randomize it Okay. So, now, it's up to you whether you consider this to be a fair approach because for people that doesn't have super computer or advanced computer, because it is fair >>They don't care >>Right. That's okay, right? To me whether the data coming take up microseconds or seconds to some of the general retail investor then it's >>Frankly, give it to me in days. I don't care I'm not going to make a decision in less than a day if I'm a small traders, so yeah >>So, that's why regulator do play a role in terms of data quality as well So, it depends on how they regulate Also, for data cleaning or data, so-called ''reconstruction'' then it's up to the data analytics to look at that seconds and try to find out the sequence of those tech data within the second >>A lot of companies talk about big data today and a lot of companies talk about analytics One of the things that surprises me is how many companies are talking about big data that actually have small data They don't have that much data So, a client may come to me and say "I have data on all the hotel stayers and all my hotels who are members of my frequent flier club." And I'm thinking, your whole dataset for 10 years is less than one hour of stock trades on the stock market for one exchange So, the amount of data we're talking about that's already pretty clean in financial markets is huge compared to what it is for a lot of companies >>Right >>So, I would say that makes data analytics incredibly powerful for financial markets because we have such clean data We have such high quality data that it's the collection the coalition, and the analysis of that data that starts to create a lot of the value in finance markets >>Yes >>Now, that's not true with peer-to-peer lending It's not true in some other things where now we're looking for social data or insurance we are looking at risk data So, it depends on which FinTech market we're talking about >>Right. Yeah. Because especially in the finance market we talk about time is critical in terms of the data Also we're talking about the real time data Now, even comparing in finance market even compare the insurance data and the so-called the stock data. Right? >>Right >>I mean, you don't, for the insurance quote you don't change your auto quote or your parents life insurance quote every second, right? >>Nor me changing my medical prescriptions very often >>Actually >>So, that's small data by comparison to stock quotes >>Right. For in a stock quote, every seconds the trading price of the stock could fluctuate ups and downs within 00.1 percent But if you could do arbitrage you could do a lot of other techniques and that 00.1 percent could make a lot of difference >>Also, in the foreign exchange market as well There is something that could make a difference in big data in health insurance or life insurance and that is if we get wearable technologies that are more mature, Internet of things I could know what have you ordered what have you eaten how much have you exercised how much insulin did you inject over the past year Many other things that could affect my analysis of your risk >>You raised a very good point, yeah For insurance data, on that particular prospect is that we always consider data privacy issue >>Yes >>It always involve personal data as compared to the financial data on the stock market So once it involve personal data basically, it's two approach One is the person willing to give you his or her data because you are offering him or her some incentive Let's say if I offer my health data or I'm going to wear something then you give me a little premium So that's incentive, so that I'll be able to, for example if I let you put in a device in my car to monitor my driving manner then probably you would reduce the insurance and I believe I'm a good driver then I don't mind you to capture those information The other part is I probably don't want to release my personal information as much as that of exactly how I drive in details but rather I'm still releasing some of it for you to do analysis. For example I mean >>Especially, if you give me a 10 percent discount >>Yeah. Some of it, yeah. Exactly >>If the price is right maybe I'll give up some privacy >>Right. Yeah, and some of it and also you could use those data to do other further analysis of coming up with new insurance model So, you may have a particular insurance model for particular type of people in a particular occupation. Okay, maybe teacher I don't know, I'm just putting a hypothetic maybe after you study, oh Teachers and professors, they tend to drive safely or whatever sort of types of people working in a particular occupation >>I don't know about that I'm not so sure that that's accurate But okay So, into summarize we have a lot of data, particularly in financial markets Data analytics matters This is one of the biggest opportunities for data analytics because we have clean data We have high quality data for stock trading and that makes up big opportunity for data analytics that's interesting But we also have a wealth and breadth of data in many other areas at FinTech and if we can do better data cleaning and get the quality of that data up we may be able to do things that we could never do before >>Right. Yeah, and also the dissemination of data is very important Like every morning, you turn on TV there are a lot of so-called financial analysts >>Right >>They are talking about these and all that They are actually influence the data It's a circle, right? They influence the data that your are collecting that date So they may influence the sentiment of people. >>True >>They influence a lot, yeah So, that's why the whole data life cycle also include the dissemination Once it disseminate, you collect it again clean it again, etc. >>So, even if it's fake news or neutral news just the reporting of the news can influence people's sentiment and therefore, influence later news >>Right >>Okay. Well, thank you very much We'll look forward to having you in our next session when we talk about AI, DSS, and automation >>Great. Thank you >>Thank you
