Big Data

00:09 We're now moving on to the big data megatrend.

00:11 I'm sure you've heard or read about this term, and perhaps even actively used it, but the major question is: What is big data? And in today's context, this term is more of a buzzword that refers to analyzing enormous amounts of data and then utilizing it.

00:31 So we're talking about big data - vast amounts of data, and when I say large amounts of data, I don't mean a box full of data, but rather an infinite quantity of data.

00:42 So there is a massive amount of data per hour from here to the moon and back.

00:47 When we talk about machines and how factories constantly give out data - every second - and make it available to us as a data package, the question is, how can we assess that? Yes, there is another phrase for big data.

01:03 We're also discussing smart data or intelligent data.

01:08 How can we extract intelligent things from enormous volumes of data, or big data, in order to use it? The important question now is, where is this data being gathered? Where exactly is it coming from? And, of course, this data is collected at every interface within the companies - keyword industry 4.0 - so we have it in logistics, purchasing, the production hall, shipping, later with the customer, at every interface, and also in manufacturing itself, in logistics itself - so everything is collected everywhere.

01:48 The amount of information available is increasing and is being stored as big data.

01:53 These are the businesses.

01:55 However, the firms also collect, and this is where you and I come in - they collect our data, so we are essentially becoming data contributors, which is exciting.

02:09 You're surfing the web, and everything you do there, including the websites you visit and the time you spend there - whether it's five minutes, ten minutes, or twenty minutes - is being recorded.

02:22 This information is provided by us.

02:24 You're walking from point A to point B; you believe they don't gather data, but you have your smartphone in your pocket, and the smartphone knows you're traveling from point A to point B. We contribute all of this data, and we do so everywhere.

02:40 The banks know what we spend our money on, where it originates from, how much we invest, and how wealthy we are.

02:47 This is our data, which we donate and make public.

02:51 To recap where and when this data is collected, it is everywhere and at all times.

02:58 Now we'll look at some examples to help us comprehend the idea of big data, and I'd like to start with e-commerce - online buying.

03:08 You've probably purchased a few items from the Internet.

03:12 I'm doing it all the time, and as a result, we're contributing data again.

03:18 You may be familiar with this category: customers who purchased this product also purchased such and such, and this recommendation is produced from the huge data streams being reviewed.

03:33 That is, if I buy an espresso machine with capsules, likely, I will soon buy capsules or a decalcifier for this espresso capsule machine.

03:45 That makes sense, and then the AI, Amazon's artificial intelligence director, remarked, "We basically already know what you'll want to buy next." And here it is, without your knowledge, and it has alarmed many people.

03:59 They're thinking to themselves, "Oh my gosh! How is this possible? "How does Amazon know what I'm going to buy next?" A similar principle was applied here.

04:09 It is simple to deduce that if you buy anything and 50% of people who buy this product also buy the following product.

04:17 You may predict that you will buy the next item with a high probability, possibly 50%.

04:23 And this is how big data functions.

04:28 Navigation systems are another example.

04:30 You've almost certainly used a navigation system, perhaps not as the driver, but as a co-driver or passenger, or you've walked through a metropolis and someone informed you, "Yes, you have to go this way." That means you've almost certainly used a navigation system at some point.

04:47 Previously, there were several manufacturers and systems that one day realized and stated, "Gosh, we need to stack the systems on top of each other." We need to integrate the systems so that we can better utilize individuals." That means everything grew bigger - huge data times 10, times 20, times 30, and so on - and Google Maps is now the world's largest navigation system.

05:11 We're all staring out the window.

05:13 Yes, we have to travel this way and that way, and by looking and then going this way, we provide all of this info.

05:20 Then everything makes sense, and navigation systems are growing more detailed.

05:24 There are complaints of traffic jams: it is 50 meters long - a 50-meter jam - because they discovered that a car that takes 20 seconds at this traffic light now takes two minutes and 30 seconds.

05:35 That suggests there's a traffic jam there, and it's not because you called in, as in the past. The roadway seems congested, but it is because the car requires more time.

05:46 That is the primary distinction.

05:48 The intriguing part is that cars are driving a path that a navigation system - in this example, Google - knows about, and many cars stop at this restaurant or café.

05:57 As a result, this café or restaurant must be good.

06:01 It is highlighted, and you look about and wonder, "Are there more cars driving there, or are there fewer?" Oh, more cars are driving there, so the restaurant must be excellent." Then we inquire, "Hey, you stayed at that restaurant for 30 minutes." What did you think of it? "Would you like to leave a review?" So, based on the navigation systems, they've installed a slew of new systems to the left and right to capture even more data and make the system as a whole more valuable.

06:27 That is essentially a reciprocal interaction between giving and taking.

06:32 The final example comes from the field of medicine.

06:36 You could make the case for Google in order to make humans live longer lives.

06:41 Google predicts where the next flu outbreak will occur.

06:45 Why? Because we Google the flu - we may Google the keyword "flu" directly, or we may say "flu" and "home remedy," or we may ask, "When do I have to visit the doctor if I have the flu?" And if a significant number of individuals do this in a certain place in a short time, Google knows that a flu epidemic is brewing here, and some departments may be contacted. "Whoa, something might be developing there," they could say.

07:11 Can't you maybe close the kindergartens and daycare facilities so that the children don't infect each other? Couldn't you speed up flu vaccinations at nursing homes and care institutions so that these folks are protected?" On a higher, more significant level, the goal is to perhaps treat diseases that cannot be treated on-site.

07:31 A good example is a rare kind of cancer, and none of the treating doctors can help the patient because this form has not yet been recognized - therefore these ten doctors have no idea how to help them or which medication would be beneficial.

07:46 There is also a system, a large data system, such as IBM Watson.

07:50 They attempted to digitalize all medical knowledge and synthesize it in enormous volumes of data to draw conclusions.

07:57 Then you realize there's this type of cancer with these bizarre, undiagnosed symptoms.

08:03 Also, none of this makes sense or appears to be connected, although a similar case occurred in Brazil. There was a doctor who successfully treated it.

08:11 Suddenly, you have this interface, this connection, and it may save your life because knowledge from one corner of the world is now available to you.

08:20 That is big data, and it may help us live longer lives.

08:27 Finally, there is an appeal to businesses.

08:30 Consider which data you require, what you require this data for, and then begin to collect gradually.

08:38 Then you assess your findings and draw your conclusions.

08:46 If you don't work in a corporation, aren't a corporation, and don't work for a large company and think, "Oh, big data, that is pretty big; that might be too big for us," then it doesn't have to be big data.

09:01 Everything should be based on cost-effectiveness.

09:04 Everything must be accessible at the appropriate speed.

09:07 For example, if you have a large data query and you wait two weeks for the result, this is not wise. It's not very clever.

09:16 That is why there is also the option of working with modest amounts of data.

09:20 To be clear, little data refers to small volumes of data, which are the polar opposite of big data. For example, in terms of sales volume, you may call 20 consumers today and ask them one question and get their answers.

09:34 Those 20 responses that you may then receive are little data, and they may be more relevant to your business, and to your organization, than establishing large data structures. Finally, you must ask yourself this question and come up with a response.

09:50 Finally, you will be able to summarize.

09:52 It truly must make sense.