The topic I will talk about is I think one facet of the, the big data thing and an important element in how we see this entire big data discussion on big data story. I think as of now, if I Evers good try, I'm not. Yes. I AMS honestly, admittedly, I think that the, a lot of companies are driving big data from the vendor side because they want to sell big storage, big computing power, et cetera, cetera. So it's about really massive of data. And I think that's one part of it. It's Mike talked about it, not everything is, is really necessarily extremely big and that it's not only about masses of data, but doing the right things with the, with the data. And I think in some areas it's when we look at it, there are some other opportunities. So I've borrowed some slides from Mike and some of the aspects which are important within that are for ensuring for, for instance, trust and data sources, scalable privacy as aspects.
So how do we deal with these aspects and being smart in dealing with data, how big it might be is one important aspect, understanding aspects of data, purpose, ownership, acquisition compliance, I think is a very important thing. And things like Providence verification, very close to this informed content control of use very important aspects. And what I will mainly talk about and share, just share, try to share some ideas I I have in mind and we have in mind is really around not only, but mainly these aspects. And when we look at big data and I've applied this business impact indicator approach here, that big data has an opportunity to enable business. It has an opportunity to do things, to do things better in business, but clearly you shouldn't expect that you save money with big data. So there's a cost behind it. It might really help you to do things better in business compliance from a compliance perspective.
And I think that's what, what we will hear today. It's, it's not an easy area. Let's say it like this, but clearly compliance fulfillment is not a no brainer when it comes to big data, you have to think a lot about it. What does it mean is said, right? And when we just look at the fact that today the SAP business warehouse, which is sort of a small, big data is one of the favorite targets of every auditor. So because, you know, when I start there, I will have my findings. I will have done my job, et cetera. Then, then this becomes clear that big data doesn't make these things easier and simpler. And on the other hand, we have trends like live management platforms. And I think a lot of you have listened to the yesterday evenings keynotes, which were very well attended where it's really about doing things better with the customer, enabling new types of businesses, privacy by the sign in mind.
So it chose a little bit better there. And on the other hand, the API economy, which is around enabling new types of, of interaction, I will talk about buzz areas shortly, but I think, you know, if we take these three pictures and, and combine them, then the business impact of big data might look far better, then it does without taking this idea of smart data into account. So that's, I think one of the things where we really have to, to, to think about how to, to, to, to do big data, right? So that we really get better in all areas and have more, in fact, come achieve more success for the business. I think Mike brought up some great examples of that. It's not that easy to do big data, right. But there are great opportunities. And I think that some things can really help doing things better.
So when talking about the API economy and I yesterday had a discussion with a journalist and I fully agree her API economy, she said, I don't understand what is what it is. I think the term is a little bit techy. So it's the nerd term sort of things. And I personally trying to, to figure out a better term. So for my keynote presentation, I, I talked about the extended enterprise plus plus in the sense of it, which is the plus plus is techy. The extended enterprise is not that techy. Yes, it's, that's correct. But you know, we we've, I've had a discussion with Greg around these things. And we, we talked about things like the hyperconnected or the et cetera enterprise. We didn't figure out on the perfect term for that. So if anyone has a great idea for this, I'm very open to, to push this to the market.
So I, the API economist already, the it perception, which is rod, I expose API. So have an application. I expose APIs. They are somewhat somewhere managed developer uses it. And then he builds new things based on that, which if you look at some things like open government data, et cetera, frequently is around accessing big amounts of data in somewhere, not exposing information, draft API, Facebook, in fact, accessing some sort of big data. You could also say it's sort of, so plus plus again, the plus plus thing, it's, it's sort of, of making the promise of, so our work beyond the scope of the enterprise. So really making it work in a far simpler, far more flexible, faster, better way. So it's sort in fact, I would sort sort of the, the, the next generation. So from a technical perspective, however, business never understood. So for a good reason, because it's technical.
When we look at it from a business perception perspective than, than things are a little bit different, it's about how can I build better business processes? How can I orchestrate business processes? How can I enrich data? So by using information from Facebook, by using information from government data, by using geographical information, by using whatever I add value to data, I enrich data. So that's the, the, the lower part of this picture, where we use different types of tools. We enrich data. So making the better we built new mobile applications, we built new apps. Cetera was impact on the business processes on enriching data, on enabling collaboration with partners and customers by building new types of apps. So Kim camera and last year brought up the, the example of, you know, in Paris, you can obtain the data of the Metro. You can obtain the data of the, of, so the current location of the bus and the traffic situation, you know where to go and you could easily build it up based on APIs, which says to you, okay, right now, it's time to leave your house to be in time at your meeting point, you can do a lot of things best.
And that a lot of things are either adding value to specific data or using large sources of data to extract some of it. So there's a clear relationship to that. And that's one of the things. And then the other, other thing is this live management platform thing where, where we have this idea of having a platform in the middle, which so the user brings in his personal data in one or more personal data stores, the platform based on dress frameworks, reputation, frameworks allows privacy enabled and secure apps, privacy by the sign to work with this data and this data, which it receives from insurance companies, government, whatever. And with consent, it also might provide some data back, but having the data ending up in big data probably is not the smartest approach here. So we also could turn that around and say, okay, how does it help us the customer make decisions, deliver value to the customer? Do whatever the customer needs that might be by extracting this needle in the haste, like by extracting some part of this big data into smart data, providing it back, enabling the collaboration with the customer, with the citizen, whatever. And then again, big data plays an important role here because you can do a better customer interaction. You know?
So, so when, when I've, I've talked about, so I'm frequently asked, so family, et cetera, what are you doing business? And some years ago, tried to explain that I do a lot of things around identity and access management. Yeah. I probably appeared a little bit to beat the nerve. There was a very techy topic. Cetera, I think last, last week, yes. I sat together with my brother-in-law and we talked about life management platforms and yes, he immediately understood it because he, he understands the value in this, in this, from a personal perspective. And I think that's, that's an important point, but he also clearly identified. Yes. But if then all the data ends up there. So if the apps or the people consuming that are not acting reliable and according to the privacy agreements to what is in the trust framework, et cetera, then those things single struggle.
So instead of putting all it up into big data stores, having the attitude and saying, okay, we just put the smart part up and down clearly helps. So there's a tight relationship. I think of the idea of life management platforms of APIs, because somewhere in there are APIs. If you use the term cetera, that's really one of the things I, I wanted to talk and my, my ideas that we need to go from big data only to a smarter approach. So I think big data done right, is not only consolidating data into big data, processing it and ending up with something which might be the needle in the haystack you've been looking for or not. So a lot of fat pipes and one small pipe there, but it's about saying, okay, where do I need to consolidate data consolidate data? Which is your data, others maybe big, but probably more small data things.
You constantly date which you process and which you then enrich using APIs by accessing others, big or small data. The small data might be trust to derive from a personal data story within the life management platform and the thing which then really does the enriching might even be a part of the life management platform. That's not necessarily the big data platform anymore, which requires a lot of computing power. This part here. I don't think we have a later pointer here. This part down there, the enriching part I think is, is really where you say this can run everywhere. So it's about combining things about understanding, where do I need big data and where do I use the results of that to make better things out of it? And I didn't attend all of, of Mike's session, but probably you've brought up this example of just using big data to reduce the mass of records, to a number which you then come work on with traditional database technologies at a start of the same story.
Maybe gonna step further by saying, okay, we really need, when we talk about big data to understand where do we need the big data technologies and what do we do with this data to really end up with the smart information we need. So that's what I wanted to share as thoughts around big data from my side, the conclusions slide. So why, why the API economy and LMP help big data? The APA API economy are, are extended enterprise plus plus whatever. It'll be hyperconnected to access to additional data that enrich big data in other data from other services, you have a masses of providers for data. So you can add data for, we are thousands, 10 thousands of APIs, which provide you open available information, social network, data cloud directory services via craft APIs. And it's sort of a trust in time delivery of data instead of building large repositories.
And on the other hand, if you look at the life management platforms, that's the best way to enhance analytics with privacy related data. So reducing the number of data, putting it together with some personal information and then working on the relatively small amount of data in a secure privacy where environment you might receive the data based on contracts, defining the use policies, which is the less smart approach or by smart big data apps, which are in fact smart, small data or smart, smart data apps as part of your life management platform. So that's the, the idea, the vision I have. And I think when we follow that pass, we can make a lot of things better around using big data. Thank you.
I have a question. I didn't understand one thing so well, okay. If you are doing big data. Yes. So you need sources for that pieces of information. Now, if I do life management platforms, I stop basically I stop the leakage of these small pieces of information. That's my primary concern for actually using life management or, or thinking about life management platforms. So what will be the big data then be fed with if that source is away?
I, I think it's always about, you have some part of your big data, which, which you own. And, and instead of saying, you know, I had a second piece of big data with all the privacy sensitive information. Why not saying I have this big data, I have some other information and I relate it sort of at run time. So I think this is more from moving from a static building, big data repositories approach towards this dynamic approach at runtime, which is by the way, one of the basic ideas of big data to enable information at runtime. So
Therefore you need this computing power.
Yes. But it's not contradictory. You know, if you do a good reanalyze, the sort of thing, and then a final combination, then I think you can do a lot of things. And my impression really is when I look at many of the use cases that there's put too much emphasis on the how to do it, how to, to, to get all the data instead of putting emphasis on the result. I think that's
Okay. So, so that would relate. I, I, this there's a conference on privacy by the way, these days also in Berlin. And there was an interview this morning on TV of a professor, which was asked, Hey, this is all this big data out there. And how do, how to address this actually very convenient. And he said, well, you know, big data is an interesting approach. He was advocating for privacy. The big data is not smart enough today. So for example, if I'm searching for yellow runners, so kind of shoes, yellow color. So, and I went to the shop, I, I searched for them. I went to the shop, I bought them and I still will get for three weeks advertisements for yellow runners.
Yes. Horrible. You know, you buy, you buy cooking field with induction at Amazon and they try to sell you the next one, instead of selling you the, the tip for the, the, the, the additional items you will need. And that's, yeah. That that's far away from being smart at all. And so if, if, if someone is willing to tell you the intention, and I think it's very worse to read a book of doc sales around intention economy versus attention economy, I think, and that's where life management platforms again, come into play. And I think, you know, I think big data currently is driven by vendors who are interested in selling big stuff. And I think we should look more at the analytical part of it instead of the storage part of it.
Yes. I, I don't, the storage will be a way, I mean, this is even among the big, the big vendors is the storage thing is, is gone. I mean, the, the, the big message is in memory computing. So you do everything online in time at the minute. Right. And, and then it makes sense actually also to connect via the API economy.
Exactly. That's what smart data smart data works. You know, that's the point there, it works because you then have the, the situation that you say I have reduced the massive data to an amount, which I can process in memory, then I can work efficiently based on APIs by enriching it by adding value.
Yeah, absolutely.
Okay. Yeah.
And to the term missing. So, I mean, API is techy. Economy is business, extended enterprise is business plus plus is techy techy. So we need to basically to sell it probably need to find words which are under understood in the business environment. The only thing that came to my mind, it's not good, but, but it would be service oriented economy.
Yes. I will think about it. Something around that somewhere. We will end up the extended service oriented hyper. No, we
Have to it again. And then we are back to the cloud. Yes. Okay. Thank you very much, Martin.
