Youtube News: Youtube daily report w May 28 2017

Under rich, relentless skies I've been setting highs

I felt you walk right through me

You're the thing that I invoke My all persistent goal

Sent to make me queazy

And oh, it's hard now

With time, it works out

To be human is to love Even when it gets too much I'm not ready to give up

All the tigers have been out I don't care, I hear them howl I let them tear right through me

Can you help me not to care? Every breath becomes a prayer Take this pain from me

And oh, you're so far now So far from my arms now

To be human is to love Even when it gets too much I'm not ready to give up

To be human

Just 'cause I predicted this Doesn't make it any easier to live with

And what's the point of knowin' it If you can't change it? You can't change, can't change it

Just 'cause I predicted this Doesn't make it any easier to live with

And what's the point of knowin' it If you can't change it? You can't change, can't change it

To be human is to love Even when it gets too much

I'm not ready to give up

To be human is to love Even when it gets too much

There's no reason to give up

Don't give up

For more infomation >> Sia - To Be Human ft. Labrinth (Official Audio) | Wonder Woman - Duration: 4:06.

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Messaggio per la vostra notte. 28 Maggio - Duration: 1:02.

For more infomation >> Messaggio per la vostra notte. 28 Maggio - Duration: 1:02.

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#233 Digital Transformation and Data: AI, IoT, Analytics, SAP Leonardo (from SapphireNOW) - Duration: 37:41.

Welcome to Episode #233 of CxOTalk.

I'm Michael Krigsman, and we are streaming live from SAP's big user conference called

Sapphire Now.

And before we go into this discussion, I just want to say "Thank you" to Livestream

because Livestream is our streaming platform; and man, those guys are really good.

And if you ever need a streaming platform, go to Livestream.

They're really good.

So, thank you, Livestream.

So, we're here at Sapphire Now, and I have the privilege of speaking with Mike Flannagan,

who is deeply involved with analytics, with data, and with something new that SAP announced

called Leonardo.

Mike, how are you, and thanks for being here!

[…] Thanks so much for having me!

So, Mike, you're deeply involved with analytics and with data, and with Leonardo, so tell

us about your role and what do you at SAP.

So, officially I'm the Senior Vice President of products for analytics.

And now that we have launched SAP Leonardo, I have also taken on the role of Head of Products

for SAP Leonardo.

And, we had a big announcement this morning by our CEO Bill McDermott.

So, I really want to dive into the issues around data and analytics, but very briefly,

tell us what is SAP Leonardo?

So, it is officially a digital innovation system, but the idea behind SAP Leonardo is

fairly simple.

Everybody struggles with business problems, particularly now with the pace of change and

the need for transformation of digital business.

If you're in retail, problems that you have are not that dissimilar from problems that

your peer companies have.

And the solutions to those problems from a methodology standpoint and a technology standpoint

also have a lot of commonalities.

So why does every company have to feel like they're reinventing the wheel?

And Leonardo is intended to help accelerate digital transformation for companies by leveraging

the SAP's experience with other companies to help them solve the same problems using

the same methodologies and approaches.

Obviously, there's some customization that's involved in the company, but you start with

a nucleus that is able to accelerate solving the business problem.

So there's this combination of technology and business process that kind of move together?

Well obviously, nobody in the C-Suite bubble stands up in the morning and says, "I want

to go buy some digital transformation."

Exactly.

They're thinking about, "How do I improve revenue growth; how do I improve bottom-line

profitability; how do I improve customer experience," so, when you look at those things, you can

break them down into a set of fairly digestible business problems that need to be attacked.

So, if you can very quickly move from the first problem to the first solution, and then

you attack the second problem and second solution, you can move your company along a maturity

curve until you become a fully digital business from a post-transformation … all the way

across.

But if you start saying, "I need to go change everything tomorrow," that seems like almost

an impossible task and a bottomless pit of money.

So, it's important that customers be able to take a little step and see the results

and get the return on that investment, so that if they feel confident taking the next

step and continuing […]. Mike, I think we should begin with a discussion

of data.

And we heard in one of the keynotes this morning the phrase "Data is gold," and we hear

similar kind of sentiments all around the industry.

And so, with digital transformation, let's begin with this notion of, what is the relationship

between the data and the ultimate digital transformation that takes place?

There are all kinds of great analogies in the market; that data's the new gold; data's

the new oil; whatever you prefer, data's a very valuable asset.

And, if you think about your data, that you think about your human capital, you think

about the way you think about your real estate investments, you start managing it as an asset

that has a lot of business value.

Then, you start realizing the transformative power of doing things with that data that

you couldn't do before.

And then, of course, everybody's talking about IoT or the industrial internet, and

that really is about opening up a whole world of data that you didn't have before with

sensors and wearables, and those sorts of thigs; and the transformative power of that

data become exponentially greater because so much more data from which you can draw

insight [becomes available].

So, talking about collecting data from many new sources that even a few years ago were

really hard to imaging; can you give us examples of some of the new data sources that are available

to us?

Sure, absolutely!

I mean, and of course, I think it's worth noting that is it's not just new data sources.

If you've been running your business for a hundred years…

Good point.

… you have a lot of really valuable enterprise data.

I think the power of things like industrial IoT is adding to that some data from new sources,

and so you think about data from sensors.

We've got examples of train companies who outfit the brake systems of their trains with

sensors so that they can measure break wear.

In fact, my car has sensors on the brakes.

It doesn't send me an email, but it gives you a little display on your dashboard.

Everybody can sort of relate to that little e example.

Now imagine you're managing like Trenitalia does to thirty thousand locomotives, and you're

trying to minimize the amount of time that you're out of service for maintenance, both

to decrease your downtime costs, but to improve your customer experience by having trains

running on the tracks.

The ability for them to just add some sensors to monitor a little bit of data about maintenance

really gave them the ability to transform the business process around predictive maintenance.

Sensors are really one example.

Wearables are a new data source.

And you know, I think if you consider those types of data sources, you could imagine what

the future might hold of all kinds of different wearables, embedded sensors…

Video is becoming a really powerful new data source; deep learning starts becoming a more

mature technology.

So, it's an incredibly interesting time for data people.

And these data sources have the power to shape and mold processes.

I mean, just, for example, last week on this show, I spoke with the Chief Marketing Officer,

the CMO, of Aetna, a huge insurance company.

And, he was talking about how they can take wearable data, just as you were describing,

and feed that back to patients in order to increase patient wellness.

So, can you elaborate, then, on the linkage between having these data sources, and changing

processes and even changing business models?

Well, you know, it's interesting.

In the enterprise world, we talk a lot about the business outcomes.

In the Aetna example, what you're talking about is patient outcomes; human outcomes.

Exactly.

If I can improve as a doctor; if I can improve the outcome of interacting with a patient

to extend their life or extend the quality of their life, I mean, that's really exciting.

You know, it's interesting to have business outcomes with more profit and more revenue.

But you know, when we start injecting some of the human discussion about the power and

the potential of this data, we start realizing we can really change the world.

We can change society, we can change the quality of people's lives, and all of that is starting

to be made possible by these new sources of data.

They give you new insight into people.

And, before we go on to the next phase in, shall we say, the life cycle; so, we collected

this data, how do we then start to use it?

Can you give us an example of existing corporate data that we can find new uses for today?

Sure.

So I think if you look at loyalty card data in retail, there's a lot of information

there about purchasing history, purchasing preferences, which stores do you tend to frequent,

those sorts of things.

There's a lot of rich data there.

Historically, it's been used for things like sending you coupons.

But, there's a lot more that can be done with that, particularly if it's augmented

with some new data from new sources.

And, so I think there's a lot of value in the dataset that already exists there, and

as you start thinking about how to augment that with new data, the power of both really

becomes much greater than the sum of the parts.

Okay.

So, we've not got our existing corporate data, that we can make use of in new and better

ways because we can now aggregate it; and we have things we can do with it that we couldn't

do before.

And so, what can we do now with that data that historically, we could not do?

Because it seems like that's the thing that unlocks the power of that existing data.

Yeah.

I think there are advances in analytical techniques, things like machine learning; you know, lots

of industry buzzwords; excitement around machine learning, these days.

The power of machine learning is that it really gives you the ability to go back into data

that may be two, three, ten, twenty years old, and take all of that history that you

have about customers and store operations, and a variety of different things, and turn

that into training data, right?

To teach the machines what a customer looks like.

What does a good customer look like?

What does a bad customer look like?

What does fraud look like?

Those sorts of things require processing the quality of data from which you learn that

a human would be incapable of dealing with, right?

So it has to be about using the power of machines.

And then, obviously, there are examples in manufacturing here you take that learning

and turn it into artificial intelligence; things like robots.

But, there are also examples in customer service, for example, with chatbots, where now I want

to ask a few questions to my bank, and instead of having to have a teller answer the questions,

I can just go online and chat and get automated responses that are amazingly accurate for

the questions I'm asking.

Okay.

So, there are all these things that we can do with the data, but how do we prepare that

data?

How do we prepare …. So, we're collecting that data, we're doing something with it,

and then it can be used in the applications you were describing?

I think there are a couple of different ways to answer that questions, but one that I think

is particularly of interest for a lot of our customer is, when you talk about leveraging

a huge population of data from which to learn, there are concerns about privacy, and there

are concerns about data protection.

And so, one of the things that is, I think, important in every conversation about large

datasets is how do you anonymize that data?

How do you protect the personal information that is contained in that data, how do you

make sure that your policies are such that you only is that data for its intended purpose?

That having been said, part of preparing the data is sometimes normalizing the data so

that things look common across a large dataset.

Also, anonymizing that data.

And so, when you take an aggregate, you can use that data for, let's say, benchmarking.

The power of the average price that I should expect to pay for a bar of soap: I can collect

data from hundreds of different sources.

Some of them may express it in dollars, some in Euros, some in different currency….

I have to normalize that data so it's all a common currency, and then I aggregate that

data.

It doesn't really matter whether the data came from Retailer A, or Retailer B, or Retailer

C, so I can anonymize that part of the dataset.

And, what I'm really looking for Is, "What is the average price in each city, each country,

per bar of soap?"

And then the value of that is a good benchmark for retailers that market to use, but you're

not using any data that's specific to a retailer in a way that's identifiable for

the […]. And where does that normalizing and anonymizing

take place?

Does it take place inside the customer walls?

Does it take place on the platform side, like on the SAP side?

How does that … What's the mechanism for that?

And then for the benchmarking as well that you were just describing?

It really depends.

So, one example of aggregated, anonymized data that is being used for benchmarking is

in SAP Fieldglass.

It's an application that we make available to customers to deal with the contingent workforce.

And, if you look at Fieldglass, we see hundreds of thousands of transactions every year for

people who are looking for jobs, and people who are hiring for temporary workers.

Inside of that application, we can now aggregate and anonymize the data so if I say, "What

should I expect to pay for a salesperson in these three cities?", we have that data.

And we can make that available to customers as live insights in real time.

When they're thinking about what is the right labor rate to offer for this role, they

can see what is a common labor rate that will get them a well-qualified, talented individual

to fill the role in a reasonable timeframe.

So, that kind of data would be aggregated and anonymized and injected back into the

application by SAP at our level.

But, we have an example here, actually, at the Sapphire conference.

Our SAP data network folks are talking about something they're doing with a very large

elevator company, and that data is that customer's data.

So in their case, aggregated, anonymized, and used on their premises in their systems.

And I have to assume that this benchmarking capability, either real-time, in order to

look up … So I want to hire somebody and what are the labor rates, for example, for

this type of position?

Or, historical, "I'm thinking of doing something and I want to know how did we compare

in the last six months to our competitors?"

So, I have to assume this is extremely valuable and this is what customers want.

It seems to be for sure something that we're getting more and more requests to make available.

What I think is interesting is not so much … I mean, [it is] certainly interesting;

the raw benchmarking … What I think is more interesting and what we hear more of a customer

saying, "If you could do more like this, it would be great," is … So I know that

I have a certain budget, and I know I have a certain set of needs, and that set of needs

materializes for me five skills that I need from an individual.

But when I go look at the benchmarking data, the five skills that I need in the market

that I need them in, twice the budget that I have available.

Well, that's not very useful.

All you've done is tell me that I can't hire what I need, and so now what?

I can't afford the thing I want to buy.

That's right!

And so, the more useful thing in that scenario, I think, is to be able to say, "What if

I could compromise and only get three of the skills that I really need?"

Maybe I can teach those other two once the person's on board.

And if that fits my budget, then that becomes sort of a win-win, right?

I get somebody who doesn't do quite what I need; the data they join, but I get them for

the labor rate that I can afford, and I get the opportunity to teach them the things that

they need to come up to … That kind of benchmarking also gives you the ability to say, "Well what's

my next best option?"

And where would this type of calculator be built?

Is this built into the HR application?

Are they doing this new, dare I say, in a spreadsheet?

[Laughter] So, in a spreadsheet is typically how this kind of stuff is managed.

We go out, we take the big salary survey, and we pre-populate a central repository;

generally a spreadsheet, of benchmark labor rates.

That is what we are helping customers move away from.

If you want to run a live business, that's not very real.

And so, the Fieldglass application … And prone to errors .. I didn't mean to

interrupt, but there's a lot of problems with spreadsheets.

But anyways, I didn't mean to interrupt.

I'm sorry.

Oh no!

Absolutely right.

But, I think the key here is that we're injecting that information back into the Fieldglass

application so that it's right there in the workflow when a customer is trying to

populate a new template for a new job, for typically a job posting.

Being able to do that means it's not […], it doesn't sit off to the side of your core

business application.

It is part of your core business application.

And therefore, it's a core part of your … So this type of analysis, then, becomes

a core part of that business process as well.

And that is the key to moving analytics from what it has been up to this point, which is

something that is useful for ten or fifteen percent of your total employee population

to something that is used by one hundred percent.

I have to put that sort of intelligence into the business process.

It can't be a separate thing.

Most common example of this with the consumer is probably Netflix with their recommendation

engine, or Amazon, real products that go with this product; those are recommendation engines

And they're extremely powerful not because of the intelligence behind them, but certainly

for that, but because they're in your process.

While you're looking for a movie, you're seeing the recommendations about movies you

might like based on your previous choices.

While you're buying toilet paper, you see bar soap that most people might buy at the

same time.

That's useful because it's in your process.

And so, it's very easy for you to use it, and very easy for you to see the value.

The key, then, is by building these, let's say features that are backed by this data,

from a user interface standpoint, the teachers probably look pretty simple on the surface.

You know, checkbox, this, this, that to make a few selections.

But, the key, there, is by building it into the application, it means it's now central

to the activities you're performing also known as the process.

That's exactly right.

Nobody wants to have to teach all of their employees how to be data scientists.

You don't want them interacting with complex statistical algorithms.

And what you want is you want them to do the work that their experts are doing.

You want our HR people focused on HR.

You want your finance people focused on finance.

But if you can make all of those applications that they're using in their day-to-day lives

more intelligent, more capable of helping them run the business correctly, then it's

great and it's embedded into the work that they're already doing so there's no learning

curve for them.

Okay.

So now, we're going through this story, and I want to remind everybody that you're

watching Episode #233 of CxOTalk.

And, we're speaking with Mike Flannagan, who is responsible for analytics inside the

important Leonardo product at SAP.

Did I say that right?

All right.

[Laughter] So now, we've got the data.

It's been anonymized, it's been aggregated, so now we can benchmark against it.

We've got the user interface to that data, and a nice friendly way inside the software

application; so it's a core part of the process.

And that application is being fed from that data store.

But, you mentioned this kind of magic term, "machine learning."

So, how does machine learning and other, let's say, I hate the term "artificial intelligence."

It's become like "digital transformation," it's sort of a catch-all phrase.

So, how does machine intelligence, machine learning change the way you now can use that

data?

Where's the magic?

Machine learning is a buzzword, and everybody's talking about machine learning.

Machine learning is a fairly horizontal capability.

What makes it interesting is data that will train a model that can then be used to make

better decisions.

Just to elaborate: When you say "train a model," for businesspeople out there, what

does that mean?

So, think of it like hiring a new college graduate and needing to teach them how do

to a job function in your business?

The more they do that job function, the more they learn how to do it.

The more they do it right, and somebody says "good job," that positive reinforcement

makes them do it that way.

The more they do something wrong, and somebody corrects them and shows them the right way,

the less likely they are to make that mistake in the future.

Fundamentally, the same concepts as machine learning.

If I want to take the very large dataset of historical data to predict what might happen

in the future, what I'm looking for is what are the things that have happened in the past

and what are the things that happen in the future, and where do I see very high levels

of correlation between the past and the future?

So, for example, Salespipe One data may be highly correlated to next quarter's revenue.

The higher my pipeline is, the higher my close rate is on that pipeline, the more revenue

I'll have next quarter.

The machine starts learning exactly how correlated that his; exactly how good of a predictor

of next quarter's revenue is this quarter's sales.

And, the more it learns, the more data you feed it, the smarter it gets.

The more accurately it can predict things; obviously, it's not a crystal ball, there's

always an opportunity for unforeseen things to change the future.

But machine learning; one of the powerful capabilities is about learning from the past

and being able to automatically apply that learning to estimate the most likely thing

to happen in the future.

So, we'll definitely dive back into that, but I also want to welcome Dion Hinchcliffe.

Dion is an industry analyst like myself, and truly one of the most influential analysts

among CIOs; and also hosts his own CIO-focused show on CxOTalk.

CxOTalk, absolutely.

Well, thank you.

Thanks, Michael.

Hi, everyone.

Hi, Michael.

So we've been talking about … You were just describing machine learning, and maybe since

Dion is here, where do IT and the business … Where do IT and the business fit together

in this whole landscape? […] one clarification.

So, I'm working with CIOs and the C-Suite in general.

There's a lot of excitement around what machine learning and artificial intelligence

can do.

The question is, that I'm getting more and more now, is, "now I'm going to hand over

my data to these learning algorithms.

What stops you from learning so much about my business?, and then that knowledge gets

transferred inevitably to the products of my competitors and other businesses.

So, how do I know that all that stuff it learns stays with me," right?

So data is the new gold, the new oil, as you guys were talking about.

How do organizations retain the control?

Well, I think the Number One thing in my mind, as you start asking that question was, it

starts with something that we talked about earlier; to recognizing the value of the data

that you own; recognizing that your data is an asset to be protected.

You don't take the buildings that your company owns and leave all the doors unlocked when

everybody goes home.

Same idea with your data.

You have to realize what data is valuable, what data is important, what data is proprietary.

And, take the appropriate steps to protecting that data.

And, sometimes, that means that you need to think very carefully about the aggregation,

anonymization process, to make sure that it can't be reverse-engineered; to make sure

that somebody can't de-anonymize data, for instance.

It's surprisingly easy to do since everything gives off data now, right?

So, there's a lot to correlate with.

Is all this data insecure, or is it underappreciated in terms of its real value?

I think it's underappreciated.

I think most of the companies that I've talked to recognize that there is value in their

data, but if you ask them to put it on a balance sheet, to put it on a bottom line, they couldn't

tell you exactly how to value their data.

And, that's a problem; because I can tell you exactly how to value my real estate assets.

I can tell you exactly the value of every employee in my enterprise, but I can't tell

you how much, what is being called now one of the most valuable assets of every enterprise

is actually […]. So Mike, given this, what are some of the

metrics that an organization that is undertaking a program of digital transformation, at least

when it comes to data, what are some of the metrics or the KPIs that they can use to evaluate

their progress?

How are we doing?

This conversation sounds very, right now, at the moment, and new, but a lot of the answer

to that question, I think, has been the same answer for thirty or forty years, which is

a lot of companies have a Garbage-in, Garbage-out problem.

If your data's wrong, it's not valuable at all.

And if you use incorrect data as training data for machine learning algorithms, it's

about to predict the future?

Your predictions are all going to be wrong.

So, a big KPI is data quality.

How good is your data?

How accurately is it inputted in your systems?

How well do you take data that's incorrect out of the system and out on a process?

So, I think that's a key starting point.

Because, if your data's not right, all these advanced technologies; all of these new techniques

from learning from data will not benefit you in a way […].

And so, this is a, shall we say, part of the … Is this – the correct terminology – part

of the software implementation process?

Well, it's part of a couple of things.

Obviously, data quality, there is software that helps with the process of data quality,

but the other thing is business process; making sure that you have a good process for data

being entered correctly, validated … On an ongoing basis.

On an ongoing basis.

The challenge, though, that we've heard from here is that speed is paramount these

days.

I surveyed 54 CIOs, top CIOs around the world, many companies, about how fast you have to

move.

And they all reported they're under very strong pressure to move much more quickly.

How can they take these quality measures when everyone's been asked to execute and deliver

as fast as possible?

Yeah, I mean, this is the problem.

Sometimes you have to slow down to go fast.

If you have a data quality problem, and you don't slow down to fix that, all of these

technologies that are going to help you go much faster are not going to help you go faster,

unless you're going faster in the wrong direction.

That's foundational.

It's foundational.

So, you really have to consider transforming the way you think about data from its origination

all the way through to its ultimate delivery of value.

And, if the origination of the data is flawed, then the whole rest of that supply chain,

if you will, becomes flawed.

Machine learning and artificial intelligence, it all sounds very new, but most of the advice

that I've just talked about goes back for a long, long time.

The fundamental processes haven't changed.

What is exciting now is that there are technologies that if you get those fundamental processes

right, can help you go at an incredibly accelerated pace.

Now, we hear about data scientists being so in demand, and you need to be prepared to

hire data scientists.

I think most businesspeople, they hardly know what a data scientist even does.

Most data scientists hardly know what a data scientist does, but if it's on your resume,

your salary goes up!

So, everybody's a data scientist!

So, how should businesses relate to, let's say, let's call it; and to all my data scientist

friends out there, I apologize for this; but how should businesses relate to the data scientist

problem?

So, I think, you know, if you think fundamentally about what your core business is, and you

make some decisions from there about how far away from your core business do you want to

learn something, versus where do you want to procure something?

In my core business, it's data […]. Having an army of data scientists in-house makes

absolute sense.

If I'm a retailer, I think there's a reasonable question about how much of that do I want

to do in-house?

But you need data science, so all of this is about data science.

So, what should we do; we businesspeople?

Well, the question is, do I want to hire them and own it in-house?

Or do I want to work for the firm who does that as their core competency?

Data science as a service, right?

[…] And I think the question is a core-versus-contexts

question, just like everything else.

Do I want to own in-house janitorial services?

Or, do I want to hire a janitorial services firm to come do that?

I think you can apply that to lots of different areas of your business about what is the core,

and what things do you need?

But they're not the business that you need to be […].

You know, we heard some great things about SAP Leonardo today, and you guys probably

already talked about some of this, but it seems like the packaging around that is really

designed to say, "Alright, so this is part of the N-to-N value chain that most organizations

have to realize.

Data is at the center, and the value it attracts is going to come from an increasing layer

of technology; so blockchain, to machine learning, to data intelligence, and so on.

If someone wanted to understand what SAP Leonardo does, how do you describe that in one sentence?

I'd say, first of all, there are a whole lot of technologies that are in Leonardo that

a hundred other companies will sell you as well.

So, the technology element is interesting and it's differentiated.

But, this isn't a technology solution.

It's a business problem solution.

So, if you look at Leonardo, the idea is once I solve a business problem, there are common

elements of my problem that apply to lots of other companies and lots of other areas.

So, we talked, I think, in one of the keynotes about an example that I mentioned here, which

is trains being outfitted with sensors for the purpose of predictive maintenance.

Eighty percent of what was done there would be interesting to a transportation company

who owned trucks, or a mining company that owned heavy machines, to be able to do the

same sort of predictive maintenance to minimize downtime and improve their operating costs.

And so, taking those common elements, and packaging them as industry-specific accelerators,

so that you as a CEO could identify a business problem and figure out very quickly how to

get them identifying that problem and implementing the solutions.

It's about accelerating that process in between.

So, it's a combination of technologies from the SAP portfolio that is aimed at a specific

industry or vertical issue.

And it's combined with a few services because what I've done is taken a hundred percent

solution for this customer, and generalized seventy or eighty percent of it.

And they get […], right?

We then need a few services to tailor it back to the next customer and the one after that.

And so, being able to do that lets us move from problem identification to implemented

solution about fifty percent faster.

So you've kind of systematized or productized some of the common technology elements, and

some of the common process and deployment aspects of it.

Exactly right.

So, there are business problems for seventy or eighty percent of common.

There are, if you look at those problems, technology solutions that are always going

to be seventy or eighty percent common; and it's taking those common elements of technology,

putting them together with the common approaches, the methodologies that are used to implement

them and [know] exactly what do I do with that sensor data to get it to reduce the operating

expense, and packaging that as an accelerated order.

In the old days, we used to call that "packaged solutions."

You must have industry … How do you break it out?

I mean, for industry; for banking; it's going to be attributes of this ... The underlying

technology may be the same, but certainly, the process aspects are going to be very different

than, say, retail or … And these accelerators are packaged by industry.

So, the recognition is certainly that business problems are fairly specific to industries.

There are some that can be generalized horizontally as more technology problems or process problems,

but the business problem if you really want to make it that repeatable, it has to be someone

specific to the user.

Probably the farther down in the technology stack you go, the more commonality there is.

And as you get closer up to the process and to the activities that people perform, and

how the data is ultimately used, I would suppose there it becomes much more differentiated

industry-by-industry.

That's right.

And I think as a consumer, you could argue that if you need to go buy a toothbrush, the

difference between Walgreens, CVS, Target, is not distinguishable for you.

But if you get into actually how they run their business every day, obviously each retailer

has things that are specific to them that are different from the retailers.

And those have to be considered in […]. So, a lot of talk these days about blockchain.

We're seeing more and more types of data used just to […] transact […] the blockchain.

And now we're hearing things like "identity," or things like SKUs or unique customer IDs;

all sorts of things are being thrown in there.

What's the blockchain story, in terms of the data and the analytics?

Now we have to talk about blockchain analytics, I guess?

The whole new generation of data?

What's your view on all of that?

Gardner has; I'm a big fan of research, and Gardner has something that I think's appropriate

and it's called the Hype Cycle.

And, it's a curve that all technologies; new technologies start to ride and at some point,

the expectations are, this technology can do everything, it can solve every problem,

it can slice it can dice.

And I think that may be a little bit of where we are with [Cooptator] right now.

There's a lot of potential.

What I think is really interesting is, which ones are going to land on real business value?

Which ones are really going to change a business model or business process?

And I think some of that's still going to be worked out.

But I love the fact that there's so much potential, and there's so much conversation

and people are trying things.

The key, I think, is fail fast with any technology that we're experimenting with.

Mike, we just have a few minutes left.

And, what advice do you have?

You're working with a lot of customers.

You see a lot of different businesses.

And what advice do you have for a businessperson who is looking at all of this and hearing

about machine learning, and all of this stuff: they're trying to figure out what to do.

So, what should they do?

My Number One piece of advice, and this is a little bit shamelessly associated to…

The approach we're taken with SAP Leonardo is [to] take one small step at a time, get

business value from that step, or fail fast and move on.

Don't try to solve every business model, every business process, every business problem all

at once with some giant tens of millions of dollars transformation project.

Start small.

Start small, find some quick wins, deliver some business value, and then do it again.

On the things that don't work, fail fast and fail cheap, and move on.

And I think that's probably the most powerful advice that I can offer, and that's the

design principle behind Leonardo.

And I know, Dion, we speak with lots of CIOs.

It's certainly great advice for any CIO.

Yeah.

Totally agree.

They get in the list as quickly enough and building the skills that are doing that, it

allows you to tackle and move […]. Alright.

Well, this has been a fascinating conversation.

We have been speaking with Mike Flannagan, Senior Vice President at SAP, and I'm so

thrilled that Dion Hinchcliffe, industry analyst, focused on CIOs, has come join us.

And of course, Dion has his own show on CxOTalk focused on CIOs.

Thank you, everybody, for participating today.

Mike Flannagan, thanks so much!

It's been great!

Thanks for having me.

And Dion…

Thank you.

Thank you, everybody, have a great day.

For more infomation >> #233 Digital Transformation and Data: AI, IoT, Analytics, SAP Leonardo (from SapphireNOW) - Duration: 37:41.

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Yu Yu Hakusho HD Capitulo 55 (Sub Español) - Duration: 20:17.

For more infomation >> Yu Yu Hakusho HD Capitulo 55 (Sub Español) - Duration: 20:17.

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Descobrindo como viajar por Cuba - EP 04 - Duration: 11:01.

One more day in Havana, finally a sunny day, no clouds, and the plan today is to go to Viñales.

Viñales is a famous city here for a day trip from Havana, as they produce the tabacco used in the famous cuban cigars.

So we are gonna have breakfast and go to the Guagua bus station, the locals bus, the one for cubans.

From there we see how we can go to Viñales.

It looks like this "Guagua bus station" thing is a mith. The bus terminal is acually the "Omnibus Nacionales" one,

which have the buses the cubans can take, but the tourists can't. We can only take Viazul buses, which are expensive.

So we are gonna try to get a shared cab.

We have some problems. We tried to take a shared cab, but the driver is charging 20 CUC (US$ 20,00) each.

Also there wasn't anyone else to come with us, so we are going back to the hostel to invite the people there

and also see how much it is to go with Viazul and check which option is the best, as 20 CUC (US$ 20,00) each is a lot.

- One way only. 20 CUC to go, 20 CUC to come back. - Really expensive.

- We are not going to Viñales today. - People in the hostel had different plans for today

and the shared taxi is too expensive. - Also now is a too late to go as the trip takes around 3h.

It is 11AM, so we would be almost all the time in the transport, so it is not worth it.

But we are gonna come back to Havana at the end of this trip, we stay 3 or 4 days longer here, so we are

gonna go to Viñales when we come back. Tomorrow we are going to Cienfuegos, so we are going to the

bus station check how it is to get a shared taxi there for tomorrow. By doing this we are gonna walk around the city also.

We got to the Viazul, I hope the parking lot is full of shared taxis. Here is where the tourists come to buy the bus tickets.

It is the first time we are trying to go to another city, so we are learning how to do it. There are shared taxis,

which apparently cost the same as the Viazul buses. But it picks you up at your place and leaves you at the house in your destination.

But still they are expensive. One possible solution we found is to travel by truck. A truck that has some seats.

Then you can take them. It is a much cheaper option. It is this kind of truck here. So we were talking to a

cuban guy and he told us these trucks leave from almost any bus station, but they don't have a schedule, so

the idea is to get there and wait. This is what we are doing tomorrow. We are gonna go to a big bus station

we found, we are gonna go early, and wait until we get this truck.

On the streets we met this guy, whom gave us really good tips where to take the truck. So tomorrow we are

gonna take this truck and get to Cienfuegos.

- Tomorrow we want to go to Cienfuegos by truck. - By truck?

- Yes. Tomorrow we will go to the bus station to get it. - Or maybe you can go to the highway and take it there.

- Do they stop at the highway also? - Here you get a Guagua that leaves you in Lisa. San Agustin.

- There you can find Luna del Mediodia, a place where all the buses that go to the countryside stop.

Then Rafael asked him where we could eat cheap and he showed it to us. Really good. Reminded me of KFC.

Then we bought a beer to him, as he help us. When we left, he came walking with us. When we were about to

say goodbye, he asked for 1 CUC (US$ 1,00) to buy food for his kids. He works for the government building houses an so on.

The monthly payment here is US$ 20,00. It is kind of a Basic Income for eveyone who works for the government.

But with this you can buy food and not starve, but if this person wants more money he will need to find another way to get it.

So it is some kind of Basic Income. You have the basic to live in a simple way. If you want more, then you will

have to do other stuff to get more money. Here you don't see poeple on the streets starving, everyone has a place to live an so on...

Take a look at Fidel's timeline on Facebook. Cool, Fidel!!

We are now in front of the Granma office. As I said yesterday, Granma is the yatch who brought the

the revolutionaries form Mexico to Cuba, but it is also the name of the main newspaper in Cuba.

They have an edition of the Granma six times a week, from Monday to Saturday, not on Sundays.

When we get back to the house we are staying I will show it an edition of the Granma for you, as they have a

signature of it in the house we are staying. - Hey, stop recording.

- I got censored. - He almost got us arrested...

- Not quite. - Yeah, I was joking. We are walking in between the ministries and Raul Castro stays somewhere here.

But we have no idea where. So he was making a video and a policeman showed up to censor him.

So, as I was saying, when we get back to the house I will show you the Granma newspaper. We read it a bit to see

how it it. It didn't look any different from others. - It is left wing.

- Of course. What the owner of the house pays for it is 1 CUC (US$ 1,00) every 3 months.

This is the Casa Particular we are staying at. They offer water, you can make coffee, the computer on the internet.

I saw them before using Facebook, Youtube and so on. Here we have our messy room. It is for 5 people.

The bathroom with hot water. A fan. It is much better than what I expected before getting here.

The owner is super cool, her son also. The whole family is cool. They help us a lot. She is so cool that behaves

like a mother here, as she said she can't sleep when someone goes out alone during the night.

Granma, the Revolution's Newspaper. This it the number 94 of the 53rd year.

At least this edition looks like any other newspaper, but revolutionary.

For more infomation >> Descobrindo como viajar por Cuba - EP 04 - Duration: 11:01.

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Yu Yu Hakusho HD Capitulo 57 (Sub Español) - Duration: 20:17.

For more infomation >> Yu Yu Hakusho HD Capitulo 57 (Sub Español) - Duration: 20:17.

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Yu Yu Hakusho HD Capitulo 56 (Sub Español) - Duration: 20:17.

For more infomation >> Yu Yu Hakusho HD Capitulo 56 (Sub Español) - Duration: 20:17.

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Que es una Cafe Racer? - Duration: 7:40.

Which is a moto Cafe Racer? Many are even asking the same thing I did not know exactly the definition until I started to make this video. I thought that bikes were 80s but no .. Which is a Cafe Racer? Cafe Racer is a style of motorcycling and motorcycle popularized in the UK in the early 1950s, mainly by the Rockers, who personalizaban their bikes with small fairings, modifications to the chassis and footrests, exhaust pipes, breadsticks and General Lightening machine, removing everything that was not necessary and an emphasis on speed and agility, leaving the comfort of their mounts in the background. Which is a Cafe Racer? Nowadays, this phenomenon and custom adopted by motorcycle enthusiasts stays with varied bases, being appreciated especially machine with tradition and curriculum, whether they are of British origin, as in the beginning, or from any other source, provided that the basic elements lend an authentic setting Cafe Racer and skill, imagination and skill of its owners and builders allow. Which is a Cafe Racer? "Coffee racing is mostly a matter of taste. It is an atavistic mentality, a peculiar mix of low style, high speed, pure nonsense and a disdainful dedication to cafe life and all its dangerous pleasures ...." Hunter S. Thompson Definition Rise, fall and resurgence of the movement Rocker and Café Racer We have seen that people moved in groups on motorcycles transformed to your liking and that this was a cultural movement at the time. But it also meant an economic movement that motorcycle manufacturers tried to make the most. Own British motorcycle manufacturers began selling models inspired by the Café Racer people who built his own garage. Which is a Cafe Racer? Marks the rest of Europe also went on sale models that met more or less with the canons of the Café Racer. powerful engines, noisy exhaust, brakes and more or less in line with engine performance and above all an image very competitive suspensions. Car seats, reservoirs, flat handlebars, inverted or semi handlebars like racing bikes and delayed footpegs for the driver to adopt a position as possible racing. Which is a Cafe Racer? This boom also allowed some small manufacturers reached its peak thanks to the wisdom of their products. For example the Rickman brothers, who produced alternative to the original chassis, so good that marks ended by buying patents to manufacture themselves. And this was partly own death Café Racer movement. Factory bikes became so good that left little room for improvement. Which is a Cafe Racer? Of course, a movement had many followers as the Rockers and Café Racer could not die just like that. Never actually died during the last decades of the twentieth century simply stood in a less visible place. Until motorcycle brands found that there was an untapped market and began a round of classic models look. Thanks to this we can talk about the resurgence of Café Racer. Although now most not seek pure performance, but an image widely studied many purists despise. Which is a Cafe Racer? They are pure or not, what is clear is that many are currently Café Racer on the street. Some are exactly as they left the factory, but many still keep alive the essence, custom bikes looking to differentiate themselves while seeking performance without losing image that we all associate to a Café Racer. Which is a Cafe Racer? Hashtags: #caferacer #queesunacaferacer #triumphpanama #triumphcaferacer KEYWORDS: Spanish Motovloggers, Cafe Racer, which is a Cafe Racer, which is a Cafe Racer, cafe racer honda, cafe racer yamaha, cafe racer bmw, cafe racer Triumph cafe racer Ducati cafe racer Honda CB750, motorcycles cafe racer? ?, motorcycles cafe racer, a racer cafe?, a cafe racer, which bike is best to make a Spanish cafe racer, motorcycle coffee mexico racer, motorcycle style cafe racer, sales of motorcycles cafe racer motorcycles cafe style racerMotovloggers , Cafe Racer, which is a Cafe Racer, which is a Cafe Racer, cafe racer honda, cafe racer yamaha, cafe racer bmw, cafe racer Triumph cafe racer Ducati cafe racer Honda CB750, motorcycles cafe racer?, motorcycles Cafe racer, a racer cafe?, a cafe racer, which bike is best to make a cafe racer, motorcycle racer mexico coffee, cafe racer style motorcycles, sales of motorcycles cafe racer motorcycles cafe racer type

gesture for my people is that they do not exist

best in today let's talk

what is a coffee mutu pace

good inter Caferra is a style of

motorcycle or motorcycle in June

popularized in the UK the cro

me 1950 mainly

Los Roques who not only roque

coqueros

are the people who wanted us Angol

with this phrase banzos Marinsa bike

small houses with small fairings

modifications banks enter one

Uncover culminate General Lightening

the maximum giving everything

it was not stadium and uploading month

especially the emphasis was

end speed and everything else as

something the community food background

today this phenomenon and

custom adopted by lovers and

motorcycles remains based on

Bulgaria being appreciated especially

machine hearth and curriculum and

Neat British origin and their

early

or from any other source

also that the basic elements

provide a real home set

avenidad root and imagination or

a good make their owners and

drivers can afford the

coffee racing threw a question

pipeline in an atavistic mentality one

peculiar

julia armes the peculiar mixture of

pure style under high speed sos

silly and really good love

jamal bus 12 only inspires

basically it is a rain coffee is

simple as a brown richter

amass the 50's with a bike that

It appears to be from the 50s era

product of rock culture

USCA moment I approach it from two bikes

fast and good basically we have today

otherwise this trend as there

a kit to transform any motorcycle

a Sinclair style and wanted the manager

that right now the king had coffee and

It is something that is gripping much much

good popularity and what kind of many

Cancer can transform good king

it really any bike

transform the royal palace are clear

you have to have a look

imagination a little bigger for

I go exactly as lower

modified so that you have the look of

250 a gesture and see so much

classic old but at the same time

aggressive and character

but I think that's all a lot of their and

ask what are the best waiters

it takes form in Cape race of self and

May pathways that who so given collage

Of course there are bikes

Flamenco is so old motocross

in coffee Renfe superbikes

change

including flash and Ferb mike coughlan

stroke engines can also be

ferriz transform all depends on my

imagination and good

later they will be video

What are the best bikes for

beginners can transform into

coffee rifles and I think that's good

all guys thank you very much we are

leave a secular greetings to all my

brothers Bingen networks

WECAF as cokes

obama

If you are the final

this ruling ensures that you will support me life

Faith

recording everything everything

For more infomation >> Que es una Cafe Racer? - Duration: 7:40.

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ale e franz - Duration: 4:21.

For more infomation >> ale e franz - Duration: 4:21.

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HSN | Household Helpers 05.28.2017 - 11 AM - Duration: 1:00:01.

For more infomation >> HSN | Household Helpers 05.28.2017 - 11 AM - Duration: 1:00:01.

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モンスト【ランク上げ】初心者卒業するまで終われません！【狐の嫁入り】 - Duration: 10:28:58.

For more infomation >> モンスト【ランク上げ】初心者卒業するまで終われません！【狐の嫁入り】 - Duration: 10:28:58.

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I can't stop thinkin' 'bout you. - Duration: 2:45.

Hey Shao...

That's when our journey begin...

I'll never forget…The first day we met.

I don't understand all the crying over a female.

You don't know!

You're not about to say "love", are you?

What if i'd say "love"?

Then i say you're fucking right! I don't understand anything about love.

Don't come around me no more.

Starting with that half black, half rice-and-peas,

Ezekiel Figuero boyfriend of yours.

He's not my fucking boyfriend.

Stab your hate into my love,

He's my friend, my best friend.

"Family's about love"

I ain't never had no fucking family, then, Books!

You got me.

You are a natural everything.

And the way you be all up in Ezekiel's business all the time,

maybe you got the hots for my boyfriend.

- Stay as long as you want, brother. - How about "forever"?

It's cool with me.

You lied to my face!

What happened to the fucking family, Books?

You ain't magic, Shaolin Fantastic.

Cause' you and me, a time, remember?

I got your back.

And not in that fake, corny, "wherever you go, i go" mack-bullshit kind of way.

No, i mean for real.

Never ever should have trusted you.

We're done.

"Done" done.

For more infomation >> I can't stop thinkin' 'bout you. - Duration: 2:45.

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Proven Treatment Of Tartar, Gingivitis And Teeth Whitening In 4 Steps According To A Dentist T - Duration: 2:14.

OUR WEBSITE : http://justhealthrelated.com/

Our health is very important to keep us healthy, beyond aesthetics, personal health is important

if we do not want toxins and bacteria to invade our body, and be a key to personal health.

One of the parts that we should have more hygiene is certainly our mouth, it is important

to remember that the way our body receives the necessary vitamins and minerals is through

food.

Everything that comes through the mouth will have contact with your body, an example: cavities

or tartar should not exist, today we will tell you a wonderful remedy for tartar, you

eliminate it from the root and you will not worry about this problem.

The best remedy against the terrible tartar: -1 tablespoon baking soda

½ teaspoon salt

– ½ cup hydrogen peroxide

-Antiseptic mouthwash

-Warm water

-Toothbrush

Preparation: The first is to take a cup and mix a spoonful

of baking soda with half a tablespoon of salt.

Once both are mixed, moisten the toothbrush with the warm water and immerse it well to

your mixture.

Rubbing our teeth gently using the brush, spit after a few seconds, you must do it for

5 minutes under the same procedure.

SECOND STEP: Combine the half cup of hydrogen peroxide

with half a cup of warm water, rinse the mouth with this mixture for 1 minute.

Spit and rinse with half a cup of cold water.

THIRD STEP: Now we will need our floss, we will rub the

tartar that is in our teeth, it is important that you try to do it very carefully so that

you do not irritate the gums, because you can damage them and cause irritations.

It is highly recommended to interlock two teeth with a child to move it from side to

side, so remove without damage.

FOURTH STEP: To finish, we recommend to rinse your mouth

very well with antiseptic mouthwash, in this way you will get better results.

By performing this treatment to the letter, we can assure you that you will not have a

pinch of tartar in your mouth.

Remember that if you liked this treatment, share it.

For more infomation >> Proven Treatment Of Tartar, Gingivitis And Teeth Whitening In 4 Steps According To A Dentist T - Duration: 2:14.

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PARTNER WITH PEOPLE BETTER THAN YOU 💪 | LumoVlog 071 - Duration: 3:30.

A lot of times, nonprofits function like

when you get started, it's easy to function like,

"Hey we're planning to do this, with your help we can..."

We started by asking questions like,

"Do you guys think this is worth doing?"

Questions around that.

"What would you say if a kid,

who didn't have a computer at home,

was able to learn how to build a computer

built his first computer and took it home?"- type of thing.

Asking questions.

Because that gets engagement.

Asking questions gets engagement.

When you say, "Hey we're doing this and we need you,"

People are like, "Oh, cool... whatever."

But whenever you ask a question,

especially when they're kind of controversial,

... people respond.

There's this natural thing

to give an opinion on social media.

So we capitalize on that,

to draw attention to what we're doing.

I'd also say to partner with people

who are good storytellers.

Whoever this entrepreneur is,

the real art of starting anything

is finding people who are really, really good at it,

even if you think you're really good at something,

find people who are better than you at these things.

Then bring them into your circle.

With a nonprofit, it's pretty cool because you just

you're not pitching to someone,

"Hey, one day you'll have equity,"

It's just, "Hey, this is a volunteer experience,

I'm not asking you to give up your life,

c'mon, you can't do it for the community?"

You're able to pitch this idea of

a better community, and for nonprofits,

what I love is that we can be a

community-based organization.

Really, they exist to be built by the community,

to support the community.

Not built by an individual

to fund that individual.

I think really looking at strategic partners -

For us, SLAM agency here in St. Louis

was one of the strategic partners that we found,

and they ended up doing our video for us,

and helping us out with space whenever

we wanted to do stuff there.

We found Haliday Douglas,

he's in the Education Department,

and I don't have an education background.

This guy graduated from Harvard and works for SLPS,

so having a good friend

who challenged our vision and mission several times,

so by bringing him in, he helped us to refine

how we executed what we do.

My wife, Danielle, is amazing

at actually implementing this stuff, and

she was coordinating the classes.

I didn't teach the classes, she taught the classes.

We brought in a computer program developer, who said,

"Dude, I have no idea how I could ever help you guys,

'cause I am not good with kids."

But next thing you know, he's in there

explaining more in depth, the technical skills.

So find a wide variety of people,

go out and talk to people,

and don't depend on yourself to build the organization.

Depend on the community, because

if your organization is worth anything,

it's only going to be...

the value will be determined by the community that supports it.

For more infomation >> PARTNER WITH PEOPLE BETTER THAN YOU 💪 | LumoVlog 071 - Duration: 3:30.

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Génération des Héros - VOSTFR - Duration: 0:51.

For more infomation >> Génération des Héros - VOSTFR - Duration: 0:51.

-------------------------------------------

Laissez les brûler - VOSTFR - Duration: 2:22.

For more infomation >> Laissez les brûler - VOSTFR - Duration: 2:22.

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Trump Swore He Wouldn't Cut Social Security And Medicare... - Duration: 6:14.

>>[STREAM WENT DOWN AGAIN]

>>THE OTHER THING IS I WILL SAVE IT FOR YOU, HOPEFULLY GET

SOME MONEY ON TOP OF THAT, GIVE YOU A NICE NEST EGG, SO YOU CAN

AT LEAST NOT DIE IN THE STREETS WHEN YOU GET OLDER.

THE OTHER

THING IS IF YOU GET HORRIBLY DISABLED IT'S ALSO GOING TO BE A

LITTLE BIT OF INSURANCE FOR YOU.

THAT'S PART OF SOCIAL SECURITY.

YOU PAID INTO IT, THAT'S WHY YOU ARE ENTITLED TO IT.

NOW THEY

WILL ROB YOU OF THAT AND GO, IF YOU GET HORRIBLY DISFIGURED THAT

IS YOUR PROBLEM, NOT MINE.

I'M A REPUBLICAN, I WILL THROW SOME

BOOTSTRAPS AT YOU, AND THE MONEY I TOOK OUT OF THAT SOCIAL

SECURITY, I'LL GIVE IT TO THE RICH.

TAX CUTS, BABY.

NOT TAX

CUTS FOR THE PAYROLL TAX THAT PAYS FOR SO SECURITY, I WILL

KEEP TAKING THAT OUT OF YOUR CHECK, BUT I WILL GIVE IT TO YOU

IF YOU GET DISABLED, GIVE IT TO MY RICH FRIENDS.

BUT I THINK YOU

WON'T NOTICE.

REPUBLICANS SAY SOCIAL SECURITY, DISABILITY,

THEY ARE FAKING IT ANYWAY --

>>THAT'S THE INSPECTOR GENERAL, NOT THE GUYS WHOSE JOB IS

PROTECT THE PROGRAM.

THE INSPECTOR GENERAL'S JOB IS TO

MAKE SURE THEY ARE ABIDING BY THE LAW AND NOT CHEATING.

LOOKS AT IT AND GOES, 1% FRAUD RATE.

DO YOU KNOW HOW HARD IT IS

TO GET

SOCIAL SECURITY DISABILITY?

ONLY 28% OF THOSE

WHO APPLY GET IT.

THAT MEANS THEY REJECT 78% OF APPLICANTS.

MEANWHILE REPUBLICANS GO, THEY ARE BOMBS.

YOU GOT YOUR LEGS CUT

OFF?

GO GROW SOME NEW ONES.

I WANT MORE TAX CUTS FOR THE RICH.

WHEN TRUMP SAID HE WOULD CUT SOCIAL SECURITY, OF COURSE HE

WAS LYING.

WHAT DID YOU EXPECT?

I'M TAKING MONEY FROM YOU AND

GIVING IT TO MY BUDDIES.

AND WHAT DID TRUMP SAY DURING THE

CAMPAIGN?

ONE MORE VOTE FOR YOU GUYS --

>>AND WHERE IS IT GOING?

WELL --

>>AND OF COURSE TAX CUTS FOR THE RICH.

>>YOUNG TURKS.

For more infomation >> Trump Swore He Wouldn't Cut Social Security And Medicare... - Duration: 6:14.

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Secrets And Easter Eggs Hiding In Adam Sandler Movies - Duration: 4:58.

As a young stand-up comedian, Adam Sandler probably didn't foresee himself becoming one

of the most successful funnymen in Hollywood, complete with a sweet Netflix deal, but he

did. While his movies are often beaten up on by critics, the actor has starred in some

pretty classic comedies, like Happy Gilmore, Billy Madison, and The Wedding Singer.

"Son of a b---- ball why didn't you just go home."

However, if you look long enough at the man's movies, you'll spot numerous in-jokes and

all sorts of references scattered throughout his filmography.

The Star Wars connection

There's no denying Sandler's films have a pretty strong connection to Star Wars.

"Millennium Falcon, Millennium Falcon. Cruising along here. Whooo, almost hit a star."

Take Carrie Fisher, for example. The late great actress is best remembered for her role

as Princess Leia, but did you know she also worked on The Wedding Singer? As it turns

out, Fisher was an uncredited writer on the 1998 comedy, and according to Drew Barrymore,

Fisher was brought in to "write the girl's part to make it balanced."

But the connections don't stop there. In 2012's That's My Boy, Sandler plays Donny Berger,

a deadbeat dad who names his kid "Han Solo."

"You were basically the worst parent ever."

"I was awesome!"

Sandler's loyalty to this galaxy far, far away might stem from a real-life interaction

he had with Han. According to Sandler, Harrison Ford once asked him to wash his car as a way

to surprise his kids, as they were big Sandler fans.

"He goes, my kids think you're funny. And it would just be such a kick for them to see

that. And I was like, 'Uh, OK.'"

And if the galaxy's greatest space scoundrel asks you for a favor, how can you say no?

The Godfather connection

Adam Sandler's extended movie universe doesn't stop at Star Wars. The actor is also a big

fan of The Godfather, and a few of his films contain references to Francis Ford Coppola's

classic gangster series. For proof, look no further than Jack and Jill, which stars Michael

Corleone himself. The actor even references the infamous scene from The Godfather Part

II where he threatens Diane Keaton with all his power, asking her, "Don't you know me?"

Of course, in Jack and Jill, Pacino uses the monologue to intimidate Sandler into getting

him a date with his twin sister.

And in Billy Madison, Sandler did his own Michael Corleone impersonation, spoofing the

scene from The Godfather Part II where Michael gives Fredo the kiss of death.

A Vendetta for V

Everybody has a type, and it seems like Adam Sandler has a thing for women whose names

start with "V." Or against them, maybe? In Billy Madison, Bridgette Wilson plays Veronica

Vaughn, a third-grade teacher who has strange ways of motivating Billy to take his education

more seriously. Then, in Happy Gilmore, Sandler's character is encouraged to get his head in

the golfing game by a PR executive named Virginia Venit.

And in The Waterboy, Bobby Boucher falls in love with witchy astrologer Vicki Vallencourt

, despite his mama's warnings.

"That woman is the devil, and I want you to stay away from her, you hear me?"

"Yes, Mama."

"Now you come on inside, before that lil' ol' witch cast a spell on ya!"

Big Daddy also saw its title slacker dumped by his girlfriend Vanessa, and in Little Nicky,

Sandler's son of Satan falls head over heels for Valerie Veran, a woman who helps Nicky

defeat his evil brothers.

And now we're wondering if maybe Adam Sandler was the guy behind that Guy Fawkes mask…

The Tarantino connection

While The Ridiculous 6 didn't fare particularly well with critics, there's no denying the

title is an intentional nod to Quentin Tarantino's The Hateful Eight, which came out the same

year. But believe it or not, Adam Sandler almost had the opportunity to work with Tarantino

himself, as the director originally wanted the comedian to play the bat-wielding "Bear

Jew" in Inglourious Basterds.

Of course, the role eventually went to Eli Roth, as Sandler chose to star in Funny People,

instead. But while Sandler missed out on a great role, Sandler's pal Jonathan Loughran

managed to catch the Tarantino train. Loughran has shown up in quite a few Sandler films,

but he's also taken serious roles with Tarantino, like playing Jasper the mechanic in Death

Proof. He also appeared in Kill Bill as a gross trucker, so perhaps it's not a coincidence

that when Loughran popped up in Sandler's Sandy Wexler, he was playing a character by

the name of Trucker Jon.

The cat connection

There's a feline theme running through a lot of Adam Sandler's movies, too. Anger Management,

for example, was a film where Sandler's character designs clothing for his overweight pet, Meatball.

(Interestingly, that's the name of Sandler's real-life dog, who served as the ring bearer

at Sandler's wedding.) Then there's Mr. Deeds, where Sandler is slandered by a dishonest

reporter after he tried to save some cats from a fire by tossing them out of a window.

In Don't Mess with the Zohan, Sandler's character actually plays hacky sack with some poor cat,

and in Funny People, Sandler lets loose with a few laughs as he watches a kid perform "Memory"

from, you guessed it, Cats. But hey, we're not done with the feline jokes just yet. In

I Now Pronounce You Chuck and Larry, Chuck falls for Alex when she's dressed up as a

Cat woman for Halloween. Meow there's a thread worth tugging at.

His love-hate relationship with felines probably has a little something to do with that time

he was mauled by a cheetah during an African safari, but who knows.

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For more infomation >> Secrets And Easter Eggs Hiding In Adam Sandler Movies - Duration: 4:58.

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Aurora Borealis and Lyrids Meteor Shower Timelapse - Higgins Lake, Michigan - April 2017 - Duration: 0:07.

Timelapse image of dark night sky.

The bright orange and red radiation from the Aurora Borealis paints the horizon.

For more infomation >> Aurora Borealis and Lyrids Meteor Shower Timelapse - Higgins Lake, Michigan - April 2017 - Duration: 0:07.