3 mitos de big data revelados

21
3 Big Data Myths

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3 Mitos de Big Data revelados Uno. Sobre el tamao de datos: el verdadero valor est en cmo utilizamos los datos, no la cantidad de datos que tenemos. Dos. Todas las personas necesitan acceder a la informacin de manera fcil y rpida. Para resolver esta necesidad requerimos alguien especializado en datos (un Data Scientist) Tres. Existen lo que se denomina "framework de software" especiales como Hadoop. Es un sistema bueno, pero generalmente necesitamos unir informacin de fuentes dispares que se encuentra dispersa.

TRANSCRIPT

Page 1: 3 Mitos de Big Data revelados

3Big DataMyths

Page 2: 3 Mitos de Big Data revelados

Big Data is

BIG

Page 3: 3 Mitos de Big Data revelados

Talk to any business, and they’ve probably discussed the topic of big data and what it means

for their organization.

Perhaps they’ve even made an investment in big data with the promise of insight.

Page 4: 3 Mitos de Big Data revelados

But today, few organizations are realizing the promised value of big data.

That’s because they are thinking about it all wrong.

Page 5: 3 Mitos de Big Data revelados

1 It’s all about size

2 You need a data scientist

3 You need a system like Hadoop

3 myths about big data

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It’s all about size

Myth

Page 7: 3 Mitos de Big Data revelados

Talking about size, speed, and complexity misses the real point of

big data. Every day, data gets bigger, faster, and more complex.

But now we’ve reached a tipping point.

Page 8: 3 Mitos de Big Data revelados

We’re collecting data about processes and activities that we’ve never captured before.

We call this datafication: it’s the idea that almost

anything can be quantified.

And because storage and processing are cheap,

we can collect data about almost anything, just

in case we need it.

Page 9: 3 Mitos de Big Data revelados

But just because you have a lot of data doesn’t mean you have business value.

Page 10: 3 Mitos de Big Data revelados

People are still the most important asset of any company. And now the real question becomes “what can you learn from your data that would change your business?”

The most important thing IT can do is to work with the business to determine how the answers to new questions can help drive the business forward.

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MythYou need a data scientist to tame big data.

Page 12: 3 Mitos de Big Data revelados

Your most valuable technology asset isn’t really a technology

at all. It’s your data. The ability to manage your data well and

to extract value from it is critical.

But it’s not necessary to hire a super human data scientist

to do it all for you.

Page 13: 3 Mitos de Big Data revelados

What is important is that you create a culture of data-driven decision making and ensure that everyone across your

organization has the data they need to do their job well.

Page 14: 3 Mitos de Big Data revelados

And, here’s a little secret:

Most people in your company don’t need big data. They need small data. But they need it in a way that is easy to use and gives them the information they need

in terms they can understand.

Page 15: 3 Mitos de Big Data revelados

MythYou need a big data system like Hadoop

Page 16: 3 Mitos de Big Data revelados

There are a number of new technologies today that help you deal with data – including Hadoop.

These are great tools to have in your toolbox. But just as you don’t need a hammer for every fix-it job, you don’t need a Hadoop-like system to solve every data problem.

Page 17: 3 Mitos de Big Data revelados

It’s important to recognize and embrace data disparity. The reality is that your data is not all going to reside in one location.

Remember the data warehouse? It was rarely, if ever, the only place that you needed to go

for all of your data. And, today, your data may be in even more

locations than ever before with the addition of big data

architectures and cloud.

Page 18: 3 Mitos de Big Data revelados

What matters most is the ability to bring data together from many

disparate sources in order to solve a business problem or tell a story

about a customer.

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So embrace big data and its complexity.

Design for it.

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Imagine what you’ll discover.

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