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Social Media, Big Data and Visualization

Social Media, Big Data and Visualization | Big Data & Digital Marketing | Scoop.it
Big data from social media can give us insight into our relationships, our habits, and the things we care about.
Luca Naso's insight:

Here is an interesting report of a panel discussion on social media, big data and visualization held at the Vancouver Enterprise Forum.

I'd like to draw your attention to the following 2 parts:

 

1. While recognizing a trend can help you make better decisions, understanding the cause behind that trend is even more valuable.
Storytelling with social media and big data can make you find this cause.

 

2. Visualizing social media data to empower individuals.

When the exchange of information between social networks and their users is fair and transparent, individuals can view their own lives and social circles in an entirely new light.

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Fàtima Galan's curator insight, September 23, 2013 9:37 AM

Luca Naso insight: "understanding the cause behind that trend is even more valuable"

Katy Volo's curator insight, October 8, 2013 6:54 PM

This is how social intelligence is revolutionizing the Digital world. Very interesting article.

Big Data Spain's comment, October 21, 2013 6:51 AM
BIG DATA SPAIN 2013 is coming! <br>#BDSpain Conference in Madrid (Kinepolis 7 & 8 Nov) <br>Buy your ticket www.bigdataspain.org
Big Data & Digital Marketing
Data analytics as the key to know your customers and offer them what they really want.
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Data science: 'Machines do analytics. Humans do analysis'

Data science: 'Machines do analytics. Humans do analysis' | Big Data & Digital Marketing | Scoop.it
Two leaders of Booz Allen's data science team talk talent, building a data science team and the machine-human link in analytics.

Via Don Dea
Luca Naso's insight:

In Data Science "talent" means to be "relentless in the face of failure"

 

Insights (aka Big Data Value) builds on Big Brains:

No machine can be a miracle cure. Humans have to find the patterns, ask the right questions and make the connections in the data.

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Don Dea's curator insight, December 10, 2014 1:06 AM

No machine can be a miracle cure. Humans have to find the patterns, ask the right questions and make the connections in the data. "Machines do analytics," explained Sullivan. "Humans do analysis." Computers are good at detail and examining the past, but real data science requires imagination and cognitive ability.

Carla Gentry CSPO's curator insight, December 14, 2014 9:42 AM

Sullivan isn't big on analytics technology that serves as a magic bullet to data science. No machine can be a miracle cure. Humans have to find the patterns, ask the right questions and make the connections in the data. "Machines do analytics," explained Sullivan. "Humans do analysis." Computers are good at detail and examining the past, but real data science requires imagination and cognitive ability.

Fàtima Galan's curator insight, December 17, 2014 3:48 AM

"Data science is a team sport and you need a diverse team to explore multiple angles."

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A Big Data Winter is waiting ahead

A Big Data Winter is waiting ahead | Big Data & Digital Marketing | Scoop.it
Big-data boondoggles and brain-inspired chips are just two of the things we’re really getting wrong
Luca Naso's insight:

Interview to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley.


1. Deep Learning has nothing to do with Neuroscience

People continue to infer that something involving neuroscience is behind deep learning, and that deep learning is taking advantage of an understanding of how the brain processes information, learns, makes decisions, or copes with large amounts of data. And that is just patently false.


For issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like.


So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.



3. A Big Data Winter is waiting ahead

When you have large amounts of data many of your inferences are likely to be false. It’s like having billions of monkeys typing. One of them will write Shakespeare.


A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that. Eventually, we have to give real guarantees. Civil engineers eventually learned to build bridges that were guaranteed to stand up.

So with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.


If nothing changes, there will be a “big-data winter.” After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.”

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Be a Data Scientist in 8 steps!

Data science is the new thing! How to be a data scientist? [originally written by the team behind DataCamp]

Luca Naso's insight:

Becoming a Data Scientist IS NOT like cooking a recipe, and a data scientist IS NOT supposed to be able to solve all of your Big Data issues.

 

This being said, here is a list of 8 categories of skills very useful to any data scientist:

1: Stats, Math, Machine Learning

2: Coding

3: Database

4: Visualisation and reports

5: Big Data

6: Meet peers

7: Get a job

8: Be social

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8 big trends in big data analytics

8 big trends in big data analytics | Big Data & Digital Marketing | Scoop.it
Big data technologies and practices are moving quickly. Here's what you need to know to stay ahead of the game.
Luca Naso's insight:

In the past, emerging technologies might have taken years to mature. Now people iterate and drive solutions in a matter of months, or weeks.

 

While the technology options are far from mature, waiting simply isn’t an option. IT managers and implementers cannot use lack of maturity as an excuse to halt experimentation

 

The article presents the top emerging technologies and trends that should be on your watch list. Here are my best 4 pick:

1. Big Data analytics in the cloud

2. SQL on Hadoop: Faster, better

3. More, better NoSQL

4. Deep learning

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Wearable Tech: a $50bln market

Wearable Tech: a $50bln market | Big Data & Digital Marketing | Scoop.it

Credit Suisse estimates the entire wearable tech market to be worth $50bn by 2018.­­­­­

Luca Naso's insight:

Wearable tech is the integration of digital tools in lifestyles to improve health, they carry not just data mining capability but also power lifestyles.



Big data analytics, data science, and wearable computing is predicted to help better analyze patient data collected objectively with potential life-changing implications for drug development, diagnosis, and treatment.



Cloud plays an integral role here: storing, computing and eventually beaming the relevant information from the smallest of devices. In fact, cloud can also aid in analysis of this data.

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Cloud computing is going to absorb your big data workloads, too

Cloud computing is going to absorb your big data workloads, too | Big Data & Digital Marketing | Scoop.it
There has been a spate of product announcements and integrations over the past few weeks signaling that many big data workloads — including, and especially, Hadoop — will soon be ready to run reliably in the cloud.
Luca Naso's insight:

I cannot think of any other place for Big Data but the cloud!

Amazon and Microsoft are clear leaders at the moment, but there is a lot of movements and Oracle might well catch up.

My Hadoop clusters have always been running on the cloud and will always be (I am currently using HDInsight on Azure).

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64% of business organizations are investing in big data in the next year

64% of business organizations are investing in big data in the next year | Big Data & Digital Marketing | Scoop.it
Adopt big data or get left behind. Business Review USA takes a look at why your business needs to get on the big data bandwagon.
Luca Naso's insight:

Big Data can help to make your company more customer-centric.


1. With Big Data you can collect and analyze massive amounts of data based on individual customers, and your business can customize each customer's experience.

 

2. With Big Data you can automate customer data collecting. This will save time to your employees and allows for a quality sales experience for your customers.


3. Data Security - Big data addresses security threats quickly, accurately, and oftentimes before they ever become a company-wide issue.

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Top Trends in Digital Marketing

Top Trends in Digital Marketing | Big Data & Digital Marketing | Scoop.it
From wearables and Big Data to personalization and multichannel – what the new “digiconomy” means for the future of digital marketing
Luca Naso's insight:

The digital revolution is bringing several changes to our lifestyle and to the way companies make successful business.


Here are my top 4:

 

1. Multichannel and crosschannel
Among those aged 16 to 45, the cell phone has replaced the television as the dominant format.

 

2. Data-driven marketing
Companies collect vast amounts of data on how consumers purchase and use products. Special algorithms analyze this data and turn it into useful insights.

 

3. Customer journey
The customer journey is the best way of understanding what the customer wants. This is where predictive analytics and big data play a key role.

 

4. Wearables and nearables

Smartwatches, wearables, nearables, and the Internet of Things are the next big trends. They offer users a multisensual experience.

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Big Data: The 4 Layers Everyone Must Know

"The different stages the data has to pass through on its journey from raw statistics to actionable insight."


Via Ana Cristina Pratas
Luca Naso's insight:

The main purpose of Big Data is to use data to create actionable insights.

 

In order to achieve such a goal, the data itself has to pass through a series of 4 layers:

 

1. Data Source
2. Data Storage
3. Data Processing/Analysis
4. Data Output

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kral2's curator insight, September 21, 2014 10:53 AM

Here is a clean "Big Data 101", in only 12 slides. 5 minutes to get at least an overview and understand if you have something to do with this huge buzz word or not :-)

 

For System Integrators, the challenge is cleary to be involved building what's need for layer 2 & 3 : say scale-out storage and massive parallel compute nodes!

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A Predictive Analytics Primer

A Predictive Analytics Primer | Big Data & Digital Marketing | Scoop.it
What you need to know and ask.
Luca Naso's insight:

Predictive analytics are gaining in popularity, but what do you—a manager, not an analyst—really need to know in order to interpret results and make better decisions? 

 

The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions. Let’s talk about each of these.


1. The Data: for good predictions you need good data;

2. The Statistics: regression analysis is what is usually used;

3. The Assumptions: every model has some assumptions, beware that assumptions can be invalid sometimes ...

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Size doesn't matter: can SMEs conquer Big Data?

Size doesn't matter: can SMEs conquer Big Data? | Big Data & Digital Marketing | Scoop.it
Big Data involves collecting a large volume of information. As SMEs don't have access to the same amount of data as larger corporations, does it mean SMEs can't make use of Big Data? Here is a case study with Ovolo Hotels.
Luca Naso's insight:

Data can be useful in a number of different ways. I like to say that Small Data gives you answers, while Big Data gives you questions.


In this article we read the case study of Ovolo Hotels. it's a small company that is leveraging data to improve their business in many ways.

 

Don't collect data just because Big Data is cool: if you don't use your data efficiently it's all a big waste.

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The Hidden Biases in Big Data

The Hidden Biases in Big Data | Big Data & Digital Marketing | Scoop.it
Blindly trusting it can lead you to the wrong conclusions.
Luca Naso's insight:

Big Data can be extremely dangerous without a Big Brain to analyse them properly.


Huge data sets ALWAYS contain some relations: some of them are right (causation), others are simply wrong. It pertains to data analysis to uncover the truth.

 

"As we move into an era in which personal devices are seen as proxies for public needs, we run the risk that already existing inequities will be further entrenched.

[...]

This goes beyond merely conducting focus groups to confirm what you already want to see in a big data set. It means complementing data sources with rigorous qualitative research."

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4 Rules for Knowing When to Invest in Big Data

4 Rules for Knowing When to Invest in Big Data | Big Data & Digital Marketing | Scoop.it

For every story about accelerated financial performance, I can point to ten that talk about mismanaged investments and a loss of interest by leadership in Big Data. 

Luca Naso's insight:

Adopters of Big Data analytics have gained a significant lead over the rest of the corporate world, but you should start your Big Data project if and only if:
1) You have some degree of mastery over business analytics.
2) You are collecting streams of data.
3) Your culture can embrace opportunistic analytics.
4) You have the nerd power.

 

Moving into Big Data without having a grasp on these four principles is like participating in a marathon when you’ve just learned how to scoot across a carpet.

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Democratizing Healthcare via Smartphones

Democratizing Healthcare via Smartphones | Big Data & Digital Marketing | Scoop.it
From smartphone attachments that can diagnose an ear infection to apps that can monitor mental health, new tools are tilting health-care control from doctors to patients.

Via Tictrac
Luca Naso's insight:

Digital avatars won’t replace physicians: You will still be seeing doctors, but the relationship will ultimately be radically altered. Deloitte says that as many as one in six doctor visits were already virtual in 2014.

 

Smartphones already can be used to take blood-pressure readings or even do an electrocardiogram. Other wearable sensor tools now being developed include necklaces that can monitor your heart function and check the amount of fluid in your lungs, contact lenses that can track your glucose levels or your eye pressure, and headbands that can capture your brain waves. Smartphone sensors under development will be able to monitor your exposure to radiation, air pollution or pesticides in foods. Smartphone attachments will soon enable you to perform an array of routine lab tests via your phone. Blood electrolytes; liver, kidney and thyroid function; analysis of breath, sweat and urine. 

 

By having the equivalent of intensive care unit monitoring on your wrist, hospital rooms can be replaced by our bedrooms. As a result, hospitals of the future are likely to be roomless data surveillance centers for remote patient monitoring.

 

Before these tools enter widespread use, they must all be validated through clinical trials and shown to preserve health.

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Hugo E's curator insight, January 12, 9:52 AM

Health proactivity will be more and more important, thanks to mobile apps and IoT. But thinking that it will allow to avoid medical monitoring is probably a big mistake...

Pascal Malengrez e-ssencials digital health's curator insight, January 15, 5:00 PM

Your smartphone becomes your health companion not only for diagnostic but also for therapy. For real. 

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How Data Analysis Impoved HSBC

How Data Analysis Impoved HSBC | Big Data & Digital Marketing | Scoop.it

Financial institutions use data analytics to improve their omni-channel marketing strategies, by focusing on what a financial institution can do with data analytics, not on what data analytics itself can do.

Luca Naso's insight:

Marketing has been called more of an art than a science. Yet in today’s financial services environment, the science of analytics is critical in making marketing a profit centre.

 

HSBC is one of the world’s largest banks and it uses data-driven decision making to optimize channel usage.

 

Combining the data from channel analytics with information about when to contact a customer and what to contact them about has helped the bank acquire new customers, enhance existing customer relationships and retain profitable customers over the long term.

 

The channel-centric model that most financial services companies continue to pursue today results in too much siloed information and an inability to develop true omni-channel strategies.

 

Data analysis should be used to develop a customer decision hub, which determines the minimum and maximum that you will spend to service a customer in each channel, the best moment in time to communicate with the customer and the best interaction with the customer at the best moment in time.

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The Rise of Big Data

The Rise of Big Data | Big Data & Digital Marketing | Scoop.it
Foreign Affairs — The leading magazine for analysis and debate of foreign policy, economics and global affairs.
Luca Naso's insight:

This is one of the best article I have ever read on Big Data.

 

Big Data is not just about having more data, or at a higher rate, or in different shapes. It is a profound shift in the way we deal with data analysis. Actually 3 shifts:

 

1. from "sample" to "population"

2. from "clean" to "messy"

3. from "causation" to "correlation"

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#BigData, the dark knight we all need?

#BigData, the dark knight we all need? | Big Data & Digital Marketing | Scoop.it
Over the last few years, state-sponsored data collection has come to the fore thanks to whistle-blowers and ex-spies. Since then, the clamor for calling the line between private and public data for...
Luca Naso's insight:

Data collection is not a news (cookies exist since the beginning of the internet). Now it has expanded into our life in the "real world", and it is bringing incredible benefits to:
1. Cities
2. Healthcare
3. Environment

 

Nevertheless Big Data is a double-edged sword, and cuts both ways. Although the potential of Big Data to do good is great; it is just as easy to manipulate and abuse.

 

Big Data: hero or villain?

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3 Big Benefits of Big Data in Gaming

3 Big Benefits of Big Data in Gaming | Big Data & Digital Marketing | Scoop.it

According to VentureBeat, sales for video games dipped by 2 percent in 2013. While nothing can stop the video game machine at this point, it can be improved, and big data may be the answer.

Luca Naso's insight:

1. Better Gaming Experience.

Our real life produces tons of digital data, imagine how much data your virtual life can generate ...

 

2. higher Gamer Engagement

Data can help to fine tune the difficulties players can find during the game.

 

3. Increased In-Game Revenue

Upselling is becoming the standard in monetizing games. Data on customer usage should be mined to increase the efficiency of this revenue stream.

 

Ubisoft is already leveraging Big Data power, as Halo 4. A revolution is on the horizon.

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Amazon epic fail

Amazon epic fail | Big Data & Digital Marketing | Scoop.it

Sometimes even the best ones can get it wrong.

Luca Naso's insight:

Increase profit or improve customer experience?

 

Big Data is a great tool. If used wisely it can deliver great value and long-lasting success.

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From Big Data to Insights: The Blueprint for Your Business

From Big Data to Insights: The Blueprint for Your Business | Big Data & Digital Marketing | Scoop.it
Data is really only valuable if you can translate it into actionable insights. Here, we lay out the framework for how businesses can put these insights to work to drive business goals.
Luca Naso's insight:

In 1910, Scottish writer and poet Andrew Lang said, "He uses statistics as a drunken man uses lampposts—for support rather than illumination." Decades later, many modern businesses still do just that, using data to support rather than drive their decisions.


Here you can find some simple suggestions on how to create a method that can help you to make sense out of your data (whatever their size):


1. Defining the data - easy and simple: do not neglect

2. Building the framework - the most difficult part: sketch, prepare and visualise

3. From data to action

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The Next Big Thing In Sports Data: Predicting (And Avoiding) Injuries

The Next Big Thing In Sports Data: Predicting (And Avoiding) Injuries | Big Data & Digital Marketing | Scoop.it
Can data tell a player's future? Teams like the San Antonio Spurs and New England Patriots are betting on it.
Luca Naso's insight:

In sports, injuries don’t just cost wins. They cost money. By one estimate, teams across Major League Baseball spent $665 million last year on the salaries of banged-up guys and their replacements.


Now, the fast-growing industry of performance analytics says it can minimize those massive losses. The trick: using data to anticipate how an athlete will get hurt before it actually happens.


Performance analytics have the potential to extend a player’s career, but it can also reveal hidden physical problems and shorten careers. If a player is shown to be at a heavy risk for injury, what owner is going to pay that person big bucks?

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LE ROUX Arnaud's curator insight, September 30, 2014 2:21 AM

La Data au service du prédictif dans le sport dans le but de prévenir les blessures, afin de réduire des coûts... vous en pensez quoi ? 

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Big Data Pays Off Big Time

Big Data Pays Off Big Time | Big Data & Digital Marketing | Scoop.it

Organizations that have actually implemented Big Data projects report "overwhelming satisfaction" with the results, according to a new survey from Accenture Analytics.

Luca Naso's insight:

"While a significant number of organizations may still be standing on the sidelines, Big Data users who start and complete projects see practical results and significant value," Accenture said.


Bigger companies seem to get the biggest benefits because they have a better understanding of the scope, value and importance of Big Data. Not because they have more money.


To benefit from Big Data, Accenture advised companies to:

1. Explore the entire Big Data ecosystem;

2. Start small then grow;

3. Be nimble;

4. Focus on building skills.

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How big data and analytics are making retailers more customer-centric

How big data and analytics are making retailers more customer-centric | Big Data & Digital Marketing | Scoop.it

Retailers today have to face a new breed of empowered customers who are always connected and have more information on products than sometimes even the retailers do. Being customer-centric is the new competitive differentiation for retailers today

Luca Naso's insight:

A recent IBM Institute for Business Value study notes that leaders are 166 percent more likely to make most decisions based on data, and 75 percent of leaders cite growth as the key source of value from analytics.

 

In the new IDC retail insights study: “Business Strategy: Big Data and Analytics Lay the Foundation for Revenue Growth,” it is noted that, in 2013, approximately 50 percent of retailers were using big data and analytics to inform pricing strategies, market intelligence and customer acquisition, with more retailers to join in next two to three years.

 

The various functions of a retail business, such as marketing, merchandising, supply chain, operations and customer service, must wear different hats at different times and should think about how others perceive their brands, as well as what they can do to enhance customer centricity.

 

Here are some examples of how IBM helped four famous retailers (Luxottica, BestBuy, Elie Tahari and Macys)

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Using Big Data to Make Better Pricing Decisions

Using Big Data to Make Better Pricing Decisions | Big Data & Digital Marketing | Scoop.it
Harnessing the flood of data available from customer interactions allows companies to price appropriately — and reap the rewards.
Luca Naso's insight:

All companies saw a profit-margin lift of between 3 and 8 percent from setting prices at much more granular product levels.

 

According to McKinsey, up to 30% of companies still fail to deliver the best price.

 

Four steps to turn data into profits:

1. Listen to the data
2. Automate
3. Build skills and confidence
4. Actively manage performance

 

 

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The Data Scientist’s Toolbox - Online Course

The Data Scientist’s Toolbox - Online Course | Big Data & Digital Marketing | Scoop.it
The Data Scientist’s Toolbox is a free online class taught by Jeff Leek, Brian Caffo and Roger D. Peng of Johns Hopkins University
Luca Naso's insight:

For those who want to learn about Big Data, Johns Hopkins University offers a "Data Science" Specialization on Coursera, a series of 9 free* courses and a final project (4 weeks each, total 40 weeks):


1. The Data Scientist's toolbox

2. R Programming

3. Getting and Cleaning Data

4. Exploratory Data Analysis

5. Reproducible Research

6. Statistical Inference

7. Regression Models

8. Practical Machine Learning

9. Developing Data Products

10. Capstone Project


*there is a $49 fee to pay (for each course and project) if you want a certified signature track. Signature Track is optional. You can still participate in the course for free and earn a Statement of Accomplishment.

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