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CFOs Take Back Seat in Big Data Drive

CFOs Take Back Seat in Big Data Drive | Big Data & Digital Marketing | Scoop.it

A growing number of companies are sifting through huge piles of data in search of insights.

 

But relatively few business and IT executives say they have their chief financial officers to thank for it.

Luca Naso's insight:

Among the more than 900 executives surveyed:

1) 15% said the chief information officer was their company’s biggest champion of Big Data,

2) followed closely by the chief executive, at 14%.

3) The CFO was cited as the main advocate by just 8% of the respondents.


And yet 70% of companies using Big Data say it primarily helps them with generating revenue (compared with the 30% who use it to control costs).


Where are the CFOs?

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Jean-Michel Franco's curator insight, November 7, 2013 1:40 AM

As thare are a lot of CIO that report to the CFO, this can explain the debates that we currently hear the relationship between CMO and CIO. 

Marketing is leading the Big Data efforts in most companies, and CIO need to take the driver seat too in this context

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(Big) Data Evolution - Infographic

(Big) Data Evolution - Infographic | Big Data & Digital Marketing | Scoop.it

"Here are some interesting facts you might not know about big data via The Visual Capitalist"

Luca Naso's insight:

It all started in late 60s with zip codes;

- then demographic data were added;

- which were complemented with Lifestyle data;

- whose power was optimized with Attitudinal data;

- and today Behavioural data (or social) has brought data to an entire new level (see the four V of Big Data).

 

However, the challenge remains. In fact, it gets harder and harder: how to use the data to make reliable predictions?

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56% Of Enterprises Will Increase Their Investment In Big Data Over The Next 3 Years

56% Of Enterprises Will Increase Their Investment In Big Data Over The Next 3 Years | Big Data & Digital Marketing | Scoop.it

These and other insights are from the jointly produced CapGemini and EMC study, Big & Fast Data: The Rise of Insight-Driven Business.

Luca Naso's insight:

This report is telling us that Big Data is truly a game changer.

 

1. Redesigning market shapes:
64% of senior executives said that big data is changing traditional business boundaries and enabling non-traditional providers to move into their industry.

 

2. Boosting or damping companies:

65% agree that they risk becoming irrelevant and/or uncompetitive if they do not embrace big data.

 

3. Requires a faster pace:

45% view their current internal IT development cycles for new analytics to be too long and not matching their business requirements.

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Big Data Fans: Don't Boil The Ocean

Big Data Fans: Don't Boil The Ocean | Big Data & Digital Marketing | Scoop.it
Planning a big data strategy? Don't be overly ambitious and always know the problems you're trying to solve.
Luca Naso's insight:

I repeat it every time I can: always state your goal *before* embracing a Big Data project.

 

What is the biggest problem you have? Why do you want to collect all this data? What kind of insight are you looking for? Just saying 'insight' and 'innovation' is a wonderful thing, but first and foremost you need to focus.


And one more thing: a successful Big Data project is not a matter of having a super-hero data scientist, but a talented TEAM.

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Myth Busting Artificial Intelligence | WIRED

Myth Busting Artificial Intelligence | WIRED | Big Data & Digital Marketing | Scoop.it

No doubt AI is in a hype cycle these days. Unfortunately, there has also been much concern about the risks of AI. In my opinion, much of this concern is misplaced and unhelpful.

Luca Naso's insight:

Read this to get a quick and simple definition of:

1. Artificial Intelligence (algorithms inspired by natural intelligence)

2. Big Data (data so large and diverse to challenge traditional methods)

3. Machine Learning (a form of Artificial Intelligence)

4. Deep Learning (a class of Machine Learning)

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AnalyticsInnovations's curator insight, February 20, 7:08 PM

AI is ready to knock us off!

Gary Hayes's curator insight, February 22, 4:27 PM

Quote "AI is now at a point where solutions for these hard problems are forthcoming – where with no intervention AI can make and incorporate discovered patterns into models for enhancing decision-making on new, unseen, real-time data.  The results are simply astounding."

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Big Data and Bacteria: Mapping the New York Subway’s DNA

Big Data and Bacteria: Mapping the New York Subway’s DNA | Big Data & Digital Marketing | Scoop.it
An 18-month project to map the microbes that populate the New York City subway system—which include the germs that cause food poisoning, meningitis and even bubonic plague—shows how commuters pass on bacteria from the food they eat, the pets or plants they keep, and their shoes, trash, sneezes and unwashed hands.
Luca Naso's insight:

The big data project (the first genetic profile of a metropolitan transit system) is in many ways “a mirror of the people themselves who ride the subway,” said Dr. Mason, a geneticist at the Weill Cornell Medical College.

 

It is also a revealing glimpse into the future of public health.

 

By documenting the miniature wildlife, microbiologists hope to discover new ways to track disease outbreaks, detect bioterrorism attacks and combat the growing antibiotic resistance among microbes, which causes about 1.7 million hospital infections every year.

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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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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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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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Why only one of the 5 Vs of big data really matters | The Big Data Hub

Why only one of the 5 Vs of big data really matters | The Big Data Hub | Big Data & Digital Marketing | Scoop.it
People have been using the four Vs (Volume, Velocity, Variety and Veracity) to describe big data, but all of the big data in the world is no good unless we can turn it into Value, the fifth V of big data.
Luca Naso's insight:

Sometimes it's good to go back to the basics:

What is Big Data?

 

Gartner in 2011 gave the 3V definitions, today we have a better understanding ad we find more appropriate to add two more concepts.

 

1. Volume: how much data?

2. Velocity: how data grows or moves?

3. Variety: about the shape

---

4. Veracity: about the reliability

5. Value: about the ROI

 

The first 4 points describe what Big Data is, the fifth one reminds us that a Big Data project is relevant if it adds value.

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Microsoft designs stress-busting bra

Microsoft designs stress-busting bra | Big Data & Digital Marketing | Scoop.it

Microsoft researchers have designed a smart bra that can detect stress.

Luca Naso's insight:

The Internet of Things will include many strange and wondrous devices. That's why analysts at ABI Research predict more than 30 billion devices will be wirelessly connected by 2020. Health-related data collection will play a large role in the IoT, of course.

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Six Predictions About Big Data and Marketing in 2015

Six Predictions About Big Data and Marketing in 2015 | Big Data & Digital Marketing | Scoop.it

Harnessing the power of Big Data has moved from an innovation to a critical success factor.


How will it continue to grow in 2015?

Luca Naso's insight:

Here are 6 possible trends in Big Data in 2015. I mainly the following 3:

 

1. Big Data will go mainstream -

so much that we might start dropping the term "Big" and just talk about "Data"

 

2. Everything with go on the cloud -

this will simplify the usage, cloud is simple, cheap and, above all, flexible

 

3. Analyses will become faster -

thanks to improvements in big data tools and technologies

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How To Use Big Data In B2B: Lead Scoring & Analytics

How To Use Big Data In B2B: Lead Scoring & Analytics | Big Data & Digital Marketing | Scoop.it
Two great ways to use data to inform your business-to-business marketing strategy
Via iNeoMarketing
Luca Naso's insight:

Nowadays the customer's journey is more complex than the classic "sales funnel", it has several touchpoints and continuous back and forths.
Companies need an automated system to collect, analyse and leverage all of the information produced along the way.

How to do that? Two common methods are:
1. Lead Scoring, to improve efficiency of marketing strategies
2. Predictive Analytics, to anticipate behaviours and improve ROI

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iNeoMarketing's curator insight, February 16, 9:08 PM

You probably don't have big data, and you really don't need it. So long as you have a good MAP implemented, you can take advantage of all these benefits. It just requires a bit of smart upfront work.

jason's curator insight, February 16, 9:58 PM

Great piece on how to tackle big data. 

clatot's curator insight, February 25, 11:23 AM

Big data is becoming increasingly practical & critical in B2B, with maybe less focus on statistics & more on meaningful usage, to optimise leads & predictive behaviours.

Who has concrete case studies about it ?

 

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Data analytics for HR: how to make effective recruitment

Data analytics for HR: how to make effective recruitment | Big Data & Digital Marketing | Scoop.it

"If we can apply science to improving the selection, management, and alignment of people, the returns can be tremendous."

Luca Naso's insight:

Big Data help to make better decisions also when it comes to choose a "person".

1. Tune hiring policies

2. Focused recruitment marketing

3. Evaluation based on "public" work

4. Proactive hiring

5. Recruiters still matter (and they need to update their own skills)

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Tavo De Leon's curator insight, February 13, 7:25 PM

Data Analytics and Big Data are beginning to shape the evolution of the recruitment process

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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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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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