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How big data is a key part of Intel’s data centre vision

How big data is a key part of Intel’s data centre vision | Big Data & Digital Marketing | Scoop.it

Intel recently shared its long-term strategic vision of how corporate data centers will evolve. Big Data processing plays a central role, driven by a future of escalating data volumes from mobile, cloud, and “Internet of Things” sources.

Luca Naso's insight:

Intel is still a long way from being a household name in Big Data analytics, but it seems intent to invest heavily in this area.

 

Intel’s Big Data strategy is not just about buzzword compliance. It is aimed at entrenching and reinforcing its x86 chips in Hadoop compute clusters.

 

Intel is taking a proactive role – not just coming up with processor optimisations, but actively engineering firmware further up the stack, and Hadoop distributions.
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Big Data & Digital Marketing
Data analytics as the key to know your customers and offer them what they really want.
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Why Big Data is the Next Frontier for Innovation

Why Big Data is the Next Frontier for Innovation | Big Data & Digital Marketing | Scoop.it

Learn about the power of big data, and how businesses need to come up with ways to manage and make sense of all the information

Luca Naso's insight:

Lots of information about Big Data from New Jersey Institute of Technology.

 

The upper half of the infographics introduces Big Data with the usual buzz words. The remaining has a lot of interesting statistics.


My 6 takeaways from the infographics:

1. $300 billion, what US could save in Healthcare

2. +60% in operating margin (retail)

3. 73% of companies have already increased revenues thanks to big data

4. 56% of IT decision makers believe that finding the right staff is the biggest challenge

5. "Query and reporting" is the Top 1 capability currently available

6. "Transactions" and "Log Data" are the most common source of data currently collected and analysed (88% and 73% respectively).

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Understanding big data leads to insights, efficiencies, and saved lives | Harvard Magazine

Understanding big data leads to insights, efficiencies, and saved lives | Harvard Magazine | Big Data & Digital Marketing | Scoop.it
Luca Naso's insight:

There is a lot of content in this (long) article published by the Harvard Magazine.

 

Here are my main 3 takeaways:

1. "The Big Data revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity" by Gary King

2. Big Data isn't everything: "We had petabytes of data and yet we were building models that were fundamentally flawed, because we didn't have insights about what was happening" by Nathan Eagle

3. "No matter how much data exists, researchers still need to ask the right questions to create a hypothesis, design a test, and use the data to determine whether the hypothesis is true." by Nathan Eagle

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Build your first IoT device with IBM and ARM kit

Build your first IoT device with IBM and ARM kit | Big Data & Digital Marketing | Scoop.it
The mBed IoT Starter Kit launches today - providing a kit for building IoT prototypes that can be sending data to the cloud for analysis within minutes.
Luca Naso's insight:

A common challenge when dealing with the Internet Of Things is the lack of standardization. This, in turns, makes it difficult for all of the sensors to communicate, for all of the data to be gathered, analysed and eventually leveraged.


IBM and ARM have teamed up to lower the barrier for developers in IoT: the mBed IoT Starter Kit costs just around 100eur, and can be directly connected to the IBM IoT Foundation (to channel data into IBM Bluemix serivces).

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Abdulmalik Ofemile's curator insight, May 10, 3:23 AM

Big Data DIY

Leonard Bremner's curator insight, May 25, 5:15 AM

Looks a fun project

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Business intelligence and analytics trend towards self-service at the Gartner Summit

Business intelligence and analytics trend towards self-service at the Gartner Summit | Big Data & Digital Marketing | Scoop.it
Self-service analytics, business intelligence on Big Data, and the changing role of the IT buyer were the belles of the annual Gartner Business Intelligence and Analytics Summit.
Luca Naso's insight:

Today's buyers are increasingly coming from the business side of the house and not from corporate IT and self-service analytics is growing while traditional dashboard BI is in remission.
Self-service analytic tools allow power users to quickly explore, blend and visualize data to produce new business insights and to validate business data requirements to support application development and data management. 
Is the pendulum swinging in the direction of analytics empowerment and reduced time-to-answer and away from cost control and data quality management?
Gartner thought leader, Frank Buytendijk, suggested that we look to the business model that cracked the code on optimizing the centralization versus decentralization trade-off; namely, franchising.
This implies standardization of tools and enterprise licensing to drive down costs, tool-specific skilling to create larger pools of skilled workers to be shared across projects and centralized provisioning of compute infrastructure to save time and money.

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6 Predictions For The $125 Billion Big Data Analytics Market in 2015

6 Predictions For The $125 Billion Big Data Analytics Market in 2015 | Big Data & Digital Marketing | Scoop.it
The big data and analytics market will reach $125 billion worldwide in 2015, according to IDC. Both IDC and The International Institute of Analytics (IIA) discussed their big data and analytics predictions for 2015 in separate webcasts yesterday.
Luca Naso's insight:

Gil Pres discusses 6 directions that the Big Data market could take in the near future.

 

Here are my top 3:

1. Security

2. Internet of Things

3. Image and video analytics

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Badr-Eddine Bourhlal's curator insight, April 18, 5:14 PM

Big data

Fàtima Galan's curator insight, April 21, 12:07 PM

"Security: combining machine learning, text mining and ontology modeling

IoT analytics: the “Analytics” of Things

Buying and selling data

Image, video, and audio analytics will become pervasive"

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Data science done well looks easy

Data science done well looks easy | Big Data & Digital Marketing | Scoop.it

After a ton of work like that, you have a nice set of data to which you fit simple statistical models and then it looks super easy.

Luca Naso's insight:

When a successful Data Science project is well presented, it usually looks very simple. It reminds me of some of the proofs in Calculus or Physics that I studied when an undergrad.


In fact, they just *look* simple, and they do so because someone has done an incredibly hard and difficult job before hand.

 

In Data Science projects, the hidden job is usually related to data: looking for data, cleaning the data, joining the data, realising you are missing some data and iterate.

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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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(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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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."

Leonard Bremner's curator insight, May 25, 5:17 AM

Whots Maths!

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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. 

Leonard Bremner's curator insight, May 25, 5:19 AM

Less trips to the GP is good for all a constant health knolage enviroment is the aim

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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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5 Signs It’s Time to Outsource Your Data Management

5 Signs It’s Time to Outsource Your Data Management | Big Data & Digital Marketing | Scoop.it

If your company is struggling with big data because it is taking up too much time and resources, consider hiring the services of a data center outsourcing firm.

Luca Naso's insight:

 

Nowadays, companies in virtually any field have the capability to gather, store and take advantage of massive amounts of information.But not all of them have the capability to manage the data in the proper way.
Here are 5 signs that it could be time to outsource your data management:1. Overpriced in-house management costs.2. Lack of in-house Big Data management experts.3. Constant need to re-deploy employees to do other tasks.4. Inability to comply with regulatory requirements.5. Failure to respond quickly to technological changes.
Avoid creating strong dependency on the third party expert and keep in mind these three things that can massively help you:1. Have a clear plan on how you want to use the data2. Embrace cloud technologies3. Develop in-house skills

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Hendrik Feddersen's curator insight, May 26, 4:50 PM

Nowadays, companies in virtually any field have the capability to gather, store and take advantage of massive amounts of information. But not all of them have the capability to manage the data in the proper way. Here are 5 signs that it could be time to outsource your data management: 1. Overpriced in-house management costs. 2. Lack of in-house Big Data management experts. 3. Constant need to re-deploy employees to do other tasks. 4. Inability to comply with regulatory requirements .5. Failure to respond quickly to technological changes.

Biel's curator insight, May 27, 11:54 AM

Com Big Data canviarà la comercialització empresarial

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A Comprehensive Guide to Data Management for Businesses

A Comprehensive Guide to Data Management for Businesses | Big Data & Digital Marketing | Scoop.it

In order to leverage data for your business effectively, you have to first develop a clear understanding of what data is and how you can efficiently make the most out of it. This ultimate guide to data management will help you out.

Luca Naso's insight:

As organizations become more and more data-driven, it becomes progressively more important to set up a healthy and productive way to manage data.

Here are 4 major steps to follow to help you improve on this:

1. Data Management

2. Data Security

3. Data Quality

4. The Team

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1.

Data management is the “administrative process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users.”Basic pillars are: provisioning, protection, replication and recovery.Evaluate data before engaging in big data analytics.Have a maintenance plan.
2.

Data security must be prioritized by any organization to enable it to function properly and for operations to flow efficiently. It also provides stockholders and executive teams peace of mind of knowing that the information they have stored in their servers will not be easily exploited by hackers or cyber-criminals.
3.

A study conducted by Experian Data Quality shows that outstanding data quality has a direct correlation to an increase in company profits. 4 steps to reduce incidence of human error (cited by 65 percent of organizations to be the main cause of data problems): Identify data entry points, train staff, Automated verification, clean data over time.
4.

Hire a competent team of professionals who know their roles very well: data management supervisor, data entry staff, data analyst, quality and training staff

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Hadoop and the Internet of Things: Better together

Hadoop and the Internet of Things: Better together | Big Data & Digital Marketing | Scoop.it

 

The Internet of Things continues to grow more popular, and the network of devices connected to it gets bigger every day. Gartner has estimated that there will be 26 billion devices connected to the IoT within the next six years.

 
Luca Naso's insight:

Up to now the Internet of Things has mainly focused on the data generation part (sensors and devices).

 

It is now time for the Analytics side to take over. Here is where Hadoop can make the difference.

 

However, my expectations are that IoT will boom with Real-Time analytics, and Hadoop can be of little use in this scenario.

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Google Boosts Cloud Services To Tackle Big Data

Google Boosts Cloud Services To Tackle Big Data | Big Data & Digital Marketing | Scoop.it

 

At the Hadoop Summit in Brussels on Thursday (Apr 16th 2015), Google announced significant updates to two of its cloud services.


Via Peter Azzopardi
Luca Naso's insight:

I believe that the future of Data Analysis, Big Data and the like is in the cloud.

 

"Big data the cloud way means being more productive when building applications, with faster and better insights, without having to worry about the underlying infrastructure", said Google Product Manager William Vambenepe.

 

Google Data Flow is now in beta and publicly available to any software developer.

 

Google BigQuery got upgraded, now able to process 100k rows in a second.

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Peter Azzopardi's curator insight, April 18, 6:07 AM

"Today, nothing stands between you and the satisfaction of seeing your processing logic, applied in your choice of streaming or batch mode, executed via a fully managed processing service. Just write a program, submit it, and Cloud Dataflow will do the rest," Vambenepe said.

Joe Boulis's curator insight, April 19, 10:32 PM

Google made major announcements at the Hadoop Summit in Brussels; including significant updates to Google Cloud Dataflow and Google BigQuery. Facilitating the processing of large quantities of data.

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Big Data Discovery Is The Next Big Trend In Analytics

Big Data Discovery Is The Next Big Trend In Analytics | Big Data & Digital Marketing | Scoop.it
According to Gartner, "Big Data Discovery" is the next big trend in analytics. It's the logical combination of three of the hottest trends of the last few years in analytics: Big Data, Data Discovery, and Data Science.
Luca Naso's insight:

Another way to look at this is:

Since the market offers fewer data scientists than needed, new tools are being developed so that less experienced professionals can analyse data in a productive way.


Will this "Data Discovery" be an evolution of self-service BI that we see emerging today?


I think that Microsoft products such as Power BI, or Excel Power Query and Power Pivot, are worth mentioning in this context.

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Elías Manuel Sánchez Castañeda's curator insight, May 26, 12:47 PM

In my case as consulting partner of a small business consulting I have no answer, but rather questions:
In my company to maintain or increase my level of competitiveness, I need to get started with Big Data and / or Data Discovered and / or Data science? Which of the three or two or three? How do I start?
If the answer is I do not need any of the three tools, well it all figured out. But if the answer is I need one, two or three, then the fun begins, because the first thing I learn is that they are, then how to use them in my business, then use them, finally measure whether there were benefits and if necessary make adjustments .
There is no doubt that the technologies of information and communication technologies (ICT) have made the profession of business an exciting journey.

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6 Ways to Succeed as a Digital Marketer

6 Ways to Succeed as a Digital Marketer | Big Data & Digital Marketing | Scoop.it
To achieve success of any measure in digital marketing, we need people who are creative, quick-thinking and quick-witted and willing to take the initiative to lead change.
Luca Naso's insight:

Here are 6 suggestions by Sarvesh Bagla on how to become "the person that everyone wants" in digital marketing. Keep in mind that he is mainly targeting the Indian market.


The 6 points are good suggestions, but definitively not enough to excel in the industry. In particular I believe that a strong focus on data and analytics is needed.


1. Be different

2. Play a lot on Social Networks

3. Develop interests outside of your immediate curricular pursuits

4. Get great at language

5. Develop your Listening skills

6. Take the lead

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Badr-Eddine Bourhlal's curator insight, April 18, 5:14 PM

digital Marketing

Leonard Bremner's curator insight, May 25, 5:14 AM

You can never learn to much!

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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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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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