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Furthering Big Data's Retail Benefits

Furthering Big Data's Retail Benefits | Big Data & Digital Marketing | Scoop.it

If your retail business isn't currently using big data to its fullest potential, it may behoove you to look at how data and analytics can do even more for your company's customers – and its revenue.

Luca Naso's insight:

Recently, market research firm PSFK Labs released the fourth annual report in its series "The Future of Retail," focusing on what those in the industry should consider in 2014.


It made a point of noting that customers expect to enjoy a shopping experience that's perfectly tailored to them across all channels (in-store, online and via mobile).


For this to occur, big data analytics must – and can – be used to offer the direct customer service and personalization that so many consumers seek.

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Kaitlyn Gregg's curator insight, November 5, 2014 3:24 PM

Big Data can pretty accurately predict consumer potential reactions, their worth as a customer throughout the lifetime, and what the trends are in order to continue satisfying them.  Its crazy how much big data can be essential to retail's success and keeping consumers happy.

Big Data & Digital Marketing
Data analytics as the key to know your customers and offer them what they really want.
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More Than 75 Percent of Companies Are Investing in Big Data in the Next Two Years

More Than 75 Percent of Companies Are Investing in Big Data in the Next Two Years | Big Data & Digital Marketing | Scoop.it

This year begins the shift of big data away from a topic unto itself, and toward standard practices

Luca Naso's insight:

My 3 takeaways from 2015 Gartner's research on Big Data:

1. Big Data solutions and technologies are becoming mainstream;

2. Big Data is mainly used for enhancing customer experience;

3. Business Leaders are as active as CIO/CTO in launching Big Data projects.

 

Organisations are overwhelmingly targeting enhanced customer experience as the primary goal of big data projects (64 percent). Process efficiency and more-targeted marketing are now tied at 47 percent. As data breaches continue to make headlines, enhanced security capabilities saw the largest increase, from 15 percent to 23 percent.

 

Last year, 37 percent of big data projects were initiated by the CIO, while 25 percent were initiated by business unit heads. In 2015, the roles are nearly tied, at 32 percent and 31 percent, respectively.

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Digitalent's curator insight, March 23, 4:19 PM

My 3 takeaways from 2015 Gartner's research on Big Data:

1. Big Data solutions and technologies are becoming mainstream;

2. Big Data is mainly used for enhancing customer experience;

3. Business Leaders are as active as CIO/CTO in launching Big Data projects.

 

Organisations are overwhelmingly targeting enhanced customer experience as the primary goal of big data projects (64 percent). Process efficiency and more-targeted marketing are now tied at 47 percent. As data breaches continue to make headlines, enhanced security capabilities saw the largest increase, from 15 percent to 23 percent.

 

Last year, 37 percent of big data projects were initiated by the CIO, while 25 percent were initiated by business unit heads. In 2015, the roles are nearly tied, at 32 percent and 31 percent, respectively.

David Cruzate's curator insight, March 28, 5:17 AM

My 3 takeaways from 2015 Gartner's research on Big Data:

1. Big Data solutions and technologies are becoming mainstream;

2. Big Data is mainly used for enhancing customer experience;

3. Business Leaders are as active as CIO/CTO in launching Big Data projects.

 

Organisations are overwhelmingly targeting enhanced customer experience as the primary goal of big data projects (64 percent). Process efficiency and more-targeted marketing are now tied at 47 percent. As data breaches continue to make headlines, enhanced security capabilities saw the largest increase, from 15 percent to 23 percent.

 

Last year, 37 percent of big data projects were initiated by the CIO, while 25 percent were initiated by business unit heads. In 2015, the roles are nearly tied, at 32 percent and 31 percent, respectively.

Zco Corporation's curator insight, April 4, 2:21 AM

My 3 takeaways from 2015 Gartner's research on Big Data:

1. Big Data solutions and technologies are becoming mainstream;

2. Big Data is mainly used for enhancing customer experience;

3. Business Leaders are as active as CIO/CTO in launching Big Data projects.

 

Organisations are overwhelmingly targeting enhanced customer experience as the primary goal of big data projects (64 percent). Process efficiency and more-targeted marketing are now tied at 47 percent. As data breaches continue to make headlines, enhanced security capabilities saw the largest increase, from 15 percent to 23 percent.

 

Last year, 37 percent of big data projects were initiated by the CIO, while 25 percent were initiated by business unit heads. In 2015, the roles are nearly tied, at 32 percent and 31 percent, respectively.

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For CFOs, A Reminder of Why Business Intelligence Is Not for Everyone

For CFOs, A Reminder of Why Business Intelligence Is Not for Everyone | Big Data & Digital Marketing | Scoop.it
Cloud computing makes it easier these days to implement a business intelligence initiative, but look before you leap.
Luca Naso's insight:
Today, technologies have advanced a great deal and made many things easier. This does not mean that implementing and doing advanced analytics is a no brainer!
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Digitalent's curator insight, March 31, 4:36 AM
Today, technologies have advanced a great deal and made many things easier. This does not mean that implementing and doing advanced analytics is a no brainer!
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5 things to know about Hadoop v. Apache Spark

5 things to know about Hadoop v. Apache Spark | Big Data & Digital Marketing | Scoop.it
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes.
Luca Naso's insight:

In my opinion, Spark should NOT be compared with Hadoop but with MapReduce. However, people usually compare Hadoop and Spark (probably because they are buzzwords).

 

5 things to keep in mind:

 

1. They do different things -

Hadoop is a distributed data infrastructure (HDFS),

Spark is a data-processing tool.

 

2. Hadoop is more complete -

Hadoop also includes a data-processing tool (MapReduce),

Spark does not have its own filesystem and needs to be integrated with some.

 

3. Spark is (much) faster -

MapReduce operates in step;

Spark operates in one shot (because it is in-memory).

 

4. Speed is not always what you need -

For batch processing you do not need Spark's high velocity;

Common applications for Spark are those requiring real-time analysis.

 

5. Failure recovery -

both Hadoop and Spark are resilient to failures.

more...
Digitalent's curator insight, March 23, 4:20 PM

In my opinion, Spark should NOT be compared with Hadoop but with MapReduce. However, people usually compare Hadoop and Spark (probably because they are buzzwords).

 

5 things to keep in mind:

 

1. They do different things -

Hadoop is a distributed data infrastructure (HDFS),

Spark is a data-processing tool.

 

2. Hadoop is more complete -

Hadoop also includes a data-processing tool (MapReduce),

Spark does not have its own filesystem and needs to be integrated with some.

 

3. Spark is (much) faster -

MapReduce operates in step;

Spark operates in one shot (because it is in-memory).

 

4. Speed is not always what you need -

For batch processing you do not need Spark's high velocity;

Common applications for Spark are those requiring real-time analysis.

 

5. Failure recovery -

both Hadoop and Spark are resilient to failures.

Filipa Alves Fonseca's curator insight, April 18, 3:23 PM
understanding big data
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The Importance Of Big Data In International Banking

The Importance Of Big Data In International Banking | Big Data & Digital Marketing | Scoop.it
Big Data is the new buzzword as far as mass operations, iterations and manipulations are concerned. The data becomes an asset that requires cost-effective innovations ...
Luca Naso's insight:

How is Big Data changing the banks business? And how this can add value for customers?


Today it is imperative to use machine learning for detecting frauds and mitigating risks. The next step should be solidifying the relationship with clients, by offering what they really need, not what the bank wants to sell.

 

The 6 uses of Big Data in banking

1. Increase efficiency - shorten the time to get information from db

2. Reduce cost - in maintaining the data infrastructure

3. Prevent fraud - self-evident

4. Accountability - better evaluation of clients' risk

5. New revenue streams - the more you know the more you can offer

6. Client satisfaction - by offering a better customer experience

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Ian Dunne's curator insight, December 1, 2015 11:41 AM

How is Big Data changing the banks business? And how this can add value for customers?


Today it is imperative to use machine learning for detecting frauds and mitigating risks. The next step should be solidifying the relationship with clients, by offering what they really need, not what the bank wants to sell.

 

The 6 uses of Big Data in banking

1. Increase efficiency - shorten the time to get information from db

2. Reduce cost - in maintaining the data infrastructure

3. Prevent fraud - self-evident

4. Accountability - better evaluation of clients' risk

5. New revenue streams - the more you know the more you can offer

6. Client satisfaction - by offering a better customer experience

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Data Integration as a key for Big Data success

Data Integration as a key for Big Data success | Big Data & Digital Marketing | Scoop.it
If you want to figure out Big Data and marketing, it starts with one core tenet and eight basic questions.
Luca Naso's insight:

A key topic when trying to leverage Big Data is data integration.

Data integration can take long time and is crucial to really benefit from big data.

 

Silo breaking, made possible by data integration, is what can let a company move from applying short-term tactics to creating a long-term strategy.

 

It goes without saying that without some good questions (i.e. business objectives) even good data integration is of little use.

One good suggestion for defining the goal is to put the customer in the center, for real.

 

8 basic question to help you get started on the right track:

1. Who is your customer?

2. What do they need?

3. What data should you be looking for to see if you are delivering?

4. Where is the data coming from?

5. How is it stored/organized?

6. Who looks at it and how often?

7. Who is analyzing it?

8. Who is presenting it?

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Auriane Boyé's curator insight, November 26, 2015 7:08 AM

A key topic when trying to leverage Big Data is data integration.

Data integration can take long time and is crucial to really benefit from big data.

 

Silo breaking, made possible by data integration, is what can let a company move from applying short-term tactics to creating a long-term strategy.

 

It goes without saying that without some good questions (i.e. business objectives) even good data integration is of little use.

One good suggestion for defining the goal is to put the customer in the center, for real.

 

8 basic question to help you get started on the right track:

1. Who is your customer?

2. What do they need?

3. What data should you be looking for to see if you are delivering?

4. Where is the data coming from?

5. How is it stored/organized?

6. Who looks at it and how often?

7. Who is analyzing it?

8. Who is presenting it?

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The Perfect Big Data Combination: Hadoop and SAP HANA

The Perfect Big Data Combination: Hadoop and SAP HANA | Big Data & Digital Marketing | Scoop.it
With Big Data solutions spawning the requirement for applications focusing on Data Analytics, HANA’s capabilities are serving as the perfect partner for Hadoop. Read this article to know more about this perfect combination
Luca Naso's insight:

SAP HANA and Hadoop are very different, that's why they could be a good combination in terms of complementing each other.

For example, SAP HANA is in-memory and uses predefined schema, while Hadoop is on disk and has no schema.

 

A scalable column-oriented database management for real-time analytics (SAP HANA) meets a technology platform that supports any kind of data for analyzing massive amount of data (Hadoop).

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Top50 BigData accounts on Twitter

Top50 BigData accounts on Twitter | Big Data & Digital Marketing | Scoop.it

Which accounts should I follow on Twitter to stay up to date with Big Data?
Here is the answer from a new algorithm.

Luca Naso's insight:

After screening the whole Twitter looking for #BigData posts, counting tweets, retweets, favourites and followers, the article reports the top50 accounts that best performed in the month of July 2015.

 

Here are the top5:

1. KPMG  (Score = 100.0%)

2. World Economic Forum (83.8%)

3. CloudEXPO ® (57.6%)

4. UN Development (45.4%)

5. Popular Science (41.4%)

 

Following them could be a wise idea for those who want to know more about the field!

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Clotilde Pasquet's curator insight, August 9, 2015 5:33 AM

After screening the whole Twitter looking for #BigData posts, counting tweets, retweets, favourites and followers, the article reports the top50 accounts that best performed in the month of July 2015.

 

Here are the top5:

1. KPMG  (Score = 100.0%)

2. World Economic Forum (83.8%)

3. CloudEXPO ® (57.6%)

4. UN Development (45.4%)

5. Popular Science (41.4%)

 

Following them could be a wise idea for those who want to know more about the field!

Laura Rathe's curator insight, August 9, 2015 11:36 AM

After screening the whole Twitter looking for #BigData posts, counting tweets, retweets, favourites and followers, the article reports the top50 accounts that best performed in the month of July 2015.

 

Here are the top5:

1. KPMG  (Score = 100.0%)

2. World Economic Forum (83.8%)

3. CloudEXPO ® (57.6%)

4. UN Development (45.4%)

5. Popular Science (41.4%)

 

Following them could be a wise idea for those who want to know more about the field!

Glenn Wallace's curator insight, August 12, 2015 8:35 AM

Stay up .......

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Mechanize Your Hiring Process to Make Better Decisions

Mechanize Your Hiring Process to Make Better Decisions | Big Data & Digital Marketing | Scoop.it
Hiring the best is hard because there's so much human variation. Amazon's Anurag Gupta shares a system that always raises the bar.
Luca Naso's insight:

"Good intentions don't work, but a repeatable, measurable and improvable process can" by Anurag Gupta, at Amazon.

 

Nobody wants to hire the wrong person and nobody wants to work for the wrong company. By following processes, it is possible to reduce the probabilities for things like these to happen.

 

1. Raise the bar, steadily and honestly;

2. Build a hiring funnel. By consistently repeating a process, you improve it. Do yourself a favour: filter candidates at each step;

3. Don't go easy on culture. This is the hardest to standardize, and yet it's one of the main reason why employee will not stay.

more...
Glenn Wallace's curator insight, July 23, 2015 10:14 AM

Stay updated & relevant 

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Actionable Ways Big Data Analytics Can Improve Innovation

Actionable Ways Big Data Analytics Can Improve Innovation | Big Data & Digital Marketing | Scoop.it

Signals assembled some of the big thinkers in big data. The discussion centered on the challenges faced by innovation teams at leading corporates, the potential (and hurdle) of “big data”, and fresh ideas on how to bridge that gap and tapping big data for innovation work.

Luca Naso's insight:

Here are 4 highlights from the conversation:

 

1. Product launchers need something to hold onto

2. Connecting the dots for the right context (aka find the right question)

3. Seeing is believing: the power of visualization

4. Caution: more data does not equal smarter data

more...
AndyDrooker's curator insight, July 1, 2015 8:46 AM

Two (2) very important topics...

promptcloud's curator insight, August 7, 2015 6:31 AM

Here are 4 highlights from the conversation:

 

1. Product launchers need something to hold onto

2. Connecting the dots for the right context (aka find the right question)

3. Seeing is believing: the power of visualization

4. Caution: more data does not equal smarter data

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Palantir Valued At $20 Billion In New Funding Round

Palantir Valued At $20 Billion In New Funding Round | Big Data & Digital Marketing | Scoop.it
The secretive data-processing company is raising up to $500 million in a previously undisclosed round of funding. The round makes it the third most valuable startup in the United States.
Luca Naso's insight:

There are a lot of secrets around Palantir, and probably you would not expect anything different from a company that has received funding from CIA (actually Q-Tel, CIA's venture capital arm).

What is known is that its data-processing software is being used to fight terror and catch financial criminals.

 

Palantir is now raising $500 million at a valuation of $20 billion, surpassed only by Uber ($50 billion) and Airbnb ($24 billion).

 

I say it's time to merge sharing economy and data science!

more...
Glenn Wallace's curator insight, June 28, 2015 11:18 AM

I need 10%

UCD CCI's curator insight, July 2, 2015 7:04 AM

big data... big business...

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SAP HANA expands its reach into Big Data analytics and the IoT

SAP HANA expands its reach into Big Data analytics and the IoT | Big Data & Digital Marketing | Scoop.it

SAP SE has rolled out an updated version of its flagship HANA in-memory database and application platform this week, with the emphasis on Big Data analytics and the Internet of Things (IoT).

Luca Naso's insight:

According to Steve Lucas, SAP’s president of platform solutions, service pack 10 is one of the biggest-ever updates to the platform since it was launched in 2010.

 

Main changes:
1. A new remote data-synchronization feature that allows organizations to synchronize data between remote locations and the enterprise;
2. A new Web-based development workbench to help enterprises cleanse data and manage duplicates (this could dramatically reduce the size of data centres);
3. Expanded data-integration capabilities that include support for the latest Cloudera Inc., Hortonworks Inc. and MapR Technologies Inc. Hadoop distributions.

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Gartner Says Advanced Analytics Is a Top Business Priority

Gartner Says Advanced Analytics Is a Top Business Priority | Big Data & Digital Marketing | Scoop.it

Gartner, Inc. said that advanced analytics is the fastest-growing segment of the business intelligence (BI) and analytics software market and surpassed $1 billion in 2013.

Luca Naso's insight:

A picture is worth a thousand words. The reason I am sharing this article is exactly for the figure it contains.

 

Data can be analysed with 4 different targets:

1. Descriptive - knowing what happened

2. Diagnostic - knowing why it happened

3. Predictive - knowing whether/when it will happen again

4. Prescriptive - knowing what to do

 

Usually, organizations approach data science aiming at goals from 1 (at the beginning) to 4 (after developing experience).

 

There is, however, an implicit flaw the figure. It seems to suggest that human intervention goes from large to almost absent. In fact, it is just shifting from "after the analysis" to "prior the analysis".

 

The Human intervention now lies in the data science team that develop and deliver the advanced analytics project.

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Jim Thompson's curator insight, June 16, 2015 11:48 AM

Analytics and decisioning engines will help markets surf a wave of exploding data!

Christophe Acher's curator insight, June 18, 2015 4:00 AM

TIBCO SPOTFIRE est depuis longtemps précurseur sur le segment de l'advanced analytics à la portée de tous, notamment avec l'intégration native de R et de fonctions statistiques avancées (segmentation, prévision, corrélations, ...).

 

A l'heure où de nombreuses entreprises se posent la question du remplacement de SAS, essentiellement pour des questions de coûts et de complexité, et SPOTFIRE s'impose comme solution de substitution partielle ou complète dans bien des cas.

 

N'hésitez pas à me contacter pour en savoir plus. christophe.acher@tibco.com

Robert McKenzie's curator insight, June 21, 2015 3:50 AM

Sounds simple ..just add data and insight

 

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Gartner Predicts 3 Big Data Trends for Business Intelligence

Gartner Predicts 3 Big Data Trends for Business Intelligence | Big Data & Digital Marketing | Scoop.it

 

Three trends Gartner has identified describe information’s ability to transform business processes over the next few years.

Luca Naso's insight:

After giving us the definition of Big Data, Gartner is now giving us 3 predictions on what will happen with Big Data in Business Intelligence:

 

1. By 2020,

information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.


2. By 2017,

more than 30% of enterprise access to broadly based big data will be via intermediary data broker services, serving context to business decisions.


3. By 2017,

more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT.

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Tutte le acquisizioni in intelligenza artificiale da Google ad Apple in una timeline

Tutte le acquisizioni in intelligenza artificiale da Google ad Apple in una timeline | Big Data & Digital Marketing | Scoop.it
Da Google ad Apple, ecco come i giganti fanno shopping tra le startup specializzate in tecnologie di intelligenza artificiale avanzata
Luca Naso's insight:
Artificial Intelligence is a very attracting field for many big corporates. Here is a graphic timeline of the acquisitions made since 2013.

Google made 5, but also Amazon, Salesforce, Facebook, IBM, Intel, Yahoo, Twitter and Apple are quite active.
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amitmahendruphotography's curator insight, April 20, 9:51 PM

Candid Photographer In Lucknow , Candid Photographer In Kanpur http://amitmahendruphotography.com

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How to use virtualenvwrapper

How to use virtualenvwrapper | Big Data & Digital Marketing | Scoop.it
A guide to virtualenvwrapper (video tutorial included): how to pip install, configure and use virtual environments in python.
Luca Naso's insight:

Are you a Data Scientist coding in Python? Then you might find this tutorial helpful.

 

Virtualenvwrapper is a tool for managing virtual environments under Python.

 

This guide and video tutorial well explain the basic usage of the package and also give suggestions on how to go beyond that.

more...
Norman Warthmann's curator insight, March 10, 1:40 AM

Are you a Data Scientist coding in Python? Then you might find this tutorial helpful.

 

Virtualenvwrapper is a tool for managing virtual environments under Python.

 

This guide and video tutorial well explain the basic usage of the package and also give suggestions on how to go beyond that.

BAILLY Romain's curator insight, March 10, 5:33 AM

Are you a Data Scientist coding in Python? Then you might find this tutorial helpful.

 

Virtualenvwrapper is a tool for managing virtual environments under Python.

 

This guide and video tutorial well explain the basic usage of the package and also give suggestions on how to go beyond that.

organsrelieved's comment, May 17, 4:35 AM
Great
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How Applications of Big Data Drive Industries

How Applications of Big Data Drive Industries | Big Data & Digital Marketing | Scoop.it
How industries like banking, healthcare, education, manufacturing, Insurance, retail, etc. are using big data.
Luca Naso's insight:

There is a substantial spending on big data, with more than 75% of companies (from different industries) investing in big data in the next two years.

 

Each industry vertical has its own challenges and solutions. Here is a great article and infographics by simplilearn that describe for 10 verticals both main challenges and applications.

The 10 industries are:

1. Banking

2. Communication

3. Healthcare

4. Education

5. Manufacturing

6. Government

7. Insurance

8. Retail

9. Transportation

10. Energy

more...
PhysicsTutorRanchi's curator insight, December 19, 2015 8:27 AM

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Fabio Di Pasquale's curator insight, April 5, 2:30 AM

There is a substantial spending on big data, with more than 75% of companies (from different industries) investing in big data in the next two years.

 

Each industry vertical has its own challenges and solutions. Here is a great article and infographics by simplilearn that describe for 10 verticals both main challenges and applications.

The 10 industries are:

1. Banking

2. Communication

3. Healthcare

4. Education

5. Manufacturing

6. Government

7. Insurance

8. Retail

9. Transportation

10. Energy

Emeric Nectoux's curator insight, April 22, 3:29 AM

A Gartner Survey for 2015 shows that more than 75% of companies are investing or are planning to invest in big data in the next two years. These findings represent a significant increase compare to 2012.

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15 Stunning Big Data Visualizations (And What You Can Learn From Them)

15 Stunning Big Data Visualizations (And What You Can Learn From Them) | Big Data & Digital Marketing | Scoop.it
15 examples of data visualizations that will give you a clearer understanding of what makes a good visualization--and what makes a bad one.
Luca Naso's insight:

A list of awesome visualisations!

Not only are they clear and beautiful, but also they keep you focused on the subject and succeed in conveying the story behind the data.

 

If you need to stimulate your creativity for your next dashboard or infographics have a close look at them!

 

Here is my list of top 5:

1. Are we alone?

Stimulate user’s imagination

http://www.bbc.com/future/story/20120821-how-many-alien-worlds-exist


2. Here is today

Give perspective

http://hereistoday.com/

 
3. You fix the budget
Empower the users

http://www.nytimes.com/interactive/2010/11/13/weekinreview/deficits-graphic.html

4. The Internet map

Exploit metaphors

http://internet-map.net/


5. Infographics with Real images

http://marion-luttenberger.squarespace.com/#/infographics/

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From Big Data to Big Algorithm

From Big Data to Big Algorithm | Big Data & Digital Marketing | Scoop.it

Businesses now collect so much data that the resulting insights can not only describe customer behaviour in the present, but predict what it will look like in the future as well.

Luca Naso's insight:

Predictive analytics, or the ability to make accurate predictions, is based on good data and good algorithms. Every sensible data scientist knows this. It's good to see that this knowledge is spreading. Data alone, no matter how big, cannot give the right answers.

 

I believe that useful predictive analytics gives *accurate* predictions, not *exact* predictions. Predictive analytics which gives exact predictions is used in Science, not in Business.

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LittleBIGJob's curator insight, November 13, 2015 10:43 AM

And to Predicitve analysis!

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IBM, Cloudera and Amazon Big Data announcements

IBM, Cloudera and Amazon Big Data announcements | Big Data & Digital Marketing | Scoop.it
New products, new technologies and an acquisition announced over the last two weeks may be small items on their own, but they add up to an important shift in the Big Data space.
Luca Naso's insight:

1. IBM is improving its cloud-based elastic data warehouse;

2. Cloudera announces Ibis: a project for letting data-scientists use Python directly on Hadoop;

3. Amazon is making Elastic MapReduce almost plug-and-play.


What's especially interesting about all of these announcements is that they are signs of a maturing market. No one's introducing a brand new data processing engine. Instead, IBM is improving, acquiring and competing; Amazon is streamlining; Cloudera is re-platforming; and Apache NiFi is integrating.

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JẴї❣'s curator insight, August 29, 2015 6:47 AM
Big shift in the Big Data space
Anthony Ricketts's curator insight, August 29, 2015 9:15 AM

I am taking a few courses in Data Analytics. My friend @laurakenyon told me it is a huge deal for marketers now. Indeed ROI and understanding metrics are fundamentals of the marketing profession.

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All about Data Lakes

All about Data Lakes | Big Data & Digital Marketing | Scoop.it

Here is an infographic developed by Aureus Analytics which shows how a Data Lake really works

Luca Naso's insight:

Data Lakes as the Big Data alternative to Data Warehouses.

 

Their main features are:

1. Contain ALL of the data, both structured and unstructured, internal and external;

2. Store all data in RAW format;

3. No SCHEMA is imposed on the data.

 

I like Data Lakes because they:

1. Are flexible

2. Break silos

This increases the success rate of Big Data projects.

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Chris Balbrick Infographiste's curator insight, August 2, 2015 7:40 AM

Data Lakes as the Big Data alternative to Data Warehouses.

 

Their main features are:

1. Contain ALL of the data, both structured and unstructured, internal and external;

2. Store all data in RAW format;

3. No SCHEMA is imposed on the data.

 

I like Data Lakes because they:

1. Are flexible

2. Break silos

This increases the success rate of Big Data projects.

iSparkCEO's curator insight, August 4, 2015 2:28 PM

Data Lakes as the Big Data alternative to Data Warehouses.

 

Their main features are:

1. Contain ALL of the data, both structured and unstructured, internal and external;

2. Store all data in RAW format;

3. No SCHEMA is imposed on the data.

 

I like Data Lakes because they:

1. Are flexible

2. Break silos

This increases the success rate of Big Data projects.

MH's curator insight, August 24, 2015 6:41 AM

Les "lacs de données", une nouvelle façon de stocker les données

 

Infographie listant les informations de base sur les lacs de données et leurs différences avec le stockage classique. Les lacs de données regroupent les données en vrac et sans traitement. L'accès y est plus ouvert que pour les bases de données classiques. De ce point de vue, ils s'apparentent à l'open data.

 

Cette nouveauté semble intimement liée au développement de méthodes de recherche capables de relier entre elles des données hétérogènes.

 

(infographie en anglais)

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Internet of Things Dissected By SmartThings CTO Jeff Hagins

Internet of Things Dissected By SmartThings CTO Jeff Hagins | Big Data & Digital Marketing | Scoop.it

The Internet of Things is the 3rd wave of internet that is transforming the ways in which we live and work. Therefore, it has to involve the physical world.

Luca Naso's insight:

"Automation" is an important part of the equation. Internet of Things is not about moving the switches from the wall into the device (smartphone or tablet), but to remove them all!

 

We are at a stage where software has a concrete opportunity to reinvent the hardware.

 

Security is another important part of the equation. And it only happens if you include it in your design. This requires time, people, resource and money.

 

Even more, security and easy of use are on different part of the balance. There is a trade-off between "plug and play" and security, and it is not yet clear how to achieve the best balance.

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Glenn Wallace's curator insight, July 4, 2015 8:38 AM

What are they speaking about

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7 More Big Data Companies to Watch

7 More Big Data Companies to Watch | Big Data & Digital Marketing | Scoop.it

Like any market in its infancy, the big data industry is bustling with companies jockeying for a comfortable position in a niche that is as surefire as any.

Luca Naso's insight:

Here is a list of 7 "new" companies that might make the difference in the realm of Big Data.

 

1. Palantir Technologies

Founded in 2004 and evaluated at $20B it is probably not a startup anymore

2. Crayon Data 

Big data for decision making

3. Neo Technology

Best known for its graph db Neo4j

4. Couchbase

The company behind the NoSQL db Couchbase server.

5. PromptCloud

Specialised in Data-as-a-Service

6. Snowflake

Big Data technologies for SMBs.

7. Saama Technologies

Is this a startup? Founded 18 years ago, it has clients such as Apple and Cisco

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Glenn Wallace's curator insight, June 30, 2015 11:18 AM

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iSparkCEO's curator insight, August 4, 2015 2:30 PM

Here is a list of 7 "new" companies that might make the difference in the realm of Big Data.

 

1. Palantir Technologies

Founded in 2004 and evaluated at $20B it is probably not a startup anymore

2. Crayon Data 

Big data for decision making

3. Neo Technology

Best known for its graph db Neo4j

4. Couchbase

The company behind the NoSQL db Couchbase server.

5. PromptCloud

Specialised in Data-as-a-Service

6. Snowflake

Big Data technologies for SMBs.

7. Saama Technologies

Is this a startup? Founded 18 years ago, it has clients such as Apple and Cisco

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IBM Serves Wimbledon Tennis With Data Analytics

IBM Serves Wimbledon Tennis With Data Analytics | Big Data & Digital Marketing | Scoop.it

It’s Wimbledon season and that means tennis, strawberries and Pimm’s. It also means big data analytics. Previous Forbes writers have chided tennis for being behind other sports in data analytics, but this suggestion may need some clarification.

Luca Naso's insight:

At the Wimbledon tennis tournament, IBM will use a solution based on InfoSphere + Watson. This will make it possible not only to collect information just about everything that happens to the players and to the balls, but also to relate this large set of data with historical information and to query it using Natural Language.

 

There's more.

With so much data about each shot and player, it will be possible to analyse players' playing style. This will in turn allow the players to study their opponents' weakness and strengths to better prepare next matches.

 

A very interesting note.

IBM is training tennis professionals to use the data entry system, rather than training data scientists on tennis.

Yet another proof of the importance of having field experts (about the specific industry under consideration).

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Glenn Wallace's curator insight, June 24, 2015 1:16 PM

We must stay abreast with current events

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Big Data Integration: Five Biggest Pitfalls to Avoid

Big Data Integration: Five Biggest Pitfalls to Avoid | Big Data & Digital Marketing | Scoop.it

The big data revolution has captivated a global audience. What you rarely hear is that there are some pitfalls that can sink your project to the abyss of no return.

Luca Naso's insight:

Five mistakes to avoid to fail a big data project:

1. Going at Big Data alone

2. Using outdated data management practices

3. Ignoring Big Data best practices

4. Failing to understand the importance of Big Data governance

5. Understanding the power of Big Data, aka concentrating at the finger and missing the moon.

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Jabbar Ziadi's curator insight, August 1, 2015 9:08 AM

Five mistakes to avoid to fail a big data project:

1. Going at Big Data alone

2. Using outdated data management practices

3. Ignoring Big Data best practices

4. Failing to understand the importance of Big Data governance

5. Understanding the power of Big Data, aka concentrating at the finger and missing the moon.

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7 steps for executing a successful data science strategy

7 steps for executing a successful data science strategy | Big Data & Digital Marketing | Scoop.it

Data Science often points to the need for change - and change can be difficult. Get tips from TDWI for making your foray into data science a success.

Luca Naso's insight:

 

Most organizations have realized both the potentials and the difficulties of Big Data.

 

Here is a TDWI checklist report that can help to get organized before beginning a new project:

1. Identify key business drivers

2. Create an effective team

3. Emphasize communication skills

4. Embrace visualization and storytelling

5. Access all the data

6. Operationalize Analytics

7. Improve governance

 

The 3 elements that I particularly consider crucial are:

A. Set clear goals

B. Invest on the team, not on individuals

C. Communicate and operationalize your project findings 

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